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Physical review. A/Physical review, A is a peer-reviewed journal published by American Physical Society, covering Quantum Information and Cryptography, Cold Atom Physics and Bose-Einstein Condensates, Quantum Computing Algorithms and Architecture.
| Publisher | American Physical Society |
|---|---|
| Reference style | Numbered (APS) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, Physical review. A/Physical review, A 12, 45 (2023).
Formats any DOI in Physical review. A/Physical review, A style. No sign-up. |
| Publishes research in | Quantum Information and Cryptography Cold Atom Physics and Bose-Einstein Condensates Quantum Computing Algorithms and Architecture Quantum Mechanics and Applications Quantum optics and atomic interactions |
| ISSN | 2469-9926 |
| Citation impact (2-yr) | 2.75 |
| h-index | 146 |
| i10-index | 11,934 |
| Total citations | 417,661 |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | journals.aps.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
We propose a classical-quantum hybrid algorithm for machine learning on near-term quantum processors, which we call quantum circuit learning. A quantum circuit driven by our framework learns a given task by tuning parameters implemented on it. The iterative optimization of the parameters allows us to circumvent the high-depth circuit. Theoretical investigation shows that a quantum…
An important application for near-term quantum computing lies in optimization tasks, with applications ranging from quantum chemistry and drug discovery to machine learning. In many settings, most prominently in so-called parametrized or variational algorithms, the objective function is a result of hybrid quantum-classical processing. To optimize the objective, it is useful to have access to…
A machine learning design is developed to train a quantum circuit specialized in solving a classification problem. In addition to discussing the training method and effect of noise, it is shown that the circuits perform reasonably well on classical benchmarks.
We introduce a single-number metric, quantum volume, that can be measured using a concrete protocol on near-term quantum computers of modest size ($n\ensuremath{\lesssim}50$), and measure it on several state-of-the-art transmon devices, finding values as high as 16. The quantum volume is linked to system error rates, and is empirically reduced by uncontrolled interactions within the…
Quantum computers can be used for supervised learning by treating parametrized quantum circuits as models that map data inputs to predictions. While a lot of work has been done to investigate the practical implications of this approach, many important theoretical properties of these models remain unknown. Here, we investigate how the strategy with which data…
