Quantum News Briefs August 9 opens with a summary of Christopher Savoie’s Forbes article “RSA’s ‘Quantum-Proof Successor May Not Be Safe for Long”, followed by a look at Quantum Exponential’s Investment in QLM Technology Ltd. Then we share the announcement that “Oxford Instruments Nano-Science Wins Bid for Quantum Sensing for the Hidden Sector”, and MORE on quantum machine learning at LCHb/Cern with even more on quantum sensing from Friedrich-Alexander-Universität Erlangen-Nürnberg.
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RSA’s ‘Quantum-Proof Successor May Not Be Safe for Long’
You might want to sit down for this: There’s no guarantee that any of the new encryption schemes will be able to resist a quantum attack. They may not even be able to resist a classical computing attack. One of the leading PQC candidates, Rainbow, was recently broken by a laptop over a weekend, and another recent paper compromised a different leading family of PQC protocols.
A big hole in NIST’s approach is that they are currently considering only encryption protocols that rely on mathematical problems that don’t have provably efficient classical or quantum solutions. This approach ignores heuristic algorithms, which don’t have theoretically rigorous proof of their efficiency. However, they could still compromise the security of post-quantum cryptographic methods.
In 2018, three researchers at my company, Zapata Computing, got together over a weekend and developed a heuristic algorithm known as variational quantum factoring (VQF) that could factor six-digit numbers using the quantum devices available at the time. If you extrapolate this research, it would be possible to factor a 2048-bit RSA key using only 6,000 high-quality physical qubits—orders of magnitude fewer than would be required to run Shor’s algorithm.
Savoie’s advice, “As an enterprise, the last thing you want is to invest millions of dollars migrating to a new PQC system, only to have that PQC system compromised a few years later. Since we have no way of knowing which PQC schemes will be secure in the long term, the best approach is to stay flexible.” Click here to read original. full-length article.
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Quantum Exponential Invests in QLM Technology Ltd
The funding round was led by Schlumberger Oilfield UK Limited (‘Schlumberger’) and included new investment from existing investors Green Angel Syndicate, Enterprise 100 Syndicate, the Development Bank of Wales, Newable, BritBots, and British Private Equity Club.The funding will be used to further develop QLM’s Quantum Gas Imaging Lidar technology and the new strategic collaboration with Schlumberger will help to introduce and distribute QLM’s quantum cameras across the global oil and gas market
Following the Investment, Quantum Exponential will hold 1,203,208 B Ordinary Shares at a price of £0.374 in QLM representing 1.6% of QLM’s fully diluted share capital.
Read IQT-News: QLM’s LiDAR sensing for greenhouse gas draws Series A funds, Schlumberger partnership
Commenting on the investment, CEO of Quantum Exponential Group, Steven Metcalfe said: “QLM is our fourth investment into the Quantum sector and brings with it further diversification to the Company’s investments. With QLM’s focus being on the detection and prevention of leaks from the oil & gas industry it contributes to our efforts to create a well-rounded portfolio of promising companies using quantum technology in different sectors.
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Oxford Instruments Nano-Science Wins Bid for Quantum Sensing for the Hidden Sector (QSHS) Project with Proteox
Oxford Instruments recently announced it has won the bid to provide the Quantum Sensing for the Hidden Sector (QSHS) project with Proteox, its next-generation Cryofree® dilution refrigerator and leading magnet technology. The QSHS project is led by scientists at the University of Sheffield and involves the Universities of Cambridge, Lancaster, Liverpool, Oxford, Royal Holloway and University College London as well as the National Physical Laboratory. Quantum News Briefs summarizes; read complete announcement here.
Funded by the Science and Technology Facilities Council, as part of UK Research and Innovation and the Quantum Technology for Fundamental Physics programme, the project is the largest UK effort in hidden sector physics to date, and involves scientists from a range of disciplines within physics. Oxford Instruments NanoScience will install its system and technology in the middle of next year, within a newly refurbished laboratory at the University of Sheffield.
“The QSHS project re-enforces the UK’s leading role globally within quantum sensing,” states Stuart Woods, Managing Director of Oxford Instruments NanoScience. “And as such at Oxford Instruments NanoScience, we’re thrilled to be supporting the University Of Sheffield and look forward to supporting and recognising these talented physicists in their future research.”
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First studies with Quantum Machine Learning at LHCb
The leveraging of Machine Learning techniques is ubiquitous in analysis in CERN/Large Hadron Collider beauty (LHCb). Given the rapid progress of quantum computers and quantum technologies, it is natural to start investigating if and how quantum algorithms can be executed on such new hardware, and whether the LHCb particle physics use-cases can benefit from the new technology and paradigm that is Quantum Computing.
The study “Quantum Machine Learning for b-jet charge identification” was carried out based on a sample of simulated b-quark initiated jets. The performance of a so-called Variational Quantum Classifier, based on two different quantum circuits, was compared with the performance obtained with a Deep Neural Network (DNN), a modern, classical (i.e., non-quantum) and powerful type of artificial intelligence algorithm. The performance is evaluated on a quantum simulator as the quantum hardware available today is still in its early stage, even though tests on real hardware are currently under development.
The results compared to those obtained with a classical DNN showed that the DNN is performing slightly better than the QML algorithms, the difference being small.
The paper demonstrates that the QML method reaches optimal performance with a lower number of events, which helps in reducing the resources usage which will become a key point at LHCb with the amount of data collected in future years. However, when a large number of features is employed, the DNN performs better than QML algorithms. Improvements are expected when more performant quantum hardware will become available.
Dr. Eduardo Rodrigues says that “this paper demonstrated, for the first time, that QML can be the used with success in LHCb data analysis.” As physicists gain experience with Quantum Computing, drastic improvements in hardware and computing technology are to be expected given the worldwide interest and investment in Quantum Computing.
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FAU ‘s New Lighthouse Quantum Measurement & Control for Enablement of Quantum Computing & Quantum Sensing
QuMeCo bundles the unique expertise of FAU in the area of the physics of light and matter with its expertise in electrical engineering, as reflected in the aims of the project. One of its goals is to lay the foundations for the next generation of superconducting quantum computers.
The researchers also hope to optimize quantum control and error correction using machine learning and artificial neural networks.
A particular challenge for quantum computing is posed by electronic control systems. The consortium aims to develop microwave circuits that are located as close as possible to the quantum chips. Eichler: “With experimental set-ups such as these, we at FAU will have a decisive influence on furthering interdisciplinary research at the interface between electrical engineering and physics.”
The third focus is on quantum sensors and imagery. The researchers are experimenting with novel quantum light sources and detectors, and using the special characteristics of entangled photons to investigate new technologies. They are also using color centers as highly sensitive quantum sensors to depict electrical and magnetic fields and electro-chemical and photo-chemical reactions of molecules at an entirely new level of optical resolution.. A further potential application is optimizing electrolytes or ionic fluids in order to increase the efficiency and lifespan of battery cells.
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Sandra K. Helsel, Ph.D. has been researching and reporting on frontier technologies since 1990. She has her Ph.D. from the University of Arizona.