#QuantumProcessingUnit

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IonQ & AstraZeneca Quantum Computing Boost Drug Discovery

IonQ & AstraZeneca Quantum Computing Boost Drug Discovery

AstraZeneca Quantum Computing A significant collaborative research breakthrough in the domains of quantum computing  and pharmaceuticals has been disclosed by AstraZeneca, Amazon Web Services (AWS), NVIDIA, and IonQ. This cooperative effort has successfully demonstrated a quantum-accelerated process for early-stage pharmaceutical development, demonstrating an astounding 20-fold boost in time-to-solution over previous methods. This crucial information will be presented at the ISC High Performance conference in Hamburg, Germany, from June 10–13, 2025.

Drug development takes pharmaceutical corporations years or perhaps billions of dollars. This time-consuming and costly process hinders early-stage research, particularly in computational chemistry that is required to forecast molecular behavior. Conventional high-fidelity simulations of large chemical reactions can take weeks or even months on conventional supercomputers due to the computational load of molecular interactions, which increases with system size.

This innovative hybrid quantum-classical system quickly overcomes these limitations and offers a promising approach to reduce these important processing bottlenecks and accelerate early-stage research, thus creating significant strategic and economic value.

This novel approach mimicked Suzuki-Miyaura cross-coupling catalysis. This chemical transformation family is essential to pharmaceutical development, notably small-molecule drug synthesis. Because of its complexity and commercial importance, the Suzuki-Miyaura reaction is an ideal example of what quantum acceleration can do.

While maintaining scientific accuracy, the study has been able to reduce the expected time of these usually time-consuming simulations from months to a few days. IonQ CEO Niccolo de Masi summed up this effect in an interview. Furthermore, months might be turned into days with computational drug development, which would change the world and save lives. According to him, this marks a sea change and the beginning of using quantum and hybrid quantum computers to deliver life-saving medications more efficiently, accurately, and rapidly.

The technological underpinning of this achievement is the convergence of cloud platforms and advanced hardware. The final response is made up of:

IonQ’s enterprise-class, state-of-the-art quantum computer is the Forte quantum processing unit (QPU). It contains thirty-six algorithmic qubits. IonQ highlights the value of its enterprise-grade hardware and accessibility through top cloud providers in order to showcase quantum-enhanced capabilities in life sciences research and development.

The NVIDIA CUDA-Q platform plays a major role in orchestrating the complex hybrid quantum-classical workflow. “The path to realizing quantum’s potential is bringing together state-of-the-art quantum and  GPU computing in hybrid workflows,” said Tim Costa, Senior Director of Quantum and CUDA-X at NVIDIA, underscoring its importance.

AWS cloud infrastructure: This includes Amazon Braket, which manages both classical and quantum resources, and AWS ParallelCluster, which provides scalable  GPU resources. “Future quantum computers will speed up certain computationally demanding processing steps as part of HPC processing pipelines, rather than replacing traditional compute,” said Eric Kessler, general manager of Amazon Braket at AWS. AstraZeneca is better able to envision how future quantum computers may speed up computational chemistry research thanks to this AWS integration.

This demonstration is the largest of its kind and the most complex chemical simulation yet performed using IonQ equipment. It shows how to effectively use quantum acceleration to get over the constraints of conventional computational chemistry, which has direct implications for activation energy analysis and drug design route optimization. The collaboration represents a “significant step towards accurately modelling activation barriers for catalysed reactions relevant to route optimising in drug development,” according to Anders Brood, Executive Director, Pharmaceutical Science, R&D, AstraZeneca.

IonQ presents this promising outcome as a proof-of-concept for a broader range of applications spanning not just drug research but also chemistry, materials science, and healthcare. It extends IonQ’s present focus on scaling realistic hybrid quantum-classical operations, which follows previous demonstrations in materials science and machine learning. The business has made a name for itself as an early adopter of cloud-based platforms, high-performance computing frameworks, and quantum technology.

Beyond high-performance computing (HPC), the project emphasizes the increasing vigor for ecosystem-level quantum applications in various sectors. Strategies like this quantum-enhanced workflow that can reduce early-stage bottlenecks are becoming increasingly relevant as pharmaceutical corporations continue to search for innovative methods to reduce the multi-year, multi-billion dollar process of bringing new medications to market.

This partnership is an illustration of how partnerships between the computing and pharmaceutical sectors are beginning to translate theoretical quantum benefits into practical and financial savings. Furthermore, pointing out that current systems, such the 36-qubit IonQ Forte, are already demonstrating minimal practical advantage, Niccolo de Masi stated his conviction that the “double exponential” potential of quantum computing might lead to much more profound changes in drug research.

This cooperation involving IonQ, AstraZeneca, AWS, and NVIDIA accelerates computational processes that were previously unattainable, enabling quantum computing to be used in drug discovery.

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The Potential of Quantum Processing Units(QPUs): The Quantum Revolution

Quantum Processing Units (QPUs) are the key concept of a brand new way of computing. The principle of a quantum computer is actually, first of all, a qubit or a quantum bit. QPUs are using qubits which are the smallest subatomic particles that can be used to represent and process huge amounts of information ¹. Unlike the classical computers, QPUs use the principles of quantum mechanics to solve complex tasks which are otherwise intractable for the fastest supercomputers.

How QPUs Work

QPUs exploit qubits through quantum logic gates following the laws of the basic principles of quantum physics and making slight adjustments ¹. Thereby, QPUs decode information in a period of time close to what would take a classical computer years to accomplish. The use of quantum algorithms gives QPUs an advantage to run code in a different manner which makes them uniquely suited for complex tasks such as large number factoring and molecular simulation.

Key Principles of Quantum Computing

Superposition: Qubits can exist in many states at the same time.

Entanglement: Qubits become linked and can then transfer information instantly.

Decoherence: Qubits are reduced to a state where the measurement can be done, which is a non-quantum state.

Interference: Qubits are in a state where they can enhance or cancel.

Advantages of QPUs

Parallel Processing: QPUs conduct multiple calculations in parallel.

Exponential Scaling: The number of qubits can grow exponentially with the number of qubits involved.

Probabilistic Computing: QPs detect probable outcomes among several alternatives thus reducing the overall computational time.

Real-World Applications

Cryptography: QPUs have enough power to crack some encryption protocols.

Optimization: QPUs show a remarkable result in resolving complicated optimization problems.

Simulation: QPUs make a correct reflection of complicated systems.

Leave aside the future, the QPUs have very high potential, but they are still in their infancy. As research continues, we may look forward to QPUs being a tool for various areas like medicine, finance, and materials science ². Computing, as we know it can be a promising adventure, and by means of QPUs, we can be part of this.

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Xanadu Achieves Scalable Gottesman–Kitaev–Preskill States

States Gottesman–Kitaev–Preskill

Xanadu leads photonic quantum computing with their development of a scalable building block for  fault-tolerant quantum computers. The achievement involves on-chip Gottesman–Kitaev–Preskill state production and was initially reported in January 2025 by Nature and summarised in June 2025. “First-of-its-kind achievement” and “key step towards scalable fault-tolerant quantum computing” describe this work.

Understanding GKP States' Importance

GKP states are error-tolerant photonic qubits. These complex quantum states consist of photons stacked in specific ways. Due to its unique structure, quantum error correcting methods may identify and fix phase shifts and photon loss. Zachary Vernon, CTO of Xanadu, calls GKP states “the optimal photonic qubit” because they enable quantum logic operations and error correction “at room temperature and using relatively straightforward, deterministic operations.” It has always been challenging to construct high-quality Gottesman–Kitaev–Preskill States on an integrated platform. This discovery advances continuous-variable quantum computing architectures by overcoming that obstacle.

GKP states provide fault-tolerant computing by using linear optics and measurement algorithms, unlike probabilistic entanglement methods that require repeated trials and complex feed-forward control. They fit well with hybrid systems because they generate quantum networks that link chips or modules or create larger cluster states for measurement-based computation.

Quantum systems' interoperability with optical fibre makes scaling easy, allowing them to be distributed among system components or data centres. This demonstration changed photonic quantum computing by taking a different approach from superconducting and trapped-ion platforms and bringing these systems closer to utility-scale quantum machine error thresholds.

Aurora: Photonic Quantum Computing Architectur

The “sub-performant scale model of a quantum computer” “Aurora” represents Xanadu's work. This system uses scalable, rack-deployed modules connected by fibre optics to incorporate all basic components. With 35 photonic devices, 84 squeezers, and 36 photon-number-resolving (PNR) detectors, Aurora provides 12 physical qubit modes each clock cycle. All system components except the cryogenic PNR detection array are operated by a single server computer and fit into four server racks.

Aurora's key technologies and their functions:

Silicon nitride waveguides feature minimal optical losses. This waveguide uses 300 mm wafers, which are common in semiconductor production. Newer chips based on Ligentec SA's 200-mm silicon-nitride waveguide architecture show potential for better squeezing and lower chip-fiber coupling losses.

The efficiency of photon-number-resolving (PNR) detectors is above 99%. In 12-mK dilution coolers, 36 transition edge sensor (TES) arrays form its base. These TES detectors cycle at 1 MHz and detect up to seven photon counts with little miscategorization error. Despite being highly effective, PNR detection efficiencies of over 99% are needed to meet the architecture's strict P1 path loss constraints.

Loss-optimized optical packaging—including accurate alignment, chip mounting, and fibre connections—was emphasised. This protects critical quantum information during routing and measurement.

The refinery array has six photonic integrated circuits (PICs) on a thin-film lithium-niobate substrate. Each refinery's two binary trees of electro-optic Mach-Zehnder modulator switches dynamically select the best output state based on PNR detection system feedforward instructions. Even though current Aurora refinery chips use probability-boosting multiplexing and Bell pair synthesis, future generations will use homodyne detectors to complete the adaptive breeding method.

Interconnects: Phase- and polarization-stabilized fiber-optical delay lines connect the refinery to QPU and refinery modules. These delays allow temporal entanglement and buffer information heralding in the cluster state.

Experiments and Results

Two large trials benchmarked Aurora's main features.

To generate a 12 × N-mode Gaussian cluster state, the system was set to send squeezed states to the QPU array. Data was collected at 1 MHz for two hours to synthesise and measure a macronode cluster state with 86.4 billion modes. Despite substantial optical losses (approximately 14 dB), the nullifier variances remained below the vacuum noise threshold, proving squeezing and cluster state entanglement.

Detecting Repetition Code Errors: This experiment showed the system's feedforward and non-Gaussian-state synthesis using low-quality GKP states. In real time, the QPU decoder assessed the system's two (foliated) repetition code checks. The decoder calculated bit values and phase error probabilities to change the measurement basis for the next time step.

Limitations and Prospects

Despite these notable examples, the “component performance gap” between existing capabilities and fault tolerance needs remains large. The main limiter of quantum state purity and coherence is optical loss. Ideal designs for fault-tolerant operation require loss budgets of about 1%, whereas the Aurora system lost 56% for heralding pathways (P1) and nearly 95% for heralded optical paths (P1 and P2).

Xanadu's future projects include:

Hardware improvements: Chip fabrication, waveguide geometry, and packaging are optimised to improve fidelity and reduce optical loss. The photonic components' insertion loss must be improved by 20-30 times (on a decibel scale).

Architectural Refinements: Testing cutting-edge hardware-level photon generation and detection rates and error mitigation measures to reduce loss and imperfection.

Integration and Scaling: combining the new GKP generation methods with Aurora's networking, error correcting protocols, and logic gates. The company believes scalable, semiconductor-compatible platforms can mass-produce, modify, and monitor error-correcting components for modular quantum computing.

Even though quantum hardware across all platforms is currently in the noisy intermediate-scale quantum (NISQ) period, Xanadu's work shows how to scale photonic quantum computers to address real applications. Fiber-optical networking, classical control electronics, and photonic-chip fabrication can scale and modularise a realistic photonic architecture. We must continuously improve optical GKP-based architectures to find the most hardware-efficient and imperfection-tolerant systems.

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Types of qubits And Applications of Quantum Processing Units

This article covers quantum processing unit applications, structure, qubit types, and more.

The “brain” of a quantum computer is a Quantum Processing Unit. This cutting-edge machine solves complicated issues with qubits and quantum physics. QPUs use qubits, which can be 0, 1, or a mix of both. Traditional computers employ binary bits. QPUs handle data differently than computers due to quantum principles like entanglement, decoherence, and interference.

QPU Structure and Function

Two key components make up a QPU:

Quantum Chip: This semiconductor base has numerous layers etched with superconducting components. These components make up physical qubits.

Control Electronics: These handle and amplify control signals, control and read qubits, and address decoherence-causing interference. They have standard CPU components for data exchange and instruction storing.

Dilution refrigerators that freeze the quantum chip to near absolute zero—colder than space—are needed for qubit coherence. Traditional computing equipment and control circuits can be stored in racks close to the refrigerator at normal temperature. The whole quantum computer system, including cryogenic systems and other classical components, may be the size of a four-door car.

Quantum logic gates in QPUs translate qubit data mathematically, unlike binary logic gates. Even though they can solve issues that classical computing cannot, QPUs are much slower than CPUs in raw computation speed. But other issue classes compute more efficiently, which can reduce calculation time.

Types of Qubit

Quantum processors’ quantum technologies vary, showing the variety of quantum computers in development. Qubits are usually made by manipulating quantum particles or building systems that approximate them. Different modalities include:

Cold, laser-controlled neutral atoms in vacuum chambers. Scaling and executing activities are their specialities.

Low-temperature  superconducting qubits  are preferred for speed and precise control. IBM QPUs employ solid-state superconducting qubits.

High-fidelity measurements and long coherence durations are possible with trapped ion qubits.

Catching an electron creates a qubit from quantum dots, tiny semiconductors. Compatible with semiconductor technology and scalable.

Photons: Light particles used in quantum communication and cryptography, notably long-distance quantum information transfer.

QPU manufacturer design direction and computing requirements often determine qubit modality. All known qubits require a lot of hardware and software for noise handling and calibration due to their extraordinary sensitivity.

Quantum Processing Unit Applications

QPUs promise advances in many vital industries and are ideally suited for unsolved problems. Important uses include:

Combinatorial optimisation challenges: These enormous issues get tougher to tackle. Neutral atom Rydberg states may solve these classification problems.

Pharmaceuticals and quantum chemistry: Accelerating medication development and chemical byproduct studies by simulating molecular and biological reactions.

Artificial Intelligence (AI) and Machine Learning (ML): Quantum algorithms may speed up Machine Learning and help AI investigate new techniques by analysing enormous volumes of classical data.

Materials Science: Studying physical matter to solve problems in solar energy, energy storage, and lighter aviation materials.

Integer factorisation can still undermine open cryptosystems.

AI and cybersecurity applications are commercialising RNG.

In quantum cryptography, new cryptographic algorithms are developed to improve data security.

Simulation of complex quantum particle systems to predict their behaviour before physical design.

Present and Future Availability

QPU development is accelerating in 2025 due to traditional computing demands. Tech giants D-Wave Systems, Google, IBM, Intel, IQM, Nvidia, QuEra, Pasqal, and Rigetti Computing are developing QPUs. IBM has achieved “quantum utility” (reliable, accurate outputs beyond brute-force classical simulations) and is pursuing “quantum advantage” (outperforming classical supercomputing).

However, serious challenges remain. Early QPUs have low qubit coherence and significant error rates due to noise. Scalability constraints limit useful uses. Software tools for building, testing, and debugging quantum algorithms can also be improved.

Commercial QPUs are appearing, but they may take time to become generally available. QPUs will likely be used only by government labs and large public cloud companies that offer quantum computing as a service due to their environmental requirements, which include powerful refrigeration systems, vacuums, and electromagnetic protection to chill qubits close to absolute zero. The QPUs’ specialised computing skills are not needed, hence they should not be integrated into cellphones or home PCs.

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QMIO Combine Hybrid High-Performance & Quantum Computing

QMIO

Computational research advanced with the invention of QMIO, a unique and tightly integrated hybrid HPC/QC system. This cooperative effort combines standard computing resources with a Quantum Processing Unit to accelerate computational kernels. By exploring synergy between classical systems and quantum computers, the program contributes to the high-performance computing innovation driven by computational advantage.

Russell Rundle, George B. Long, Gavin Dold, Jamie Friel, Álvaro C. Sánchez, and Javier Cacheiro and Andrés Gómez from Oxford Quantum Circuits, FSAS International Quantum Centre, and Galicia Supercomputing Centre recently published on QMIO development. Their book, “QMIO: A tightly integrated hybrid HPCQC system,” advances hybrid quantum-classical computation by detailing the system's hardware and software architecture and sharing insights from its construction and early functioning.

A functioning hybrid HPC and QC platform, QMIO, integrates QPUs with HPC resources. This facilitates cooperative computing and exploration. The system's complex architecture—specialized hardware, powerful software, and vital integration middleware—provides insights into its design, implementation, and operational performance.

QMIO relies on offloading computing kernels to the QPU. Using quantum algorithms to speed up jobs that standard HPC systems cannot solve is the goal of this method. Carefully distributing these kernels to the QPU improves complex simulations, speeds computing, and may reduce energy consumption. The system's ability to communicate and transfer data between the classical and quantum worlds is essential for hybrid algorithm development and quantum acceleration optimisation.

Number of components and supporting studies that made the system work. Suzuki, Kawase, Masumura, and Mitarai found Qulacs to be a fast and adaptable quantum circuit simulator. Also mentioned is mpiqulacs, its dispersed extension (Honda et al., 2023). A new programming model and runtime framework for hybrid HPC-QC infrastructures by Cao et al. (2023) makes it easy to specify and execute hybrid algorithms.

Hybrid system conception and deployment provide significant challenges. Recent advances in quantum error mitigation strategies may help reduce noise and improve quantum computation reliability. Researchers are studying symmetry verification, probabilistic error cancellation, and zero-noise extrapolation to eliminate these errors. Combining quantum and classical resources causes communication, synchronisation, and data transfer challenges. To solve these integration issues, researchers are investigating high-bandwidth interconnects, data serialisation formats, and communication protocols.

The success of QMIO and related research shows that hybrid computing systems are possible and may open the door to further development. Researchers are investigating better quantum algorithms, applications, programming models, runtime systems, and scalable and reliable quantum hardware. Computer science, engineering, and quantum physics professionals stress teamwork and multidisciplinary knowledge in hybrid computing. Designing, building, and operating these complex systems requires ongoing R&D and qualified staff.

In Conclusion

A new, strongly connected hybrid system, QMIO combines  Quantum Computing (QC)  and high-performance computing. A Quantum Processing Unit (QPU) was integrated with classical computers in this collaborative project. The system's design integrates QPUs with HPC resources for collaborative computation. The key is strategically offloading computing kernels to the QPU.

This technology uses quantum algorithms to speed up usually intractable or wasteful tasks to improve simulations, speed up processes, and maybe reduce energy usage. QMIO's hardware, software, and integration middleware simplify classical-quantum communication. Resource integration and quantum error mitigation remain difficulties, but its successful development proves that hybrid computing systems may be built. It also allows future growth.

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What Is Quantum Centric Supercomputing? And How It Works

What is Quantum centric supercomputing?

In order to develop a computing system that can tackle very complicated real-world issues, quantum centric supercomputing, a groundbreaking approach to computer science, blends quantum computing with conventional high-performance computing (HPC).

Using error mitigation and error correction methods, a quantum-centric supercomputer is a next-generation combination of a quantum computer with a classical supercomputer that produces results in real-world runtimes.

It is anticipated that in the age of quantum computing, quantum-centric supercomputing would enable scientists to make significant advances in generative AI, postquantum cryptography, machine learning, material sciences, and other areas, maybe even surpassing large-scale fully quantum systems.

A fully functional quantum-centric supercomputer integrates quantum circuitry with traditional computing resources through sophisticated middleware. The fundamental components of quantum centric supercomputing, which are based on the IBM Quantum System Two architecture, integrate quantum technology with conventional supercomputers to enhance and complement their respective capabilities.

How Quantum centric supercomputing work?

The quantum processing unit (QPU) is the central component of a quantum centric supercomputing. IBM’s QPU consists of a multilayer semiconductor chip etched with superconducting circuits and the gear that receives and outputs circuits. These circuits house the qubits that are utilized for computations as well as the gates that manipulate them. The circuits are separated into many layers of input and output wire, a layer with resonators for readout, and a layer containing the qubits. Interconnects, amplifiers, and signal-filtering components are also included in the QPU.

A superconducting capacitor connected to elements known as Josephson junctions, which function similarly to lossless, nonlinear inductors, makes up the kind of physical qubit that IBM uses. Only certain values may be assumed by the current flowing across Josephson junctions due to the superconducting nature of the system. Additionally, only two of those particular values are available due to the Josephson junctions spacing them away.

The lowest two current values zero and one, or a superposition of both zero and one are then used to encode the qubit. Programmers use quantum instructions, often referred to as gates, to couple qubits together and alter their states. These are a number of microwave waveforms that have been particularly created.

Some of the QPU components must be kept within a dilution refrigerator, which uses liquid-helium to keep them cool, in order to maintain the qubits’ proper working temperature. Classical computing hardware at normal temperature is needed for other QPU components. The QPU is then linked to runtime infrastructure, which handles results processing and error mitigation. This computer is quantum.

By enabling smooth communication between the two, middleware and hybrid cloud solutions enable the integration of quantum and classical systems. Without requiring a total redesign of present infrastructures, this hybrid technique helps guarantee that quantum processing units may be utilized efficiently within quantum computers coupled to conventional computing frameworks, optimizing their impact.

Quantum centric supercomputing use cases

Large-scale data processing might be accelerated by quantum computers, which are particularly good at tackling some challenging issues. Quantum computing may provide the key to advancements in a number of crucial fields, including material research, supply chain optimization, medication development, and climate change issues.

Pharmaceuticals: Research and development of novel, life-saving medications and medical treatments can be greatly accelerated by quantum computers that can simulate molecular behavior and biochemical interactions.

Chemistry: Quantum computers may influence medical research for the same reasons, but they may also offer previously unidentified ways to reduce hazardous or damaging chemical byproducts. Better procedures for the carbon breakdown required to tackle climate-threatening emissions or better catalysts that enable petrochemical alternatives can result from quantum computing.

Machine learning: Researchers are investigating whether some quantum algorithms would be able to see datasets in a novel way, offering a speedup for specific machine learning tasks, as interest and investment in artificial intelligence (AI) and related disciplines like machine learning increase.

Challenges Of Quantum centric supercomputing

Today’s quantum computers are scientific instruments that can execute some programs more effectively than conventional simulations, at least when modeling particular quantum systems. Nonetheless, quantum computing will continue to be beneficial for the foreseeable future when combined with current and upcoming conventional supercomputing. As a result, quantum scientists are getting ready for a time when quantum circuits will be able to assist traditional supercomputers in solving issues.

The development of the middleware that enables communication between classical and quantum computers, as well as general issues with quantum computers themselves, are the main obstacles facing quantum centric supercomputing. The following major challenges have been recognized by developers to be addressed prior to attaining quantum advantage.

Enhancing Interconnects

Millions of physical qubits are needed to create a fully functional large-scale quantum computer. However, scaling individual chips to these levels is extremely difficult due to real hardware limits. IBM is creating next-generation interconnects that can transfer quantum information between many devices as a remedy. To achieve the necessary qubits for error correction, this method offers modular scalability.

IBM intends to use proof-of-concept chips dubbed Flamingo and Crossbill, respectively, to show these novel interconnects, which are referred to as l-couples and m-couplers. Chip scaling is the responsibility of these couplers. IBM intends to use a chip known as Kookaburra to demonstrate c-couplers by the end of 2026. They are in charge of helping to fix errors.

Scaling quantum processors

Current quantum processors can only handle a small number of possible qubits, despite the fact that quantum processors based on qubits utilized in quantum computing have the potential to significantly surpass bit-based processors. IBM intends to launch a quantum system with 200 logical qubits that can execute 100 million quantum gates by 2029 as research advances, with a target of 2,000 logical qubits that can execute 1 billion gates by 2033.

Scaling quantum hardware

Qubits require massive cooling systems that can produce temperatures lower than space since, despite their power, they are also very prone to errors. In order to lower footprint, cost, and energy consumption, researchers are creating methods to scale qubits, electronics, infrastructure, and software.

Quantum error correction

Although qubit coherence is fleeting, it is essential for producing precise quantum data. One of the biggest challenges for any quantum system is decoherence, which is the process by which qubits malfunction and provide erroneous outputs. Encoding quantum information into more qubits than would otherwise be necessary is necessary for quantum error correction. IBM unveiled a revolutionary new error-correcting code in 2024 that is around ten times more effective than previous techniques. This new code paves the way for the operation of quantum circuits with a billion logic gates or more, even if error correction is still an open subject.

Quantum algorithm discovery

Two elements are necessary for quantum advantage. The first consists of feasible quantum circuits, and the second is a technique to show that, in comparison to other state-of-the-art approaches, such quantum circuits are the most effective way to tackle a quantum issue. Current quantum technologies will go from quantum usefulness to quantum advantage with the discovery of quantum algorithms.

Quantum software and middleware

In order to design, optimize, and run quantum programs, the core of quantum algorithm discovery depends on an extremely reliable and powerful software stack. By far the most used quantum software in the world is IBM’s Qiskit. Its open source SDK and related tools and services are built on Python and may be used to execute on IBM’s fleet of superconducting quantum computers as well as on systems that employ other technologies, such quantum annealing or ions trapped in magnetic fields.

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NanoQT & VeriQloud Partner On Blind Quantum Computing

The global alliance between quantum network protocol specialist VeriQloud and Japanese firm Nanofiber Quantum Technologies, Inc. (NanoQT) has taken a critical step towards safe quantum computation. This alliance seeks a hardware-integrated, scalable Blind Quantum Computing (BQC) architecture disclosed on June 16, 2025. Project support from EUREKA Globalstars-Japan Round 3 highlights its strategic importance.

Knowing Blind Quantum Computing

Blind Quantum Computing (BQC) addresses data security and privacy issues that hinder quantum computing adoption. As quantum computers become more capable and accessible through cloud services, consumers will need assurances that their private information and proprietary computing processes will be protected, even when allocated to a remote quantum processor.

A client can use a quantum computer to calculate without telling the operator the input, computation, or outcome under the BQC paradigm. This is especially important for government, healthcare, and finance companies that manage sensitive data. This NanoQT and VeriQloud project develops a BQC-compatible architecture for safe delegated computing on networked quantum computers.

An Expert Fusion: Hardware and Protocols

The NanoQT-VeriQloud partnership seamlessly combines their complimentary skills. The project benefits from NanoQT's nanofiber cavity quantum hardware. This gear is needed to interface a nanofiber quantum network. This interface is designed to enable connectivity and quantum communication for scalable quantum operations in a networked quantum system. Connecting distant quantum resources safely and efficiently requires such an interface.

In contrast, VeriQloud shares its quantum network expertise. They are crucial for implementing cryptographic methods. The BQC architecture relies on these protocols to protect client-side data privacy. Even when the client's input data is processed on a remote quantum computer, these encryption approaches keep the quantum computing details confidential. This strong cryptographic layer allows the server to compute “blind” without “seeing” the sensitive data in BQC.

Preserving Privacy Through Networking

Though theoretical, the proposed system tries to be closely related. NanoQT's nanofiber-based quantum network interface will be used with neutral-atom QPUs. In this rapidly changing hardware paradigm, neutral-atom systems are prioritised to drive privacy-preserving quantum computing. This hardware-software co-design approach focusses on scalability and deployment difficulties in secure quantum cloud services. The cooperation addresses these difficulties to produce a reliable and effective system that can manage complex quantum operations and provide an unbreakable firewall.

The “next frontier” in quantum computing, networked architectures, is being expanded by this partnership. One example is NanoQT's collaboration with US-based neutral-atom quantum computing leader QuEra Computing to investigate the integration of neutral-atom QPUs with quantum networking interfaces to create a networked, scalable quantum computing architecture. This unique cooperation, disclosed on March 4, 2025, shows how networking is becoming increasingly important for scalable, networked systems with Neutral-atom QPU capability. QuEra focusses on networking and scalability, while VeriQloud focusses on privacy-preserving quantum computing via BQC.

Strategic Funding and Future Impact

Its considerable financial backing emphasises NanoQT and VeriQloud's collaboration. Using the entire EUREKA framework, this program is sponsored by Japanese NEDO and French Bpifrance. Future technological innovation and sovereignty depend on secure quantum infrastructure, and this cross-border investment demonstrates that key governments have a common goal of achieving it.

Measureable results from the agreement should advance safe quantum computing. Along with technology prototypes, these outputs include privacy-preserving quantum computation IP. These findings are more than simply academic research since they provide the groundwork for safe quantum cloud services. By providing safe access to quantum computing power, these services could alter government, defence, healthcare, and finance sectors that handle sensitive data. This partnership shows the global nature of quantum innovation by combining complementary skills to safeguard a quantum future.

In summary

NanoQT, QuEra, and VeriQloud's recent collaborations show a quantum computing push towards safe, scalable, and interconnected quantum networks. Neutral-atom technology aids these advances. Networked architectures are prioritised to enable new operating scales and applications, such as Blind Quantum Computing, which protects privacy. These advancements represent a swift shift towards more useful and significant quantum computing solutions, together with continued advances in quantum simulation and wider industry usage.

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Pasqal Roadmap to Scalable Neutral-Atom Quantum Computing

Pasqal roadmap

Pasqal, a leader in neutral-atom quantum computing, presented its 2025 product and technology roadmap for quantum computing. From Paris, France, the corporation pledged to provide meaningful benefits now and a smooth transition to fault-tolerant systems in the future on June 12, 2025.

The roadmap has three major pillars:

Implement quantum computing quickly.

Industry-relevant quantum advantage (QA) is shown.

A faster digital path to  fault-tolerant quantum computing (FTQC)

Current Pasqal machines compute analogly using physical qubits. They are designed to switch to digital FTQC utilising the same modular, upgradeable hardware. This revolutionary architecture ensures consumers get quick quantum performance without losing long-term scalability for future breakthroughs.

Large-scale deployment: Quantum power to users Today Pasqal's proposal emphasises large-scale quantum processing unit (QPU) deployment to let clients use quantum power now. Pasqal achieved major achievements last year by installing the first neutral atom QPUs in HPC centres. Genci bought the Orion Beta computer, known as “Ruby,” in France and gave another to Forschungszentrum Jülich in Germany. These deployments introduce a new era by directly integrating enterprise-grade quantum processors into computing infrastructures.

Pasqal will be deployed in Canada, the Middle East, and Italy's CINECA HPC centre. These implementations are critical to hybrid quantum-classical workflow development. In this paradigm, QPUs, high-performance CPUs, and classical computers will work together to solve difficult problems. Pasqal is collaborating with NVIDIA and IBM to standardise QPU integration in HPC infrastructures to simplify hybrid workflow orchestration.

Quantum Advantage gives industry measurable performance

Pasqal is working to prove quantum advantage (QA), the theory that quantum computers outperform classical systems in real applications. The company is developing a 250-qubit QPU optimised for this purpose on an industry-relevant problem for a demonstration in the first half of 2026. Pasqal trapped over 1,000 neutral atoms in a quantum processor, making tangible, domain-specific quantum advances.

The QA effort targets three key algorithm development areas:

Optimisation: For complex scheduling and logistics challenges.

Quantum simulation: modelling and identifying new materials for data storage and energy.

Machine Learning: Accelerates predictive modelling and pattern recognition.

Short-term domain-specific gains are expected from neutral-atom quantum computers before digital FTQC matures. Pasqal expects its QPUs to alter pharmaceutical drug development and materials sciences in five years through quantum simulation and quantum-enhanced graph machine learning.

Building the Future with Fault-Tolerant Quantum Computing Pasqal's roadmap includes a lot of hardware technology for scalable, digital fault-tolerant quantum computing. The company wants 1,000 physical qubits by 2025 and 10,000 by 2028.

Scaling quantum computers involves increasing the number of physical qubits and logical qubits to improve their reliability. Logical qubits let computations perform slower and more accurately by integrating numerous physical qubits to substantially reduce errors. Pasqal's technology roadmap promises improved logical performance:

Start with two logical qubits in 2025.

Increasing to 20 by 2027.

By 2029, 100 high-fidelity logical qubits will exist.

In 2030, 200 logical qubits will exist.

Pasqal hopes to release Orion Gamma, the third Orion QPU platform, with over 140 physical qubits by 2025. Also expected: future generations

In 2027, Vela will have over 200 physical qubits.

Centaurus, designed for 2028 early FTQC.

Lyra should deliver strong FTQC in 2029. Pasqal's processors have more qubits and improve fidelity, repetition rate, and parallel gate operations with each iteration.

Photonic Integrated Circuits (PICs) in Pasqal's next-generation machines are crucial to its FTQC transition. This planned move follows the purchase of Canadian PIC pioneer Aeponyx. PICs should improve hardware scalability, system stability, and qubit control fidelity. PICs will improve qubit manipulation accuracy, making scaling from hundreds to thousands of qubits easier and increasing hardware platform adaptability.

Community, Open Software, and Hybrid Integration Empower the Ecosystem

A new open innovation centre, Pasqal Community, will open in 2025. Pasqal is aggressively expanding hardware availability with cloud growth and a full open-source software stack. This endeavour empowers developers, scholars, and quantum enthusiasts through performance unlocking, education support, and quantum ecosystem collaboration.

Pasqal's specific user interface and popular cloud platforms like Google Cloud Marketplace and Microsoft Azure make the Orion Alpha Machine accessible. This comprehensive strategy ensures availability and simplifies integration for many users.

According to Loïc Henriet, CEO of Pasqal, the 2025 plan aims to scale effect by growing worldwide deployments, demonstrating quantum advantage on industry difficulties, and accelerating digital quantum computing development. He praised Pasqal for leading quantum technology adoption and the sector's next phase. Another webinar with technical experts and company leaders will outline the 2025 roadmap.

Pasqal, founded in 2019 by the Institut d'Optique, creates quantum processors with organised neutral atoms in 2D and 3D arrays to address real-world problems and deliver quantum advantages. Over €140 million was raised by the company.

Pasqal's 2025 product and technology roadmap and strategy news emphasise quick value delivery, quantum advantage, and fault-tolerant quantum computing.

[govindhtech]

Quantum Art Uses CUDA-Q For Fast Logical Qubit Compilation

Quantum Art

Quantum Art, a leader in full-stack quantum computers using trapped-ionqubits and a patented scale-up architecture, announced a critical integration with NVIDIA CUDA-Q to accelerate quantum computing deployment. By optimising and synthesising logical qubits, this partnership aims to scale quantum computing for practical usage.

Quantum Art wants to help humanity by providing top-tier, scalable quantum computers for business. They use two exclusive technology pillars to provide fault-tolerant and scalable quantum computing.

Advanced Multi-Qubit gates are in the first pillar. These unusual gates in Quantum Art can implement 1,000 standard two-qubit gates in one operation. Multi-tone, multi-mode coherent control over all qubits allows code compactization by orders of magnitude. This compactization is essential for building complex quantum circuits for logical qubits.

A dynamically reconfigurable multi-core architecture is pillar two. This design allows Quantum Art to execute tens of cores in parallel, speeding up and improving quantum computations. Dynamically rearranging these cores in microseconds creates hundreds of cross-core links for true all-to-all communication. Logical qubits, which are more error-resistant than physical qubits, require dynamic reconfigurability and connectivity for their complex calculations.

The new integration combines NVIDIA CUDA-Q, an open-source hybrid quantum-classical computing platform, with Quantum Art's Logical Qubit Compiler, which uses multi-qubit gates and multi-core architecture. Developers may easily run quantum applications on QPUs, CPUs, and GPUs with this powerful combo. This relationship combines NVIDIA's multi-core orchestration and developer assistance with Quantum Art's compiler, which is naturally tailored for low circuit depth and scalable performance, to advance actual quantum use cases.

This integration should boost scalability and performance. The partnership's multi-qubit and reconfigurable multi-core operations should reduce circuit depth and improve performance. Preliminary physical layer results demonstrate improved scaling, especially N vs N² code lines, and a 25% increase in Quantum Volume circuit logarithm. Therefore, shallower circuits with significant performance improvements are developed. These advances are crucial because they can boost Quantum Volume when utilising this compiler on suitable quantum hardware platforms. Quantum Volume is essential for evaluating the platform's efficacy and scalability.

Quantum circuit creation and development at the ~200 logical qubit level are key strategic objectives of this collaboration. This scale fits new commercial use cases. A complete investigation of quantifiable performance benefits will include circuit depth, core reconfigurations, and T-gate count, which measures quantum process complexity.

As the industry moves towards commercialisation, its revolutionary multi-core design and trapped-ion qubits offer unmatched scaling potential, addressing quantum computers' top difficulty, said Quantum Art CEO Tal David, excited about the alliance. He also noted that the compiler's interaction with CUDA-Q will allow developers to scale up quantum applications.

Sam Stanwyck, NVIDIA Group Product Manager for Quantum Computing, said “The CUDA-Q platform is built to accelerate breakthroughs in quantum computing by building on the successes of AI supercomputing”. Quantum Art's integration of CUDA-Q with their compiler is a good illustration of how quantum and classical hardware are combining to improve performance.

With its multi-qubit gates, complex trapped-ion systems, and dynamically programmable multi-core architecture, Quantum Art is scaling quantum computing. These developments address the main challenge of scaling to hundreds and millions of qubits for commercial value. Integration with NVIDIA CUDA-Q is a major step towards Quantum Art's aim of commercial quantum advantage and expanding possibilities in materials discovery, logistics, and energy systems.

Quantum Art's solutions could also transform Chemistry & Materials, Machine Learning, Process Optimization, and Finance. This alliance aims to turn theoretical quantum benefits into large-scale, useful applications for several industries.

[govindhtech]

Quantware QPUs Integrate Q-CTRL’s Autonomous Calibration

Q-CTRL, a pioneer in quantum infrastructure software, and QuantWare, a leading quantum hardware supplier and VIO QPU scaling technology developer, announced a major alliance. This partnership will provide QuantWare's clients with an autonomous calibration solution, overcoming a major barrier to large-scale quantum computer deployment.

Current quantum computing hardware users struggle with the tedious, imprecise, and manual changing of QPU control parameters. Manual processing may take days, according to the notice.

Combining QuantWare's QPUs with Q-CTRL's Boulder Opal Scale Up tool will simplify this approach.

The cooperation allows QuantWare customers to “push-button tuneup” their on-premises quantum computers. This setup simplification should reduce test times from days to hours. Plug-and-play solutions enable seamless integration on-premises and in the cloud, expediting quantum error correction development by eliminating manual tuning.

Boosting System Development and Performance

QuantWare clients benefit from improved QPU performance and faster system development. Customers can create and install quantum error correcting systems faster.

Boulder Opal Scale Up lets users easily maximise QuantWare QPU performance, maximising hardware utilisation.

Contralto-A: Collaboration's Main Winner

Integration benefits QuantWare's cutting-edge QPUs like the early-access Contralto-A Quantum Error Correction QPU. Contralto-A is the next step towards quantum error-correcting systems. It was designed for distance-3 surface codes by quantum error-correction experts. This QPU uses Purcell filters and tunable couplers for high-fidelity operations.

Up to 17 premium transmon qubits are connected by 24 adjustable couplers. The Hamiltonian is optimised for Quantum Error Correction, and the qubits are “Ninja star”-arranged.

Read about quantum entanglement entropy and challenges.

Three Purcell-filtered readout, driving, and flux lines are on each qubit.

Contralto-A comes fully packaged and compatible with Ardent connectors. Options include magnetic shielding. A DC source, AWG for flux biassing for each qubit, tunable coupler, RF AWG for driving, and RF readout module for qubit readout are needed for optimal performance. With a Crescendo-S TWPA, all three feedlines read best.

QuantWare's hardware and algorithm experts teach and assist the Contralto-A for real-world applications and utility-scale development. By providing hardware-level access at every stack tier, it allows system configuration freedom and control due to its Quantum Open Architecture conformance. Contralto-A is pre-orderable for Early Access partners and will be released later this year.

VIO drives scaling

QuantWare's VIO technology scaling is also promoted by the agreement. QuantWare's VIO scaling technology makes upgrading to larger QPUs economical. It unlocks multimillion-qubit processors. As they scale systems using VIO-powered processing units, many clients will need to tune massive QPUs efficiently. Q-CTRL may enable utility-scale quantum computers with over a million qubits.

Due to VIO, devices scale quickly, hence QuantWare stressed the importance of automatic tuneup. Boulder Opal and Contralto-A work together to dramatically boost client capabilities.

Foundry Services now offers VIO to help companies make over 100 qubit devices.

Boulder Opal Scale Up: AI-Driven Automation

Q-CTRL's Boulder Opal Scale Up solution powers self-calibration. Fusing AI-driven automation with PhD-level human intelligence breaks the quantum industry bottleneck. Boulder Opal Scale Up offers a fully autonomous software solution for quick, reliable, and repeatable QPU characterisation and calibration based on the company's experience using physics-informed AI to optimise QPU performance. Q-CTR wants quantum technology to benefit as many teams as possible. They were excited to apply their skills to QuantWare's products and clientele.

This partnership was crucial to developing utility-scale quantum computers. QuantWare's QPUs simplify the user experience so customers can focus on their goals instead of manual tuning.

In addition to the Contralto-A, QuantWare offers the Contralto-D, a 21-qubit fixed coupler QPU, and the Soprano-D, a 5-qubit one. Their amplifiers include the Crescendo-S and Crescendo-E TWPAs and the VIO-176-driven TENOR-D QPU. Contralto-A works with QuantrolOx's Quantum EDGE automated tune-up software.

QuantWare and Q-CTRL have teamed to simplify quantum hardware operation to enable researchers and developers accelerate quantum error correction and large-scale, utility-scale quantum computing.