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[govindhtech]

OQC Sets 2034 Goal for 50,000 Logical Qubits In Quantum Plan

Oxford Quantum Circuits (OQC), a UK quantum computing company, announced its ambitious fault-tolerant quantum computer roadmap. OQC leads the global effort to build commercial quantum machines.

Vision and Milestones of OQC

OQC is a bold quantum computing vision with explicit logical qubit goals. Businesses aim to:

200 logic qubits by 2028: Quantum computers may revolutionise essential applications including vulnerability analysis, fraud detection, arbitrage, and cyber threat identification. OQC predicts that by 2028, smartphones with this capabilities will surpass supercomputers on certain workloads.

By 2034, 50,000 logical qubits According to other quantum computing roadmaps, this objective is over ten times the highest, making it extremely ambitious. This size is expected to boost quantum computer applications including decryption, drug discovery, and quantum chemistry. Gerald Mullally, OQC's interim CEO, calls this initiative a “landmark for quantum computing, in the UK and globally,” indicating that quantum computing is “closer than many realise” to changing lives. He stresses that enterprises, notably financial and national security firms, must prepare for a “quantum-transformed world”.

Transfer to the “Logical Era” and OQC's Main Advantage The shift from the “physical era” to the “logical era” of quantum computing is central to OQC's roadmap.

Physical qubits are noisy and defective, requiring error correction in the “physical era”.

A quantum computer's capabilities depend on the number of error-corrected logical qubits in the “logical era”. Physical qubits are fragile and error-prone, hence logical qubits are needed to build successful quantum computers.

Oxford Quantum Circuits' patented technology provides them an edge in this move. Their device uses 10 times fewer physical qubits than current approaches to generate each error-corrected logical qubit. This shows that OQC's technique uses fewer than 100 physical qubits per logical gate, while others can use up to 1,000. They scale better due to their “resource ratio” efficiency.

Exclusive Technology: 3D Superconducting Transmon Circuits

OQC's technology relies on Oxford University superconducting transistor circuits. The 3D architecture is unique to their design. This 3D architecture has performance and scaling advantages:

Easy control and readout: Making qubit manipulation and reading easier, which is difficult.

By reducing qubit interactions, reduced crosstalk preserves quantum coherence and reduces mistakes in larger arrays.

OQC qubit architecture detects faults and their locations. With location data, errors can be reduced. Their design allows them to identify energetic qubit states degrading to less energetic ones, the main source of architecture mistakes.

In addition to architectural design, OQC improves physical error rates. They intend to lower these rates to less than 0.1% by carefully tuning qubits to reduce errors and improving chip materials to extend qubit coherence.

Their qubit gates' accuracy and speed demonstrate the technology's capability. In under 25 ns, OQC's two-qubit gate achieves 99.8% fidelity. This makes it one of the most precise and fast gates ever seen. Scaling quantum machines for economic benefits and efficiently performing more complex algorithms requires rapid gate speeds.

Leadership, Funding, and Strategic Partnerships

OQC's ambitious ambition relies on strategic connections and ongoing fundraising.

They partner with Riverlane, which develops quantum computer fault-tolerant algorithms. Riverlane CEO Steve Brierley called OQC's strategy a “bold vision” and “clear statement of intent” that places the UK at the forefront of quantum computing.

Organisational leadership has changed recently. Gerald Mullally replaced inventor Ilana Wisby as interim CEO last year. In April, Jack Boyer became board chairman.

A successful Series A investment round in 2022 raised £38 million for OQC, the biggest for a UK quantum computing business. Series B fundraising, estimated at $100 million, is underway. Backed by Oxford Science Enterprises (OSE), University of Tokyo Edge Capital Partners (UTEC), Lansdowne Partners, and OTIF, SBI Investment in Japan is leading this round.

As part of its global expansion, OQC will install its first quantum computer in New York City alongside a data centre partner later this year. They signed their first quantum computing co-location data centre arrangement.

OQC's roadmap also includes an Application Optimised Compute strategy that designs quantum computing systems for applications where quantum technology has a clear advantage. This strategic goal ensures that their ideas immediately benefit businesses in national security and financial services. The sources briefly mention Google, IBM, Rigetti, and IQM in Finland, but OQC claims their 50,000 logical qubit goal is better than other roadmaps.

[reallytoosublime]

With the power to create unbreakable encryption, supercharge the development of AI, and radically expedite the development of drug treatments, quantum technology will revolutionize our world. In this video, we're diving deep into the world of the power of quantum encryption.

Quantum encryption, a groundbreaking advancement in the realm of cryptography and data security, has unveiled a new era of impregnable communication and data protection. This revolutionary technology harnesses the bewildering principles of quantum mechanics to enable the creation of unbreakable codes and shield sensitive information from the ever-looming threats of cyberattacks and surveillance.

Traditional encryption methods rely on complex mathematical algorithms to encode data, requiring vast computational power to crack these codes. In contrast, quantum encryption leverages the peculiar properties of quantum particles, such as photons, to establish an unbreakable link between the sender and the receiver. This link, often referred to as a quantum key distribution, is based on the principle of quantum entanglement, where the states of two particles become intertwined in such a way that any change in one particle instantaneously affects the other, regardless of the distance separating them.

The emergence of quantum encryption marks a watershed moment in the ongoing battle between information security and cyber threats. By harnessing the mystifying behaviors of quantum particles, this technology promises an era where sensitive data can be communicated and stored with unprecedented levels of security. As researchers continue to refine its implementation and address its challenges, quantum encryption holds the potential to revolutionize the way we safeguard our digital world.

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The Mind-Blowing Power of Quantum Encryption Revealed

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[govindhtech]

New Python Package And Quantum Machine Learning Models

Combining machine learning and quantum computing,  quantum machine learning (QML) is an interdisciplinary field that is rapidly expanding. Studying how machine learning can be applied to quantum problems and how quantum systems might enhance machine learning is fascinating. Python is vital in this business because to its robust libraries and frameworks.

Introduction to Quantum Machine Learning and Python

Machine learning or quantum computing expert to learn QML. Quantum computing, which began in physics research, is now available to high school students as software. Math and linear algebra are the key requirements, along with basic Python. Trigonometry, vectors, matrices, polar and Cartesian coordinate systems, complex numbers, functions, gradients, eigenvalues, eigenvectors, and linear combinations are important math concepts. Although a basic understanding is sufficient, understanding qubit representation and manipulation requires these mathematical building blocks.

Python underpins numerous prominent classical and quantum machine learning tools and frameworks, including PyTorch, scikit-learn, and PennyLane. Free online courses or, if you've coded before, grammar videos, cheat sheets, and little projects are good ways to learn Python. QML benefits from NumPy, a popular Python scientific computing library.

After mastering these basics, you can study QML's three pillars: optimisation, machine learning, and quantum computing.

Optimisation is crucial and often involves minimising a “cost function” through progressive “cost landscape” modifications. Optimisation methods use gradient, which shows a function's steepest change, to find the lowest cost point.

Machine learning allows computers to recognise patterns in data and extrapolate them to new data without programming. This may involve training a model on a dataset, optimising a cost function, then testing it on a new dataset to ensure broad trends. The correct prediction rate or squared distance between model output and label, which is useful for gradient-based optimisation due to its continuity, can be used to measure classification progress.

Quantum computing QML tasks often use neural networks, a key machine learning concept. They are trained using backpropagation to estimate the gradient of the cost function with respect to the weights and have nodes and weighted edges that process data from inputs to outputs. Besides picture classification, machine learning tasks include regression, clustering, and reinforcement learning.

Physical quantum systems and their special characteristics are used in quantum computing to perform calculations. Quantum computers employ qubits, such as photons, superconducting qubits, or trapped ions, in contrast to classical computers. Qubits, which are complex-valued unit vectors or their linear combinations, are the building blocks of quantum information.

The idea of superposition, in which a qubit might be 0 or 1 like a spinning coin, is crucial. Entanglement and interference are also used in computation. Qubit gates, which are similar to classical logic gates, can superpose, entangle, and change measurement probabilities. These processes are usually depicted as a quantum circuit with gates and qubit wires. The final measurement compresses superpositions into classical states.

Quantum machine learning Python packages: PennyLane and Beyond

Combining these components makes Python packages crucial. PennyLane, a cross-platform Python quantum computer programming package with differentiability, is an example. This makes writing and running quantum computing algorithms easier and allows customers to use quantum computers from multiple manufacturers.

The following steps are typical for PennyLane QML program development:

Explain a device: State its quantum device type (e.g., ‘default.qubit’ simulator) and how many qubits (wires) it needs.

Define your quantum circuit (QNode): Write a Python function that performs the quantum circuit and returns a measurement using parameters.

Describe optional pre-/postprocessing: Hybrid models often use preprocessing or postprocessing methods like simple additions or complex neural networks.

Define cost function: Your QNode output and any traditional pre/postprocessing are used to minimise this Python function during training.

Execute optimisation: Choose an optimiser (PennyLane offers many).

Determine step size.

Quantum circuit parameters should be estimated beforehand. Repeat a set number of times to lower costs and adjust parameters.

Appreciate your results: Print or graph the optimisation result to see if the model found the data pattern.

Training a quantum circuit to replicate a sine function shows how to train a quantum model to recognise patterns.Outside PennyLane, specialised Python packages are being created. A new Python library that extends PennyLane's capabilities was designed to simplify Fourier model analysis and training for quantum machine learning models. This program, detailed in “QML Essentials A framework for working with Quantum Fourier Models” by Melvin Strobl, Maja Franz, Eileen Kuehn, Wolfgang Mauerer, and Achim Streit, provides strong analytical tools to understand QML model behaviour and maximise performance.

The main features

Main characteristics of this new package:

Noise addition: By merging different noise models, it can replicate genuine quantum hardware conditions, helping researchers test algorithm resilience and create noise-resistant circuits.

Circuit parameter initialisation methods: The package offers several approaches that can affect training and model quality.

Expression and entanglement calculations: These assess a model's learning and generalisation to new inputs. Expressibility is a circuit's ability to match any target function, while entanglement measures quantum interactions.

Fourier spectrum calculations: It uses two methods to calculate a quantum circuit's Fourier spectrum: an analytical trigonometric polynomial expansion method and the computationally efficient Fast Fourier Transform. This reveals the circuit's core dynamics and capabilities, revealing optimisation options.

Because the package is modular, the quantum machine learning community may simply add new features and encourage code reuse and collaboration. The development team values community feedback and strives towards improvement.

A new Python library, LazyQML, benchmarks and compares many QML models based on architectures and ansatzes from the literature. The conference paper LazyQML addresses the lack of a clear and systematic framework for comparing QML models due to the rapid expansion of quantum computing and the rapidly evolving QML frameworks like Qiskit and PennyLane.

In conclusion, Python libraries like PennyLane make QML accessible by defining quantum circuits, integrating them into machine learning algorithms, and optimising. Dedicated benchmarking packages like LazyQML and PennyLane's Fourier model extension improve the capacity to analyse, train, and compare complex QML models.

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