govindhtech

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Japan KDDI And Partners Launch AI-Quantum Platform

With AI-Quantum Platform, KDDI Leads Japan's Quantum Revolution KDDI and top domestic partners have launched a multi-institution research endeavour to create a “AI-quantum common platform,” which might alter Japan's technology landscape. This ambitious strategy, launched on February 27, 2025, intends to democratise quantum computing by letting more people and sectors access its remarkable powers without quantum understanding. According to the Cabinet Office's "Vision for a Quantum Future Society," Japan aims to cultivate 10 million quantum technology users and generate ¥50 trillion in domestic production value by 2030. This initiative targets this strategic goal. Japan is determined to stay in the quantum race despite rising competition.

The project received significant funding from Japan's Post-5G Infrastructure Strengthening Program (G5-3), overseen by NEDO. Its main goal is to reduce the major operational and technological impediments to enterprise quantum computing adoption. By fiscal year 2027, KDDI wants to address the quantum sector's significant lack of specialised knowledge and operational technologies, which have hindered industrial engagement. AI-Quantum Fusion for Unprecedented Accessibility

The easy integration of AI and quantum computing resources underpins this novel platform. Users can run the system without quantum mechanics or information knowledge due to this unusual combination. Large language models (LLMs) and other generative AI are becoming more popular, revealing the processing speed and computational capacity constraints of classical computers. This shows the need for a platform. Quantum computers can perform multiple calculations at once, which could speed up artificial intelligence development and solve complex problems like combinatorial optimization that classical computers cannot. Its capacity to support superconducting, neutral atom, and optical quantum technology will be a major benefit. By dynamically selecting the optimum cloud-based resources for each application, this flexibility lowers the adoption barrier across sectors. Even while KDDI has researched quantum use cases in the telecom industry, such as automating contact centre shift plan development and optimising base station settings, this platform aims to expand quantum's reach beyond its usual limits. This project requires a lot of teamwork due to its size. Ten domestic partners are involved. These include: The KDDI Research Institute Company SEC. Jij QunaSys The National Institute of Advanced Industrial Science and Technology Waseda U Keio University University of Osaka Shibaura Tech Institute AIST's Global Research Centre for Quantum-AI Fusion Technology Business Development will coordinate research using ABCI-Q, a sophisticated hybrid computing testbed. Project technical development follows two primary lines: Middleware Technology: This track will integrate generative AI strongly educated on quantum knowledge with abstracted quantum functions, a crucial challenge. Easy operation with an integrated development environment (IDE) is the goal. Advanced load-balancing systems that automatically allocate the best AI and quantum computing resources to applications will also be developed in this track. This path also requires the formation of application service providers to distribute quantum-enabled applications to a bigger market.

The fundamental instability and fragility of qubit, the building blocks of quantum computers, limits continuous operational capabilities, making this a crucial topic. This route involves developing advanced methods for directly collecting and analysing quantum processor and support hardware telemetry data, such as cryogenic freezers and other control devices. The ultimate goal is to provide reliable fault detection and comprehensive management systems that can reduce qubit instability-related operational issues. Adding Use Cases and Speeding Commercialization According to KDDI, quantum computing development and commercialization have increased worldwide since Google's 2019 claim of "quantum supremacy." This milestone fuelled the fight for quantum dominance, prompting global IT companies to provide cloud services and quantum computers. Japan is determined to stay up by pushing industry use cases and improving its domestic technology capacity. Partners will develop use cases for chemical calculations and optimisation, where early commercialisation is expected. Optimisation includes KDDI's main telecom activities such base station energy management and telecommunications quality. Non-telecommunications applications include manufacturing production planning and logistics routing optimisation. Chemical calculations are used to create next-generation renewable energy materials. The AI and quantum common business platform will separate consumers from application developers and offer techniques similar to computer APIs and application portals to make quantum computing easier for non-specialists. As AI and quantum computing platforms improve, they will adapt to technical changes, allowing applications to choose the optimum computing resources year after year. KDDI believes their AI-quantum common platform will increase the number of useful quantum applications, improve quantum system operational maturity, and accelerate commercialisation, aligning with national policy goals. The alliance is also considering expanding such programs outside Japan and strengthening ties with Europe through the Strategic Innovation Promotion Program (SIP3 quantum). The AI-quantum common platform could give Japanese business quantum computing capacity if successful. The nation should benefit from this ubiquitous accessibility in a market for advanced materials research, complex logistics, and supply chain optimisation. Japan may lead the quantum revolution, boosting economic growth and creativity for years.

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Quantum Query Complexity: A Key to Quantum Speedups

Quantum Query Complexity

Query complexity in quantum computing reveals computing's basic boundaries

Quantum computing, which offers unheard-of computational power for tasks beyond ordinary computers, is generating headlines as the leading edge of technology. Understanding these changes and assessing quantum algorithms' potential depends on query complexity, a key metric that currently assesses algorithmic processing power. This topic is vital for showing how quantum algorithms may speed up database searches and difficult theoretical problems.

Traditional sequential binary bit processing gets extremely difficult for large tasks. Qubit superposition and entanglement offer simultaneous computations and exponential speedups for specialised applications in quantum computing. Quantum physics supports this power, but query complexity determines an algorithm's efficiency.

The minimum number of times an algorithm must use its input data to solve a problem is query complexity. It is significant because it abstracts an algorithm's core restrictions from implementation and hardware concerns. By setting strong lower constraints on query counts, researchers can show that tasks are difficult even with quantum algorithms. Quantum speedup upper bounds will be defined.

Researchers define these important boundaries using four methods:

The Hybrid Method: Establishing lower bounds using classical and quantum computational approaches shows how tough a task is even with quantum aid. Bounded by the distance change after each query, it carefully examines a quantum algorithm's intermediate states for different inputs. This method reveals that the most insightful queries focus on input differences.

The Polynomial Method: This method uses polynomial approximations of functions and links problem complexity to polynomial degree. Recent advancements have improved this by tying a function's approximate degree to its block sensitivity. Dual polynomials and symmetrisation techniques aid complex function evaluation and reduce quantum query complexity.

To limit the amount of queries, this method painstakingly stores the information acquired from each query, building an image of the data.

The Adversary Method: This adaptable strategy creates a fake enemy to limit query information. This lets researchers rigorously determine the minimal number of queries any software must ask to set robust lower bounds. This approach can also design algorithms that reach certain bounds, demonstrating its dual utility.

Read Ground-Truth Quantum Security on Oracle Cloud Infrastructure.

Classic methods like Deutsch-Jozsa, Grover's search, and Simon's algorithm are used to evaluate quantum algorithms. Shor's approach provides superpolynomial speedups for discrete logarithm and integer factorisation issues using quantum techniques. Quantum polynomial speedups occur in the Eulerian Tour problem and k-distinctness issue.

The field of quantum speedups is complicated. In many cases, full functions (where algorithms must work correctly for all inputs) only improve by a cubic factor, but partial functions may improve exponentially. Research is ongoing on this distinction.

Despite advances, quantum complexity theory remains difficult. Current methods are limited and complex, such as applying symmetry to collision and AND-OR trees. More study is needed. Quantum Complexity Theory studies the semantics of quantum computational complexity classes (such BQP) and how they relate to classical classes (P, NP).

Industry Progress in Quantum Computing:

Quick breakthroughs in quantum hardware and applications complement theoretical advances in query complexity. Recent reports described a lively and developing quantum ecosystem:

Rigetti Computing's 36-qubit multi-chip quantum computer advances quantum computing.

Oxford Ionics installed a quantum computer at the UK's National Quantum Computing Centre, expanding national quantum technology investment and infrastructure.

Caltech is exploring sound-based memory enhancement for quantum storage, providing new ways to store quantum data.

Through its Quantum Credits Program, IBM helps companies and scientists access cutting-edge quantum hardware.

Famous figures in the field, including the Diraq Co-Founder, discussed quantum commercialisation and the viability of silicon quantum qubit enterprises.

In conclusion

Query complexity is crucial to understanding and using quantum computing. By meticulously evaluating restrictions and potential speedups, researchers are developing quantum algorithms and hardware and advancing theoretical understanding. As the industry advances, query complexity's precise analytical framework will unlock ultra-high computer power and change several sectors in the quantum era.

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Nuclear Magnetic Resonance Validate Key Protocol To Quantum

Nuclear Magnetic Resonance

NMR Processors Verify a Key Protocol to Overcome Quantum Noise in the Quantum Leap

New research has empirically confirmed the Petz recovery map, a crucial theoretical approach for recovering quantum information lost to ambient noise, making significant progress in the search for resilient quantum technologies. This map was applied to a nuclear magnetic resonance (NMR) quantum processor by Gayatri Singh, Ram Sagar Sahani, and colleagues from the Indian Institute of Science Education and Research Mohali and Universität Ulm, confirming that recovered quantum states match theoretical predictions. This innovation proves the map's viability on current quantum platforms and its usefulness for mistake reduction in forthcoming quantum devices. Quantum Noise continues to challenge

The intrinsic sensitivity of quantum systems to external interactions, known as quantum decoherence, hinders quantum technology development. Quantum computing, communication, and sensing depend on superposition and entanglement, which classical systems often lose. Quantum channels, used to visualise disruptive interactions, theoretically represent the evolution of a quantum system under noise. The researchers focused on two common types of single-qubit noise: amplitude damping (AD), which models energy dissipation and drives the system towards a ground state, affecting populations and coherences, and phase damping (PD), which erodes quantum coherence by suppressing off-diagonal density matrix elements without changing populations. Practical quantum platforms including NMR systems, trapped ions, and superconducting qubits exhibit these noise processes. A Flexible Quantum Experiment Platform: NMR

This study found that NMR technology was ideal for testing the Petz recovery map's theoretical foundations in practice. The group employed 13C-labeled diethyl fluoromalonate nuclei 1H, 19F, and 13C on a three-qubit NMR quantum processor. All measurements were performed on a Bruker Avance-III 600 MHz NMR spectrometer with a 5 mm QXI probe at 300 K. The experimental setup used the 19F nucleus as the system qubit and the 1H and 13C as auxiliary qubits. These supplementary qubits were needed to generate the damping channel and Petz recovery map. The weak coupling approximation of this three-qubit system's internal Hamiltonian includes empirically calculated scalar J-couplings and chemical shifts. The pseudopure state (PPS) NMR method simulates a pure quantum state by starting with thermal equilibrium and applying rotations, free evolution, and pulsed field gradients. Experimentally reconstructed PPS exhibited high average fidelity. Duality Quantum Computing (DQC) Quantum Channel Simulation Researchers created damping channels and the Petz recovery map using the duality quantum computing (DQC) technique. DQC is a sophisticated framework that simulates unitary operators on qubits using ancillary systems. The single-qubit channels (AD and PD) studied in this study only needed one additional qubit to create channel dynamics. The DQC algorithm has several key steps: Set the auxiliary qubits to |0⟩ and the system qubit to the required input state during initialisation. Additional qubit and unitary operator (V). Controlled unitary operations (Uj) on the system qubit using the auxiliary qubit. Additional unitary operation (W) with the auxiliary qubit.

An extra controlled unitary operation will map the simulated operators to the quantum channel's Kraus operators. Finally, the auxiliary system is measured to determine the Kraus operator's effect on the qubit. To determine the Kraus operator's effect on the system qubit, the ancillary system is measured last. In these investigations, AD and PD were explicitly modelled as perfect quantum channels rather than relying on the NMR environment's natural decoherence (T1 and T2 relaxation times). The recovery maps require precise channel knowledge, hence this extensive modelling was needed. Key Role of Reference State A study indicated that the Petz map's efficacy depended on its reference state. How well the reference state matches the input state determines data fidelity during recovery. The map performed well for amplitude damping with the right reference state. Using a reference state with smaller epsilon and sigma led to better recovery when the input state was near |0⟩, which the AD channel naturally drives towards. However, higher epsilon values were better for input states, improving fidelity.

Phase damping results were more complicated. Large input-reference state overlap improved recovery fidelity. However, faithfulness decreased when the reference state diverged from the input state (e.g., |−⟩ for |+⟩). It is notable that a maximally mixed reference state with sigma did not recover and often worsened fidelity. As a PD channel, the Petz map further reduced coherence. No recovery was observed for diagonal input states like |0⟩ due to inappropriate reference states, as the PD channel primarily impacts off-diagonal elements. Enabling Practical Quantum Technologies This experimental use of the Petz recovery map on an NMR quantum processor advances quantum error avoidance. Key theoretical assumptions concerning the map's reference state dependence are validated, and a framework for applying this recovery strategy in quantum protocols is provided. These findings emphasise the importance of noise qualities and Petz map adjustments. Even though the tests only analysed one qubit, the researchers accept this as an important first step. This project will expand to multi-qubit systems, analyse more sophisticated noise models, and integrate the Petz map with well-known fault-tolerant techniques to improve quantum computations, communications, and technologies.

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Jiuzhang 4.0 Achieves Quantum Advantage in Microseconds

Jiuzhang 4.0

Chinese Researchers Set Quantum Computing Standards with Jiuzhang 4.0

With their new programmable quantum processor, Jiuzhang 4.0, Tsinghua University and Jiuzhang Quantum Technology have revolutionised quantum computation and demonstrated a quantum advantage. Performing computing work in microseconds, which would have taken classical supercomputers 10^42 years, is a huge step towards developing fault-tolerant quantum gear. Gaussian boson sampling (GBS) uses linear optics to outperform typical computers computationally. A quantum processor processes photons to produce detection events in GBS. Jiuzhang 4.0 has produced almost three thousand of these events, proving its quantum edge. Rare Speed and Scale Jiuzhang 4.0 can handle 1024 compressed states and 8176 output modes, making it more difficult than previous trials. Squeezed light from numerous optical parametric oscillators is carefully filtered to form this processor's core. After then, a complex system of interferometers and delay loops distributes each input photon over a tremendous number of temporal and spatial modes to cube scale the connection. This dense coupling and a novel spatial-temporal hybrid encoding circuit maximise computational potential. Processor performance is excellent. Jiuzhang 4.0 calculates in 25.6 microseconds, while EI Capitan, a cutting-edge supercomputer, takes over 10^42 years. This delivers a speedup of almost 10^54 compared to the most powerful traditional supercomputers. With Jiuzhang 4.0 producing up to 3050 detected photons, the team showed that photon loss, which could make classical simulations easier, does not destroy the quantum advantage. Validating the results with a 1432-core GPU cluster for simulation and verification verified the quantum advantage claim. Traditional Advancement: Jiuzhang Series This latest achievement builds on the University of Science and Technology of China (USTC)'s Jiuzhang quantum computer achievements. First iteration, Jiuzhang (2020), completed GBS in 200 seconds, showing quantum computational advantage. The Sunway TaihuLight supercomputer reportedly needs 2.5 billion years to finish this. With a sampling rate of 10^14 times faster than standard simulations, it generated up to 76 photon clicks. Jiuzhang 2.0 (2021): Improved version with 144-mode photonic circuit yields 113 photon detection events and a Hilbert space size of 10^43. It introduced a scalable quantum light source based on stimulated emission of compressed photons with near-unity purity and efficiency. Jiuzhang 3.0 (2023): A 144-mode ultralow-loss optical interferometer recorded 255 photon-click events. The quantum computer completed a task in 1.27 microseconds, while the Frontier supercomputer would require 600 years or 3.1 x 10^10 years for the most challenging sample using exact methods. Future effects and prospects Jiuzhang 4.0's quantum advantage in GBS is a major step towards adopting quantum computers for real problems. Applications include materials science, machine learning, and drug development. Complex optimisation could affect finance and energy management, but the ability to accurately replicate complex quantum phenomena could advance quantum chemistry and materials research. Researchers want to control larger clusters of entangled quantum states and improve squeezed light source efficiency while accepting present restrictions to scale up these systems and construct fault-tolerant quantum technology.

Photonic Quantum Computing is Growing A Dynamic Photonic Quantum Computing Environment The fast-growing subject of photonic quantum computing, in which several large businesses worldwide are making headway, includes Jiuzhang 4.0: Xanadu introduced Borealis, a programmable photonic quantum computer technology, in 2022. Quantum computing allowed it to execute a task in 36 microseconds that would take classical supercomputers 9,000 years utilising 216 compressed modes. PennyLane, an open-source Python framework for differentiable quantum programming, is also available from Xanadu. Quandela provides hardware, middleware, and software quantum solutions. MosaiQ technology allows controlled manipulation of pure photons on demand. Prometheus was the first compact, self-sufficient single-photon generator to produce a photonic qubit. Ascella, a general-purpose quantum computing prototype, uses single photons to give 1, 2, and 3-qubit gates strong fidelities for variational quantum eigensolvers and quantum neural networks. They also offer Perceval, an open-source photonic quantum computing system model. ORCA Computing: Develops modular, fiber-connected photonic quantum computers for long-term error-corrected systems and quantum accelerators. GHZ-state measurements are being used to study fault-tolerant designs that reduce photon loss and probabilistic processes. Using silicon spin-photon interfaces and T centres for highly-connected topologies and low-overhead quantum error correction, photonics develops a scalable, fault-tolerant, and integrated quantum computing and networking platform. Using photons, PsiQuantum implants fusion measurements as gates and conduits in silicon circuits. Their Fusion-Based Quantum Computation (FBQC) model claims robustness against 10.4% photon loss per fusion, aiming for far higher fault tolerance thresholds. QuiX Quantum: A leading integrated photonic processor company, QuiX Quantum produces flexible multimode adjustable interferometers. They constructed 12-mode and 20-mode quantum photonic processors that can perform arbitrary linear operations with high fidelity using low-loss silicon nitride waveguides. The first Chinese optical quantum computer chip company is TuringQ, founded in 2021. Zhiyuan membosonsampling machine achieves 56-fold multi-photon registrations in 750,000 modes and scales the boson sampling issue to dimensions unreachable by classical supercomputers, giving it quantum advantage. They offer FeynmanPAQS, a commercial photonic quantum computing simulation program.

Global TundraSystems Global Photonic quantum computing company with a 64-qubit quantum processor and deep learning-based quantum error correction. It created the quantum photonics microprocessor Tundra Processor. Growing application of photonics technologies in various industries will drive the global photonics market to USD 837.8 billion by 2025. This growth demonstrates quantum photonic technology's promise and investment. Photonic quantum computers developed by top institutes worldwide will revolutionise information processing and computing.

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New Protocols Enable Multi Qubit Gates In Spin Processors

Multi-qubit Gates

New Quantum Leap Protocols Promise Faster, High-Fidelity Multi-Qubit Gates in Spin Processors.

A revolutionary quantum computing technology promises to speed up and improve the reliability of complex quantum processes, scientists say. “Single-step high-fidelity three-qubit gates by anisotropic chiral interactions” and “Fast Multi qubit Gates in Spin-Based Quantum Computing” highlight the research, which introduces new multi-qubit gate methods, a key bottleneck in quantum computer scalability. These unique methods use modest three-qubit interactions to make quantum calculations for near-term devices more feasible.

Immediately Need Better Multi-Qubit Gates

Scaling quantum computers is difficult since most quantum algorithms use multi-qubit gates. Unlike single- and two-qubit gates, which form a universal gate set, multi-qubit operations like the Toffoli (controlled-controlled-not) gate are inefficient to break down into these smaller primitives.

A single Toffoli gate in a conventional gate set requires six two-qubit and nine single-qubit operations, increasing circuit depth and decoherence risk. Existing single-step resonant Dephasing and phase errors from off-resonant transitions cause poor fidelity (≤ 90%) in Toffoli-like gates on silicon and germanium spin-qubit platforms. Real-world applications need fast, high-fidelity multi-qubit gates to reduce mistakes and circuit depth.

Overcoming Synchronisation Issues

A fundamental obstacle for high-fidelity multi-qubit gates is the “synchronization issue”. Traditional single-step methods using two-qubit interactions cannot accurately synchronise all resonant and off-resonant transitions. Gate speed and fidelity must be traded off due to this constraint; faster gates have lower fidelity. A three-qubit C²Ry gate that achieves 98% fidelity is the fastest solution for fixed interactions, but it takes 16 times longer to accomplish 99.99% fidelity. Extended gate lengths are impracticable due to the systematic mistakes induced by neglected flip-flop terms, which reduce fidelity.

Breakthrough: Anisotropic Chiral Interactions

An innovative method for synchronisation via chiral and small anisotropic three-qubit interactions is presented in the new work. The combination between orbital magnetic fields and spin-orbit interactions (SOI) in modern spin-based quantum technologies naturally creates these peculiar interactions.

Mechanism: When spin qubits are organised in a triangular loop, third-order virtual tunnelling events, in which a particle passes through the loop, cause an effective three-qubit interaction. The destructive interference of closed trajectories causes this interaction, which is preceded by a prefactor confirming that spin-up and spin-down particles phase-interfere.

Resolution: A little interaction much less than two-qubit interactions can overcome the synchronisation problem to enable completely synchronised three-qubit gates in one step. This maintains integrity and speeds gate functioning.

Numerical simulations show that our single-step three-qubit gate outperforms current approaches, potentially achieving ≤ 10⁻⁴ infidelity in 80-100 nanoseconds. This matches typical two-qubit gate times.

Tunability and Feasibility: Local quantum dots (QDs) energies, tilting g-tensors, or the SOI can considerably modify the interaction. These interactions are physically and experimentally possible in cutting-edge silicon and germanium spin-qubit devices. Current setups can support orbital magnetic fields (20–60 mT).

A Strong Alternative: Four-Step Echo Protocol

The researchers proposed a four-step echo approach for three-qubit gates in addition to the single-step protocol. This protocol will benefit two-qubit architectures.

Mechanism: Echo uses two single-qubit gates, unlike the fully synchronised approach. It suppresses unwanted precessions in off-resonant subspaces by flipping the Z-component of the precession axis approximately halfway of the time evolution.

This four-step technique considerably enhances robustness against quasi-static errors and 1/f noise. Numerical simulations show it can reduce systematic errors by two orders of magnitude for rapid gate times. The single-step strategy is excellent for low noise, although the four-step chiral anisotropic technique often outperforms it.

The echo protocol has unexpected fidelity improvements with additional control qubits and can be adapted for multi-control C^(N-1)Ry gates with over three qubits. This suggests it might be utilised for larger quantum systems.

Measure the Mysterious Three-Qubit Interaction

A dynamical decoupling (DD) method was suggested to assess interaction intensity precisely and simplify experimental validation. This method selectively extracts the term from generic three-qubit Hamiltonians. Numerical simulations showed that a four-layer DD sequence with eight single-qubit operations can produce accurate measurements with differences below 10⁻³. This accuracy is needed to calibrate and implement direct three-qubit gates.

In conclusion

Innovative protocols advance spin-qubit quantum computation on a large scale. By directly enabling rapid, high-fidelity multi-qubit gates, they overcome low-fidelity gates and complicated circuit depths, advancing near-term quantum processor development.

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ITTI Sets Latin American Distribution For SignQuantum’s PQC

ITTI and SignQuantum Partner for Latin American Quantum-Proof E-Signatures

ITTI, a leading Paraguayan digital transformation provider, and SignQuantum, a quantum-resistant e-signature expert, announced an exclusive Latin American distribution arrangement. This strategic cooperation could improve digitally signed document security by protecting banks, insurance providers, and government organisations from quantum computing.

ITTI's vast clientele has a substantial impact, serving nine of twenty-two Central Bank of Paraguay-regulated financial institutions and handling over 40% of the country's financial transactions using its main software. ITTI can accelerate Mexico's adoption of post-quantum security solutions due to its market presence. ITTI Vice President Luis Angulo said, “This partnership is a strategic addition to the service portfolio,” acknowledging its importance. SignQuantum's quantum-resistant solution protects clients' sensitive data in this fast-growing digital signature market and provides them with the latest technologies.

Meeting the Quantum Threat to Digital Security

A major shortcoming in the current digital security architecture necessitates quantum-resistant digital signatures.

This risk increases as quantum computing accelerates. IBM will provide large-scale, fault-tolerant quantum computers by 2029, requiring future-proof solutions. NIST will deprecate digital signature technologies in 2030 and promote quantum-safe cryptography to address this issue. US lawmakers want post-quantum technologies implemented quickly to counter such assaults. ITTI and SignQuantum's partnership immediately addresses this vital requirement, protecting against future quantum attacks.

Unique Quantum-Resistant Solution from SignQuantum

SignQuantum offers an integrated smart solution. Businesses can start their quantum-safe transformation without disrupting operations. This self-hosted, quantum-resistant e-signature add-on works with existing systems. The unique technique addresses two key issues: digital signature authenticity and timing verification.

The NIST-recommended post-quantum algorithm underpins SignQuantum. It uses the quantum-resistant blockchain QANplatform for immutable time-stamping. Blockchain integration provides unmatched protection and integrity for key digital contracts and documents by allowing tamper-proof document validity checks. Johann Polecsak, co-founder and CTO of QANplatform, said, “It is tremendously exciting to see QANplatform's technology being implemented globally with real-life use cases like SignQuantum.” Another chance to show that QANplatform can handle and safeguard ITTI's clients' huge volume of digital transactions using SignQuantum.

Strategic Move by ITTI in Booming Market

The rapid growth of the global digital signature sector makes this alliance timely. With a 40.9% CAGR, this sector is expected to reach USD 118.88 billion by 2032 from USD 10.80 billion in 2025. Rapid digital change, regulatory compliance, and remote-first operational paradigms are pushing this.

ITTI is leading this fast-growing area and increasing its service range by offering SignQuantum's cutting-edge technology to its Latin American clientele. ITTI is well positioned to push post-quantum security adoption with a strong foundation established on the trust of 9 of 22 financial institutions regulated by the Central Bank of Paraguay, according to SignQuantum CEO Nazmath Nazeer. Their success makes them the ideal partner to accelerate this vital transformation in Latin America.

Latin American Post-Quantum Pioneer

Partnering to secure Latin America's digital infrastructure against quantum computing is proactive. ITTI's digital transformation expertise and SignQuantum's quantum-resistant technology protect private banking, insurance, and public sector data. ITTI is excited to announce its first deployment of the solution, and both companies are committed to helping businesses navigate secure digital signatures. The collaboration emphasises the necessity for strong, quantum-resistant solutions and the importance of timing integrity and document validity in a cyberconnected future.

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Quanta Computer Invests $50Million Funding in Quantinuum

Quanta Computer

Quanta Computer Bets $50 Million on Quantinuum, Quantum Computing Leader

The $50 million strategic investment by well-known Taiwanese electronics firm Quanta Computer Inc. in Quantinuum Ltd. shows growing corporate interest in cutting-edge computer technologies. This investment will put Quanta, a leading maker of servers, laptops, and other devices for international brands, at the forefront of quantum computing. Quanta believes it is a long-term holding compatible with its advanced computing strategy, indicating a strong desire to participate in high-performance computing.

Investment details

According to Quanta's financial statement, the board approved the purchase of 1,867,840 Quantinuum Series B preferred shares on August 12 for $26.7689 each. Quanta will fund the $50 million investment entirely. This investment is a strategic 0.49% fully diluted Quantinuum stake. Financial sources said the deal is part of Quantinuum's latest capital issue, which is expected to raise $400 million. The share price in Quanta's prospectus values Quantinuum at $10 billion in this Series B fundraising round.

Quanta's Advanced Computing Strategy

Quanta Computer is aggressively diversifying beyond electronics into data centre and high-performance computing systems. That strategic attitude is shown in their Quantinuum investment. The company stated the board supported the choice following a thorough internal examination. Quanta's latest financial statement reveals 6.01% of total assets and 22.56% of parent company shareholder equity from the NT$1.465 billion investment. Quanta is affected financially by these numbers. Quanta's commitment reflects its belief in quantum computing's future.

Leader in quantum innovation

Honeywell Quantum Solutions and Cambridge Quantum founded quantum computing leader Quantinuum in 2021. The company is creating quantum hardware and software for unprecedented processing. Its research and business focus on many vital applications, including:

Cybersecurity

Simulation of chemicals

AI

These domains represent challenging problems that quantum systems can address expertly. Quantinuum is popular among investors. Before its $300 million Series A fund-raise in February 2024, the company was worth $5.3 billion. In that round, Amgen, Mitsui, and JP Morgan Chase invested. Quanta has joined the Series B financing, which indicates a rising valuation and investor trust for the quantum computing pioneer.

The Trend: Corporate Interest in Quantum's Commercial Potential Quanta owns a modest percentage of Quantinuum, but this investment is part of an increasing trend of firms exploring quantum computing for business. Despite its youth, the technology is promising. Quantum systems can address data problems that even the most powerful conventional supercomputers cannot by leveraging quantum physics. Therefore, enterprises seeking data processing, optimisation, and complex simulation advances must focus on quantum computing. Quanta's action shows that huge firms are realising that investing in this developing industry may yield long-term competitive advantages.

Transaction formalities and Quantinuum's position

The petition did not restrict share delivery or payment. Quantinuum's capital growth timeline will guide the subscription process, and Quanta's board and audit committee authorised the purchase the day of the announcement. Quanta disclosed that Quantinuum has not commented on the investment. Privately invested enterprises often go silent, especially when the investor makes news.

This calculated investment shows Quanta Computer's commitment to cutting-edge computing and the industry's economic viability and growth, enabling greater developments and uses.

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IBM Quantum Credits Program Fuels Quantum Innovation

IBM Quantum Credits

IBM Quantum Credits Program: Fuelling Utility-Scale Quantum Research

IBM is expanding its IBM Quantum Credits program and inviting top quantum researchers worldwide to apply for exclusive access to its cutting-edge quantum computing capabilities. Top researchers have free access to IBM quantum computers since 2016. The enhanced IBM Quantum Platform houses important program enhancements that continue this heritage. We aim to accelerate the hunt for high-impact, utility-scale quantum projects by removing financial and other barriers.

A Legacy of Access and Innovation

The IBM Quantum Credits program, formerly the IBM Quantum Researchers Program, was introduced in the summer of 2020 and builds on IBM's 2016 cloud-based quantum processor decision. IBM's commitment to building usable tools has ignited an era of quantum discovery, allowing researchers, scientists, and engineers from around the world to contribute to a quantum advantage. IBM has introduced enterprise-level quantum products to its stack, but it still values open-source principles for quantum computing research and development.

Accessing Cutting-Edge Capabilities

Upgraded Credits program participants can use cutting-edge quantum gear and software. Utility-scale dynamic circuits and the upcoming IBM Quantum Nighthawk processor are major breakthroughs. Compared to its heavy-hex lattice predecessors, the Nighthawk's innovative square lattice architecture is predicted to enhance effective circuit depth by 16x, challenging scientists' limits.

The timing of this improved access is essential as the quantum community approaches quantum advantage. With IBM's quantum systems' faster runtime and coherence times, the gap between quantum advantage and hardware capabilities is decreasing. A historic 2023 quantum utility experiment may now be completed in 80 minutes, 85 times faster than with the first-generation stack.

Researchers are using fractional gates and innovative dynamic decoupling mechanisms to scale quantum computations. New and powerful quantum algorithms like sample-based quantum diagonalisation (SQD) are opening up interesting new application research areas. Participants execute their experiments on a fleet of the most advanced IBM quantum computers, including ones with 127-156 qubits and constantly improving.

Apply for Utility-Scale Research

IBM Quantum Credits emphasises utility-scale research. High-impact, cutting-edge proposals focus on issues larger than 30 qubits. Applicants are judged on their originality, calibre, and ability to investigate real utility-scale challenges that push the limits of traditional approaches, as well as their feasibility, which should be finished in a year and require five to ten hours of computation time. Successful submissions show innovation and scalable quantum approaches, advancing in 5–10 hours of QPU time.

Applicant prerequisites include:

One of the top quantum computing researchers in academia and industry, producing promising results. Permanent or tenure-track academic employment in a research institute.

Lack of IBM Quantum computers outside the Open Plan.

Quantum scientists can apply to physics, chemistry, computer science, engineering, and materials science. IBM delivers strategic papers written with quantum computing experts to inspire research initiatives in optimisation, high-energy physics, materials science, and healthcare.

A global quantum progress program

The IBM Quantum Credits effort has influenced the global quantum community. Over 30,000 quantum computation hours have been awarded, averaging 5–10 hours per project.

Program impact extends beyond immediate participants. This program advances quantum research, as shown by the more than 3,600 citations of IBM Quantum Credits-funded research articles. Interactive working sessions at the IBM Quantum Developer Conference 2024 demonstrate this focus.

The IBM Quantum Credits program aims to help the community better quantum science together. Talented researchers are equipped, free, and supported to conduct utility-scale research. This outreach should strengthen the quantum ecosystem and advance quantum advantage and fault-tolerant, large-scale quantum computing.

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UK NQCC Receives Oxford Ionics Quantum Quartet Computer

Quantum Quartet

Oxford Ionics Gives the National Quantum Computing Centre a Revolutionary Quartet Quantum Computer.

Oxford Ionics, a leading trapped-ion quantum computing business, delivered and installed Quartet, a cutting-edge full-stack quantum computer, to the UK National Quantum Computing Centre (NQCC). This accomplishment is significant for the NQCC's quantum computing testbeds initiative and the UK quantum landscape. The Quartet system is prominently shown in the NQCC's Harwell quantum data centre.

The UK's national quantum computing laboratory, the NQCC, advances cutting-edge applications research. It collaborates with government, commercial, and academic partners. The NQCC and Innovate UK created the testbed effort to test and develop quantum computing commercial use cases, including funding Oxford Ionics' quantum computer. This project shows the UK's commitment to quantum technology's rapid adoption. The NQCC receives most of its funding from the UK Research and Innovation research agencies Science and Technology Facilities Council and Engineering and Physical Sciences Research Council.

Technological Power: Quartet's Heart

Oxford Ionics' Electronic Qubit Control technology distinguishes Quartet, a trapped-ion, full-stack quantum computer. This innovative qubit technology uses electronics instead of lasers, a major change from existing methods. This architecture integrates all the components needed to trap and control qubits onto a standard electrical chip, which can be made in semiconductor foundries.

This integration onto ordinary processors solves one of quantum computing's main issues and provides unprecedented performance and scalability. This method has made Oxford Ionics the world's best quantum platform for quantum states preparation and measurement (SPAM), single-qubit gate fidelity, and two-qubit gate fidelity. These performance parameters indicate a quantum computer's processing power and reliability.

Field Upgrades for the Future

Oxford Ionics' quantum computers are special because of its field-upgradability. This means the NQCC Quartet system can be quickly adjusted to match the needs of the best-performing systems. Only the credit card-sized Quantum Processor Unit (QPU) needs to be replaced, simplifying the operation. With these features, the NQCC can smoothly improve performance and processing power at previously unheard-of speeds, which is breakthrough for quantum infrastructure and doesn't require infrastructure modifications. This “future-proof” design will keep the NQCC at the forefront of quantum capabilities.

Strategic Research and National Quantum Missions

Oxford Ionics and the NQCC collaborate after installation. Quartet will support crucial research and development as part of the UK's Quantum Missions program. This massive governmental initiative funds quantum computing projects that aim to remove technological barriers to quantum technology's commercialisation and adoption. Oxford Ionics, Riverlane, and Bay Photonics were selected for a Quantum Missions pilot as part of their Q-Surge initiative earlier this year, demonstrating their creativity. The project aims to improve Quartet by adding 2D qubit connection, which should make it more usable for complex applications.

Leadership Views on Quantum Future

Dr. Michael Cuthbert, Director of the UK's National Quantum Computing Centre, was excited about Oxford Ionics' QUARTET trapped-ion quantum computer installation, which represented a major step forward in the NQCC's quantum computing testbeds effort. The system's proprietary architecture overcomes quantum computing scaling issues. We can't wait to test and validate the technology to design new apps and algorithms. He stressed the NQCC's focus on fundamental research and quantum computing applications.

Dr Chris Ballance, co-founder and CEO of Oxford Ionics, agreed, saying, “Setting up Quartet at the NQCC is a significant milestone for business as well as for opening the door to a quantum computing-powered future. Quartet ensures we have the processing power to solve some of the world's biggest challenges and is a crucial step towards commercial quantum computing. We are proud to assist the NQCC as they develop groundbreaking applications that can change the world. His statement emphasises the technology's strategic role in solving global problems and making a difference.

Strong Growth and Ambition for Oxford Ionics

Oxford Ionics' NQCC delivery ends a period of significant growth and strategic development. The company underwent a major transition after Dr. Ballance and Dr. Tom Harty co-founded it in 2019. Oxford Ionics was bought by US quantum computing firm IonQ for $1.08 billion in June 2025. This deal promises quantum computing synergy by combining both businesses' assets. The merging companies have ambitious quantum advancement targets. They expect 256 physical qubit systems with 99.99 percent accuracy in 2026.

Quartet's installation at the NQCC advances quantum computing commercialisation. Quartet's unique architecture, upgrade capabilities, government funding, and recent business expansion help it shape UK and global quantum computing.

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Efficient Quantum Error Correction With Ancillary Qubits

Qubit auxiliary

NTT Researchers Lead Quantum Error Correction and Optimise Qubit Counts for Scalable Quantum Computers.

Scientists Shintaro Sato and Yasunari Suzuki of NTT Computer and Data Science Laboratories and their colleagues have developed a framework that reduces the large qubit overhead needed for quantum error correction, paving the way for scalable and useful quantum computers. Their revolutionary study reveals that a careful balance between data qubits that store quantum information and ancillary qubits that check errors can cut logical error rates even with fewer auxiliary qubits. This discovery challenges assumptions and leads to more reliable and effective quantum computing systems.

Noise and environmental challenges plague quantum computers, despite their promise. QEC, which overcomes this fragility, employs auxiliary qubits to discover and fix problems without seeing the logical qubit's sensitive quantum states. Scaling quantum computers is difficult since the number of qubits often equals syndrome measurements needed for error detection and adds complexity and cost.

NTT's innovative solution quickly solves this problem by reducing the number of additional qubits and simplifying error detection. Their method optimises measurement patterns and intelligently reuses auxiliary qubits. Their approach allows a systematic search for small, efficient circuits by modelling the syndrome measuring process as a series of transitions utilising two-qubit gates instead of assigning an auxiliary qubit to each error check. This innovative measurement sequence ensures qubit reuse by dramatically reducing qubit overhead without losing speed.

To run the method, a logical qubit is encoded into multiple lattice-stacked physical qubits. After that, auxiliary qubits detect errors. For this complex process, the framework tracks syndrome extraction using a Parity Check Processing Matrix (M) that documents qubit interactions, a Qubit-to-Location Map (P) that describes qubit positions, and an Unmeasured Operator Label List (L) that indicates remaining error checks.

The algorithm's guaranteed termination keeps computation moving without stalling or cycling. It separates each auxiliary qubit's state into four scenarios and decides what to do, such as measuring it or using CNOT or SWAP gates to push qubits closer together. A “tie-breaking” method is implemented to avoid stopped situations and compel qubit motions when progress is slow. After the circuit is generated, any unnecessary two-qubit gates are removed to improve efficiency and reduce error induction.

To test their methods, the researchers changed the data-to-supplementary qubit ratio in surface codes, a top candidate for realistic quantum computation. Their numerical investigations included depolarising noise from CNOT gates, SWAP gates, and idle times using a circuit-level noise model.

As the number of ancillary qubits increased, circuit depth (the length of the critical path of two-qubit gates) and volume (depth multiplied by total physical qubits) decreased, demonstrating that their algorithm generates shallower circuits. Reduced idling error occurrences during syndrome extraction affect logical error rates, hence circuit depth reduction is crucial.

They discovered something interesting when studying noise types. The number of supplementary qubits did not increase logical error rates when errors affected CNOT or SWAP gates. However, when idle errors were the main noise source, logical error rates skyrocketed as the number of additional qubits decreased. This means that reducing auxiliary qubits predominantly affects idle mistakes, whereas two-qubit gate faults remain substantially unaltered.

How to distribute qubits may be the biggest finding. Instead than maximising data or auxiliary qubits, the researchers found that balancing them improved logical error rates while maintaining the overall number of physical qubits.

This groundbreaking discovery shows that fewer qubits than error checks can improve performance within a size restriction. This unique design technique is especially beneficial for qubits with extended coherence lengths, where idle mistakes are less of an issue.

This new paradigm advances the development of practical and scalable quantum computers. NTT's study reduces qubit overhead and streamlines communication, addressing quantum technology's most pressing concerns.

Future research could optimise qubit initial placements, expand the framework to support more operations (such as CNOT gates between supplementary qubits) and non-CSS codes, and customise the framework to hardware characteristics like gate latencies. The work is a quantum computing milestone and lays the groundwork for powerful and practical quantum computers.

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Unlocking Hidden Alzheimer’s Disease vs Quantum Computing

Alzheimer's vs. Quantum Computing

Studies of neurodegenerative diseases using quantum and classical methods reveal hidden trends.

Harvard Medical School and Massachusetts General Hospital researchers' mathematical framework may improve our understanding and treatment of progressive neurodegenerative disorders like Alzheimer's, MS, PD, and ALS. Dr. John D. Mayfield's group introduces a new method that translates time-based data into the frequency domain, revealing weak, cryptic rhythmic patterns that normal analytical methods miss.

Recent advances in quantum machine learning (QML) have shown impressive accuracy in classifying Alzheimer's disease. This novel framework integrates classical and quantum computing, uses sophisticated quaternionic representations, and seeks to improve disease progression and therapy resistance prediction.

Traditional time-domain analysis methods like transformer models and standard LSTM networks struggle with neurodegenerative illnesses' high-dimensional, noisy data. These models often fail to predict biomarkers like amyloid PET SUVR and CSF tau because to their variability. Traditional methods' focus on amplitude, which overlooks phase information, is a major shortcoming.

Phase data is needed to capture neural network temporal coordination, such as multivariate cognitive changes, tau deposition cycles, or DNN fluctuations. Noise and intrinsic nonlinearity mask underlying periodicities such oscillatory tau accumulation or cyclic myelin degradation in M.

The proposed framework formalises a frequency-domain method to address these issues. Fourier and Laplace transforms are a major invention for converting multiomic and neuroimaging time-series data into the frequency or s-domain. By transforming complicated signals into sinusoidal components, researchers can uncover dominant rhythms and periodicities. A discrete data representation using the Discrete Fourier Transform (DFT) encodes phase (temporal shift) and amplitude (signal strength) for different frequency bins.

This decomposition helps distinguish between high-frequency fluctuations and low-frequency trends, which is especially useful in AD, where tau cycles predominate at lower frequencies. For continuous systems, the Fourier transform is used, while the Laplace transform, which adds decay, maps data to the s-domain and aids stability investigations in progressive illnesses. Quantum Fourier transforms (QFT) decrease aliasing in underdamped biological data better than Fast Fourier transforms due to their logarithmic gate complexity.

Quantum mechanics is used to mimic neurone dynamics using a Hamiltonian framework. New data suggests that quantum processes like brain signalling entanglement or microtubule network coherence may produce rhythmic patterns in disorders like Alzheimer's. Neuroimaging parameters like DTI myelin density and resting-state functional MRI synaptic connections are included in the Hamiltonian.

A perturbation operator explains disease-specific changes (such as tau functioning as local fields), while an unperturbed Hamiltonian represents a healthy state. Non-degenerate first-order perturbation theory measures the effect of disease on healthy eigenstates by producing frequency-domain signals like shifting energy levels that may indicate tau-induced connection problems and correlate with clinical ratings.

Quaternionic representations, a 4D hypercomplex algebra with three imaginary units, expand this paradigm. Quaternionic extensions may describe non-commutative multidimensional interactions like amyloid, tau, and inflammatory synergy, which complex representations may undervalue, yet standard quantum mechanics uses complex numbers.

This method is comparable to quantum neuromorphic models of entangled neurone dynamics. Inflammation, amyloid aggregation, and tau dynamics are described by quaternionic Hamiltonians. This makes high-dimensional amplitude-phase data easier to analyse, making outliers and frequency fingerprints of multistate transitions and sickness development easier to discover.

The system uses quantum-classical hybrid computing, notably the Variational Quantum Eigensolver (VQE), to solve classical brain-scale model exponential scaling difficulties. VQE optimises a parameterised quantum circuit using a conventional optimiser to approach quantum system ground states. This allows quantum machine learning applications like Alzheimer's MRI categorisation to use up to 16 qubits for modality subsets.

In QML predecessors, QNN and Q-LSTM could classify Alzheimer's with 99.89% accuracy using MRI and handwriting data. Quantum Support Vector Machines (QSVM) use quantum kernels to identify high-risk patients with abnormal low-frequency amplitudes and angle encoding to include frequency vectors into quantum states for frequency analysis and outlier detection. By using logarithmic gate complexity instead of polynomial complexity, the QFT speeds up spectral analysis.

This paradigm offers therapeutic potential, especially in identifying high-risk patients who are resistant to treatment or progress quickly. The frequency-domain fingerprints in the s-domain, especially low-frequency oscillations linked to tau buildup in AD or cyclic myelin degradation in MS, give novel biomarkers. AD patients with anomalous low-frequency amplitudes in tau PET SUVR or CSF tau revealed by QSVM outlier analysis may have accelerated amyloid-tau synergy, which accelerates cognitive impairment.

Frequency analysis of DTI fractional anisotropy can reveal cyclic myelin degradation in MS, identifying patients at risk of rapid disability development. Combining handwriting analysis with high-frequency tremor patterns induced by dopamine depletion may identify Parkinson's disease patients who are resistant to treatment. The approach could also predict pharmaceutical response, identify AD lecanemab non-responders, and enable more individualised treatment regimens. Adding these s-domain features to clinical decision support systems and leveraging quantum kernel approaches for real-time outlier detection could improve patient outcomes.

Despite its speculative nature, this study provides a solid conceptual foundation. Error rates, the need for quantum advantage, and noisy intermediate-scale quantum (NISQ) device restrictions remain challenges. Quantum hardware and huge datasets like ADNI and PPMI will be used to objectively test performance against classical baselines. This theoretical paradigm could revolutionise precision medicine by enabling earlier and more effective neurodegenerative disease therapies. It advances neuroscientific quantum computing greatly.

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QCopilot: Automating Quantum Sensing Experiments With LLMs

QCopilot

The revolutionary QCopilot Framework enables automated atom cooling and 100x experimentation speedup, enabling autonomous quantum discovery.

QCopilot, a breakthrough framework, can automate challenging experiments, speeding discovery and reducing human engagement. This is a major advance in science, especially in quantum sensing. Rong and colleagues' QCopilot uses numerous interacting large language models (LLMs) to design, diagnose, and optimise experiments in the complex field of atom cooling.

QCopilot addresses the issues of complex scientific systems, which often require interdisciplinary expertise and are laborious, time-consuming, and biassed by humans. By automating laborious tasks, the framework makes it easier to study experimental parameters.

The coordination of specialised AI agents, along with dynamic learning, access to outside knowledge, and rigorous uncertainty evaluation, enable unprecedented scientific research autonomy.

QCopilot's complicated multi-agent architecture is its core. This system can reason, plan, and understand experimental settings like human scientists using external knowledge and pre-trained language models. Important components include:

Decision Maker: This agent analyses complex topics and chooses the optimal course of action using previous data and web searches. Experimenter: This agent autonomously adjusts experimental parameters to optimise system performance based on Decision Maker instructions using active learning. Analyst: This agent models projected system behaviour to set a baseline. Multimodal Diagnose: This agent analyses multiple data sources, including photographs, to find anomalies. Recorder and Web Searcher: These agents work with diagnostic agents to find problem sources for autonomous fault rectification and focused troubleshooting. Read also A 2D Quantum Simulator Captures Real-Time ‘String Breaking’

This integrated strategy lets QCopilot learn from mistakes and optimise experiments to produce a self-improving experimental system. The bidirectional framework can diagnose problems in reverse and optimise experimental settings.

QCopilot demonstrated ultra-cold atom creation for high-precision quantum sensors. Without people, the team reached temperatures below one Kelvin and one microkelvin in a thick atom cloud. This 100-fold increase in experimental speed compared to manual approaches was achieved in a few hours. QCopilot performed multi-objective optimisation in this cold atom experiment by decreasing the temperature of the confined atoms and increasing their number, a difficult task manually. Bayesian optimisation and experimental data knowledge are used to identify optimal settings across a variety of experimental controls.

QCopilot excels at adaptive and active learning, going beyond pre-programmed commands. Every experiment teaches it to spot uncommon parameters and dynamically enhance its optimisation strategies. QCopilot's dynamic modelling capability lets it generalise its performance even when the environment changes, which is useful in complex experimental setups where various factors might effect findings. The system can also discover unusual factors in complex experiments, which is essential for building cutting-edge technologies.

AI-driven frameworks have many benefits:

Automating tedious tasks enhanced experimental efficiency. Better optimisation by exploring bigger parameter ranges yields perfect solutions. Reduced human bias ensures a more objective and reliable experimental technique. By substantially cutting research timelines, discovery was hastened. Increased scalability for complex experiments. Read ColibriTD Launches QUICK-PDE Hybrid Solver On IBM Qiskit.

QCopilot has great promise yet has challenges. Due of online huge language model access, the current iteration's offline application is constrained. Researchers acknowledge the difficulty of comprehending AI models' complex decision-making processes and the need for massive datasets to train AI systems. Getting AI models to connect with present infrastructure and generalise to new data is harder.

However, QCopilot appears promising. For installation on regular hardware and autonomous quantum sensor operation in field applications, the authors anticipate integration with localised inference models. This may simplify academic and commercial application of cutting-edge technology like cold-atom-based quantum sensors.

In conclusion

QCopilot could automate scientific research and transform how complex quantum experiments are designed, executed, and assessed. This will enhance our understanding of the quantum realm. By simplifying quantum mechanics and encouraging rapid invention, this intelligent multi-agent system could revolutionise quantum research.

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Trotter Errors: New For High-Precision Quantum Simulations

Trotter Error

New quantum simulation method promises Improved Results with Shallower Circuits Trotter Steps

IBM Quantum and KIST researchers disclosed a resource-efficient strategy to dramatically reduce algorithmic errors in quantum simulations, advancing near-term quantum computers. The unique “error profiling” methodology beats multi-product formulas (MPFs) and addresses the longstanding problem of “Trotter error,” which reduces quantum simulation accuracy.

Understanding the intricate dynamics of quantum systems is crucial to modern physics, including quantum chemistry and materials research. Complex many-body systems are difficult to model on classical computers due to the vast Hilbert space and exponential processing cost increase with system size. Quantum computers, especially those that mimic Hamiltonian time evolution, may circumvent these limits.

The Trotter Error Challenge

Trotterization, also known as product formulas, is a common quantum simulation algorithm that is ideal for current and future quantum devices. This technique approximates the time evolution operator by decomposing it into quantum gate products. Trotterization is simple and has low overhead, however it has an algorithmic flaw termed the Trotter error. This error occurs because Hamiltonian components rarely commute, therefore their application order matters.

Increasing the number of divided steps in Trotterization has reduced this error, but it deepens the quantum circuit. Physical faults in noisy quantum gear may damage deeper circuits more, complicating the simulation accuracy-practical viability trade-off. Previous attempts to reduce Trotter error, such as multi-product formulas (MPFs), needed deeper quantum circuits or complex implementations like linear circuit combinations, which could increase physical faults and experimental problems.

MPF methods often simulate the same scenario multiple times with varied Trotter step counts and then post-process. Algorithmic error mitigation includes polynomial interpolation and Richardson extrapolation, like hardware error mitigation. They achieved “commutator scaling,” where the cost is completely governed by nested commutators of Hamiltonian terms, while improving precision and lowering exponentially.

Error Profiling: An Introduction

The new study by Sangjin Lee, Youngseok Kim, and Seung-Woo Lee presents a resource-efficient algorithmic Trotter error-reducing method with a short circuit depth. Their profiling approach uses an auxiliary parameter, ‘a’, to assess expectation value error implications, their key innovation. This technique suppresses errors while maintaining Trotter error, making it ideal for near-term quantum processors.

Method operation has three steps:

First, researchers simulate Trotterized circuits with bespoke composite operators. Both the original Trotterized circuit V(t) and its inverse V†(t) are combined to form these operators. Rearranging current gates is sufficient to implement V†(t), without affecting circuit complexity or depth. Error Parameter variation profiling: While keeping the simulation length ‘t’ constant, the auxiliary parameter ‘a’ is changed to efficiently profile Trotter error. Measure the expectation values of a specified observable. Importantly, changing ‘a’ should not affect the outcome in a perfect quantum simulation, but in Trotterized circuits, the results do depend on ‘a’ since the combination of error terms creates different error profiles. This dependence is used in profiling. Ideal Value Estimation: The researchers can precisely estimate the ideal expectation value by contrasting the gathered data with behaviors that are theoretically expected. This procedure, which involves fitting the profiled data into a computed theoretical function, can be thought of as least squares fitting.

Key Advantages and Performance

The suggested error profiling (EP) approach has a number of strong advantages over current methods, such as MPFs:

Fixed Trotter Steps & Shallow Circuits: The EP approach can be implemented with a set number of Trotter steps, in contrast to MPF, which frequently calls for altering Trotter steps and adding more quantum gates in deeper circuits. Because of this, it is much more robust for noisy hardware because it does not generate extra physical defects that are usually linked to higher circuit depth. The overall performance of quantum simulations is improved by this circuit shallowness, which also increases the efficacy of other popular error mitigation strategies like probabilistic error cancellation and zero-noise extrapolation. Enhanced Error Mitigation: The EP approach uses shallower quantum circuits for a specific mitigation order. Superior Performance in Benchmarks: The study shows that the profiling approach performs better than MPF in simulations of two representative models: the XXZ spin chain and the one-dimensional transverse field Ising model (1D TFIM). It was demonstrated that the EP approach suppresses Trotter errors by almost two orders of magnitude more than MPF for common Trotter formulas of orders α=4 and α=5. Resource Efficiency in Noisy Hardware: The MPF approach usually needs O(N^2) Trotter circuits for error mitigation in noisy conditions, where ‘N’ is the number of Trotter error stacks. The suggested profiling approach, on the other hand, only needs O(N) circuits, demonstrating its exceptional efficiency.

Future Outlook

The usefulness of Trotter error mitigation strategies in algorithmic applications is strongly supported by this work. The researchers think that by utilizing the physical characteristics of the Hamiltonian, such its symmetry, and investigating the hierarchical relationships between matrix elements in the profiled errors, additional performance improvements may be achievable.

By offering a reliable tool to handle both algorithmic and physical mistakes, this novel error profiling technique is a major step towards making realistic quantum simulations possible on near-term quantum processors. Such resource-efficient error mitigation techniques will be essential to maximizing the capabilities of these formidable machines prior to the introduction of complete fault tolerance as quantum computing continues its rapid development.

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Rice University Research Creates Record Phonon Interference

Rice University Research

Rice researchers achieve record-strong phonon interference, unlocking quantum potential.

Rice University researchers discovered record-strong quantum interference between phonons, the basic quanta of heat or sound, furthering quantum mechanics and enabling thermal management, sensing, and quantum technologies. This revolutionary Science Advances discovery shows how minuscule quantum vibrations can be used as efficiently as light or electrons, altering next-generation device building.

Light, sound, and atomic vibrations can interact and magnify each other like pond ripples. This interference powers high-precision sensors and is important to quantum computing. Phonon interference has received less attention than electron and photon interference. However, phonons' long-term wave nature makes them attractive for high-performance, stable electronics.

The Discovery of Record-Strong Phonon Interference

Fano resonance occurs when two phonons with different frequency distributions interact. Rice's team achieved two orders of magnitude greater Fano resonance than any other. This exceptional strength shows phonons' untapped potential in quantum technologies.

Kunyan Zhang, the study's first author and former Rice postdoctoral researcher, says the findings are noteworthy. Zhang observed that electron and photon interference have been studied more than phonon interference. Since they can maintain wave behaviour for long durations, phonons could be stable, high-performing electronics.

The Breakthrough Mechanism

Innovative use of a two-dimensional metal on silicon carbide makes the team's finding conceivable. The researchers used confinement heteroepitaxy to intercalate silver atoms between graphene and silicon carbide. This approach produced a strongly bonded contact with extraordinary quantum properties.

This two-dimensional metallic film acts as a catalyst, making it easier and achieving record vibrational interference between silicon carbide's various phononic modes. The illustration shows a two-dimensional metal (middle layer) between silicon carbide (bottom) and graphene (top).

Unmatched Sensitivity and Detection

The research team examined phonon interference using Raman spectroscopy, which detects material vibrational modes. The Raman spectra's highly asymmetric line shape and occasional entire dip created the antiresonance pattern, indicating strong interference. Besides detecting minute material changes, these spectral fingerprints can also indicate their surroundings.

The effect was sensitive to silicon carbide surface details. A substantial correlation was found between the Raman line forms of three silicon carbide surface terminations. The spectral line changed significantly when the researchers put a single dye molecule to the surface. “This interference can detect a single molecule because it is so sensitive,” Zhang said. It allows label-free single-molecule detection with a simple and expandable setup.

Additional analysis of the dynamic effect at low temperatures revealed that phonon interactions, not electrons, caused the interference, revealing a rare occurrence of phonon-only quantum interference. The study's 2D metal/silicon carbide system shows this effect, whereas bulk metals do not due to the atomically thin metal layer's unique surface topologies and transition routes.

Making Way for Next-Generation Tech

This phonon-based technique advances molecular sensing and opens up quantum technologies, energy harvesting, and thermal control, which need vibration manipulation. The team also examined if gallium or indium may cause similar effects. Researchers could modify these intercalated layers' chemical makeup to build quantum-specific interfaces.

The study's corresponding author, Rice associate professor of electrical and computer engineering, materials science, and nanoengineering Shengxi Huang, noted the benefits: The approach is sensitive, but traditional sensors require chemical labels and considerable device setup.

These findings have fascinating implications beyond lab experiments. Allowing extremely sensitive measurements without chemical labels or intricate gadget settings proposes a paradigm shift in understanding and engaging with molecular and atomic processes. These advances increase present capabilities and prepare for future vibrational state manipulation technologies.

Funding, wider impact

This groundbreaking research was funded by the Welch Foundation, Air Force Office of Scientific Research, and National Science Foundation, exhibiting teamwork. These findings show that phononic interference has a bright future in quantum technologies, improve material understanding, and inspire quantum research.

As feasible components in next-generation sensing systems, phonons offer a new materials science and engineering field. This study extends the topic of tiny quantum interactions in real-world applications and shows the promise of phonons in a future where quantum mechanics dominates. Quantum innovation research may lead to revolutionary advances in several industries by widening its scope.

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QDNL Participations Opens €60M Fund For Global Quantum

Increased QDNL Team and Global Fund Drive Quantum Revolution

QDNL Participations, a leading quantum technology venture capital firm, established a €60 million (USD 70 million) fund to invest in high-potential quantum technology firms worldwide to expedite the quantum information age. This announcement significantly expands the company's global ambitions. This new fund's €25 million inaugural closure marks a milestone in the company's goal of connecting groundbreaking scientific research to profitable economic ventures.

Fixing Quantum Commercialisation Gaps

Brilliant scientists with commercially feasible quantum notions have struggled to develop marketable solutions for too long. The quantum sector has suffered from weak growth, inadequate investment, and scientific stagnation due to a lack of economic backing for these professionals. Due to a shortage of specialised investors, many of whom are outside elite scientists' circles, it has been difficult to identify good opportunities and provide the persistent support needed for humanity to benefit from these advancements.

QDNL Participations was designed to address these crucial issues. From quantum research grant-giving to venture financing's ‘patient capital’ stage, the fund is crucial. Their goal is to turn good technology ideas into “obviously great investable companies” by providing business and commercial support. This involves helping talented technologists recruit top talent and commercialise their ideas for mankind.

Customised Support for Quantum Founders

QDNL Participations helps entrepreneurs at several stages of business development:

Idea-Stage Founders: QDNL Participations helps scientists with groundbreaking ideas who have not yet founded a business examine their ideas' practicality, remove early barriers to funding and company formation, and build leadership and financial confidence. This proactive method allows talented researchers to overcome inertia and start unbiased commercial conversations early.

Early-Stage Founders Seeking Venture financing: The fund assists incorporated companies with research financing gain institutional capital and set up their operations for long-term success. QDNL Participations reduces investor risks to improve governance, stability, and investability of early-stage, grant-funded, and research-led firms. Their help ensures founders meet technical roadmap deadlines, raise funds, and conduct vital research.

With a community of hard-to-reach, knowledgeable quantum talent, the fund's specialised team helps early-stage scientist founders become investable. They know how to reduce investor risks, open markets to new ideas, and build reliable teams and effective investment strategies. Talented researchers are not left to struggle with unproven concepts or small, grant-dependent enterprises that don't attract venture funding QDNL Participations fills knowledge gaps and boosts financial and leadership confidence.

Increase Global Reach and Expertise

QDNL Participations, which started with a €15 million fund to enhance the Dutch quantum technology ecosystem, now has global aspirations. To accommodate expansion, its personnel has risen dramatically.

The company just hired Nicola Weiroster as an investment team associate. He gained valuable experience as a Junior Investment Manager at Onsight Ventures, an Austrian deep tech venture fund, where he focused on early-stage European investments. Weiroster co-founded Austrian-Dutch space business Team Tumbleweed. He was eager to help companies turn science into scalable businesses and for quantum's commercial impact to emerge in the “pivotal phase”.

The QDNL Participations team includes General Partner Tonne van ‘t Noordende. He believes quantum will be the next “innovation cycle” after software and artificial intelligence and wants to start a pioneering investment company. Venture partner Chad Rigetti, creator of Rigetti Computing and quantum computing pioneer, has industry knowledge. Additional team members include Charles Marcus (special advisor), Nadia Carlsten (special advisor), Cheryne Jonay (analyst), and Kris Kaczmarek (investment director).

History of Innovative Investments

QDNL Participations has invested in some of the most creative quantum enterprises. Nine high-potential Dutch quantum companies, including Qblox, QuantWare, QphoX, and Q*Bird, have received their €15 million investment. The first international agreements are expected soon.

Notable portfolio achievements include:

QuantWare: Delft-based quantum hardware firm QuantWare closed a $27 million oversubscribed Series A investment with a $4.5 million extension. QuantWare is selling its patented VIO scaling technology to enable superconducting quantum computers with more than one million qubits and democratise quantum computing hardware.

Orange Quantum Systems raised €12 million in an oversubscribed seed round to accelerate quantum chip testing tool development. QDNL Participations and Cottonwood Technology Fund gave Orange Quantum Systems €1.5 million in pre-seed funding.

QT Sense: This Dutch quantum company raised €6 million to enhance Quantum Nuova for disease diagnosis. The finance came from Interreg Europe grants, QDNL Participations shares, and angel investors.

QphoX: The highest Dutch quantum startup investment, €8 million, helped QphoX commercialise its quantum modem technology and advance the quantum internet.

Q*Bird: This communications security firm raised €2.5 million to expand its quantum security business and safeguard enterprises from hacks and future quantum threats.

QuantaMap: The Dutch business raised €1.4 million for its cryogenics and quantum sensor-based quantum computer chip quality assurance solution.

Through strategic investments and comprehensive support, QDNL Participations is shaping the global quantum industry while accelerating quantum enterprise growth. The company invites people interested in learning more about its investment guidelines and how they may help quantum innovation succeed and profit.

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OPTIA & Patero Launch World’s First PQC-Enabled GPU Server

OPTIA and Patero Introducing the First Post-Quantum GPU Compute Platform for Defence and Edge Applications

OPTIA, a leading producer of ruggedised, high-performance GPU compute solutions, and Patero, a leader in post-quantum cryptography (PQC), announced the first PQC-enabled GPU server. This cutting-edge platform integrates Patero's CryptoQoR encryption suite into OPTIA's NVIDIA-based commercial and mission-critical defence systems. The August 12, 2025 announcement advanced next-generation AI and machine learning (AI/ML) workload protection, particularly at the periphery.

Quantum-Safe Encryption Revolutionises Data Security

This integrated system protects inbound and outgoing data streams against quantum assaults and cyberthreats. For C5ISR workloads, tactical edge analytics, and AI/ML acceleration, OPTIA's portable solutions are popular with the DoD. These devices smoothly integrate Patero's quantum-safe cryptography to deliver end-to-end encrypted streams.

OPTIA BD Director James Elder remarked, “This is a first-of-its-kind platform, a tactical NVIDIA GPU server with quantum-resilient protection.” This pre-integrated and bundled solution “delivers high-performance compute without compromising data security” regardless of operating system or threat surface. Patero COO Peter Bentley said, “Whether it’s battlefield intelligence, secure video feeds, or edge AI inference, when it leaves the OPTIA server, it leaves encrypted with Patero’s PQC,” emphasising the solution’s speed and utility. Field-ready quantum security, not a future possibility.

Compliance with Key Federal Initiatives

This strategic partnership and its platform align with key US federal goals and activities, emphasising its strategic importance for national security and military infrastructure. This includes:

E.O. 14028: Zero-trust cybersecurity for defence and federal systems. Patero's zero-trust cybersecurity network reduces network attack surfaces.

Federal entities must migrate to quantum-resistant cryptography, per National Security Memo 10. Patero provides a “Quantum Readiness” assessment and a list of encryptions to help firms move to quantum.

JADC2 recommendations: Safe, compatible edge computing power.

Patero's "double encryption" scheme encrypts data with classic and post-quantum keys, decreasing network attack surfaces and making data incomprehensible with quantum-safe encryption. There hybrid-PQC approach uses NIST-selected quantum-resistant encryption algorithms to boost standard cryptosystems. The attack surface is reduced and data security is considerably improved.

Market Differentiators and Key Features

The new platform has several key features:

The first NVIDIA GPU server with PQC capability for public, commercial, and military use. Systems can resist tough circumstances in harsh, forward-deployable environments due to their portability and durability. It provides end-to-end encrypted streams for AI/ML, ISR, robotics, and logistics. The solution meets ALL Post-Quantum migration standards from the NSA, DHS, and DOD. Ruggedised GPU platforms from OPTIA are known for their performance and durability. Future-proof security is readily integrated with NVIDIA's latest GPU architectures and Patero's CryptoQoR. Patero's lightweight, fast, and infrastructure-compatible CryptoQoR is ideal for resource-constrained edge applications. The NIST-standardized PQC methods it supports give it flexibility and resistance to new cryptographic threats.

Future Opportunities and Joint Market Execution

To capitalise on rising opportunities in various key industries, OPTIA and Patero are developing a wide range of secure solutions and form factors:

Defence programmes include C5ISR, AI/ML/LLM deployment, and situational awareness. Safeguarding smart ports, cities, airports, and the energy sector. Manufacturing, logistics, and autonomous platforms: Industrial AI. DoD agencies and defence contractors are reviewing the platform for deployment in the coming months. This partnership combines quantum-safe encryption and high-performance computation, a cybersecurity trend. As quantum technologies advance, the proactive, integrated OPTIA-Patero system secures edge computing, AI, national security, data integrity, and secrecy.

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TUSQ Simulation Streamlines Noisy Quantum Circuit Computing

The exponentially faster TUSQ simulator revolutionises noisy quantum circuit modelling.

Quantum computing could change health and materials research. The high cost and long wait periods of actual quantum gear remain a major impediment. Complex simulators that accurately and scalablely simulate quantum circuits on noisy quantum hardware are needed.

Siddharth Dangwal, Tina Oberoi, and Ajay Sailopal of the University of Chicago and their colleagues developed TUSQ—Tracking, Uncomputation, and Sampling for Noisy Quantum Simulation—to address this important issue. By reducing needless computations and reusing computational resources, this novel strategy speeds up noisy quantum circuit simulations by unprecedented amounts.

Traditional quantum circuit simulation (QCS) struggles with noise. Noisy QCS uses stochastic processes, whereas State Vector Simulation (SVS) can do noiseless QCS by multiplying a quantum state vector with deterministic unitary matrices.

A basic SVS technique must repeat the matrix-vector multiplications for each sample to account for probabilistic noise effects. This causes an S-fold time overhead, where S is the sample count. DMS uses matrices to represent quantum states and matrix-matrix multiplications to account for noise in a single circuit execution. Due to its quadruple memory overhead compared to SVS, DMS is unsuitable for many qubits.

TUSQ, like CUDA-Q and Qiskit Statevector Simulator, simulates noise with many SVSs, reducing memory footprint four times over DMS. But it also addresses the time overhead. TUSQ's efficiency comes from its cutting-edge Error Characterisation Module (ECM) and Tree-based Execution Module (TEM). To provide faster noisy QCS, these modules locate and reduce unnecessary or irrelevant calculations.

ECM helps streamline circuit execution

The ECM analyses stochastic noise channel circuits to locate samples with the same output, reducing the number of circuit iterations. This involves two important steps:

Error Realisation (ER) Tallying: A noisy quantum circuit is a classical average of multiple circuits with stochastic channel-sampled “fixed noisy gates”. This is error realisation (ER) tallying. TUSQ tracks these "error realisations" (ERs). If an ER occurs s times, TUSQ replicates the circuit once and samples its output state vector s times. This is significantly cheaper than multiple simulations. Modern quantum computers, which have low error rates, contain more ERs with low Hamming weights, making this operate well. Beyond counting, ER Commutation finds scenarios when many ERs can produce the same output state vector. TUSQ uses Pauli gate and CNOT commutation rules to "push" noisy gates as far right as possible without affecting the noiseless circuit. If two ERs develop identical new ERs by combining their shot counts, the number of distinct circuits to simulate is reduced.

Tree-based Execution Module-TEM: Computation Reuse

TEM optimises the execution of the minimal set of circuits with distinct outputs the ECM has developed by taking advantage of computational reuse. The module includes:

Depth-first Tree Traversal (DFTT): TUSQ depicts circuits as a tree with nodes representing state vectors and edges gates. TUSQ uses rollback-recovery instead of precalculating each circuit, like classical computer design. It “uncomputes” the final state vector for a single circuit (a leaf node) using the inverse of gates before shifting to a new branch to calculate a new circuit. It greatly reduces unnecessary matrix-vector multiplications. Note that TUSQ assumes all gates, even noisy ones, are unitary, which has been proved for a wide range of realistic noise models, including depolarising, measurement, and Pauli-twirling approximations for decoherence. Makes this computation possible. Because it does not memorise intermediate states, TUSQ has a minimal memory footprint and can parallelise DFTT using available RAM. DFTT has an asymptotic advantage by reducing operations from a naive implementation to, where |E| is the number of edges and b is the number of noisy channel possibilities. Pruning: To boost speed, TUSQ discovers “insignificant circuits” that emerge rarely after ECM because they have little effect on output distribution. These branches are removed to speed up processing. Even with a small, controlled disturbance, this is necessary for efficiency. TUSQ samples a subset of inconsequential circuits with a large aggregate contribution and adjusts their probability to maintain output distribution contribution. User-defined hyperparameters can alter this trimming's average relative fidelity difference from 2.1% to 8.7%.

Incredible Performance and Impact

TUSQ optimisations boost performance dramatically. The simulator ran 186 benchmarks on an Nvidia A100 GPU, including QAOA, Adder, Bit Code, Phase Code, and GHZ circuits.

Performance highlights include:

Average 12.53x speedup over CUDA-Q and 52.5x over Qiskit. Larger benchmarks (>15 qubits) speed up 55.42x over Qiskit and 23.03x over CUDA-Q. TUSQ sometimes surpassed Qiskit by 7878.03x and CUDA-Q by 439.38x. Similar simulators require almost 10 hours to replicate a 30-qubit Adder circuit, yet TUSQ did it in 819.87 seconds. TUSQ outperformed the newly suggested TQSim simulator by 68.6x and 493.4x. Due to its depth-first traversal and uncomputation approach, TUSQ is faster than TQSim, which requires more memory and memoization.

TUSQ's speedup increases with qubits, but deeper circuits or higher error rates may decrease it since more branches become “significant” and pruning becomes less effective. TUSQ's expected 1% error rate is typical of existing systems, and future technologies will make it more viable.

The development of TUSQ, a powerful tool for modelling noisy quantum circuits, advances quantum computing research. Resource reuse and computational overhead control allow researchers to study larger and more complex quantum algorithms with TUSQ, speeding up the shift to scalable quantum processing. Plans call for an open-source TUSQ, which should boost innovation.