Queen Mary University of London builds quantum modules like computer components

Researchers from Queen Mary University of London, Imperial College and University of Oxford have unveiled Clavina, a new modular photonic quantum computing architecture capable of combining both linear and nonlinear quantum operations within a single system. Published in Nature Photonics, this development addresses a longstanding challenge in building universal photonic quantum computers, which have previously struggled to incorporate essential nonlinear operations.

The flexible design allows for specialized quantum modules to be added or removed as required, mirroring the component-based design of conventional computers. “Photonic quantum computing has enormous potential,” said Shang Yu, first author at Imperial, “but one of its greatest limitations has been the lack of a practical way to combine scalable optical circuits with the nonlinear operations required for universal quantum computing.”

Clavina Architecture Integrates Linear and Nonlinear Photonic Operations

This achievement, detailed in Nature Photonics, addresses longstanding limitations preventing photonic quantum computers from reaching their full potential, as existing designs struggled to incorporate the necessary nonlinear capabilities for advanced algorithms. This flexibility enables a broader range of quantum computing tasks to be performed on a single platform, eliminating the need for separate, purpose-built experimental setups. The team demonstrated several advanced applications using this architecture, including large-scale quantum simulations and the generation of quantum states crucial for future error correction, calculations previously impractical with existing photonic hardware.

Experiments underpinning these demonstrations were conducted in the laboratory of Professor Ian Walmsley and Dr. Raj B. Patel at Imperial. Theoretical work led by Dr. Jinzhao Sun of Queen Mary University of London, in collaboration with Professors Vlatko Vedral from Oxford and Myungshik Kim and Roberto Bondesan from Imperial, was instrumental in the development of Clavina.

Specifically, they established the theoretical framework for quantum simulation of the Bose-Hubbard model, a key tool for understanding interacting quantum particles, and quasi-deterministic breeding of Gottesman-Kitaev-Preskill states, a new approach to universal quantum computing. This work demonstrates a flexible architecture that brings those capabilities together, creating a platform that can be adapted for many different quantum applications. The researchers emphasize that the modular design facilitates the incorporation of future technologies without requiring a complete system overhaul, allowing the platform to evolve alongside advancements in the field.

While fully fault-tolerant quantum computers remain a long-term objective, this research represents a step towards scalable quantum technologies with the potential to revolutionize materials science, chemistry, optimization, and secure communications. The international collaboration included partners from the University of Hong Kong, further solidifying the global effort to advance quantum computing capabilities.

Photonic quantum computing has enormous potential, but one of its greatest limitations has been the lack of a practical way to combine scalable optical circuits with the nonlinear operations required for universal quantum computing.

Dr. Shang Yu, first author at Imperial
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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