458 lines
23 KiB
BibTeX
458 lines
23 KiB
BibTeX
@article{quantum-advantage-bounds,
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title = {Information-Theoretic Bounds on Quantum Advantage in Machine
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Learning },
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author = {Huang, Hsin-Yuan and Kueng, Richard and Preskill, John},
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journal = {Phys. Rev. Lett.},
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volume = {126},
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issue = {19},
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pages = {190505},
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numpages = {7},
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year = {2021},
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month = {May},
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publisher = {American Physical Society},
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doi = {10.1103/PhysRevLett.126.190505},
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url = {https://link.aps.org/doi/10.1103/PhysRevLett.126.190505},
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}
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@article{quantum-advantage-learning,
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author = {Hsin-Yuan Huang and Michael Broughton and Jordan Cotler and Sitan
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Chen and Jerry Li and Masoud Mohseni and Hartmut Neven and Ryan
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Babbush and Richard Kueng and John Preskill and Jarrod R. McClean },
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title = {Quantum advantage in learning from experiments},
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journal = {Science},
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volume = {376},
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number = {6598},
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pages = {1182-1186},
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year = {2022},
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doi = {10.1126/science.abn7293},
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URL = {https://www.science.org/doi/abs/10.1126/science.abn7293},
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eprint = {https://www.science.org/doi/pdf/10.1126/science.abn7293},
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abstract = {Quantum technology promises to revolutionize how we learn about
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the physical world. An experiment that processes quantum data
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with a quantum computer could have substantial advantages over
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conventional experiments in which quantum states are measured and
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outcomes are processed with a classical computer. We proved that
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quantum machines could learn from exponentially fewer experiments
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than the number required by conventional experiments. This
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exponential advantage is shown for predicting properties of
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physical systems, performing quantum principal component analysis
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, and learning about physical dynamics. Furthermore, the quantum
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resources needed for achieving an exponential advantage are quite
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modest in some cases. Conducting experiments with 40
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superconducting qubits and 1300 quantum gates, we demonstrated
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that a substantial quantum advantage is possible with today’s
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quantum processors. There is considerable interest in extending
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the recent success of quantum computers in outperforming their
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conventional classical counterparts (quantum advantage) from some
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model mathematical problems to more meaningful tasks. Huang et
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al. show how manipulating multiple quantum states can provide an
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exponential advantage over classical processing of measurements
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of single-quantum states for certain learning tasks. These
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include predicting properties of physical systems, performing
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quantum principal component analysis on noisy states, and
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learning approximate models of physical dynamics (see the
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Perspective by Dunjko). In their proof-of-principle experiments
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using up to 40 qubits on a Google Sycamore quantum processor, the
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authors achieved almost four orders of magnitude of reduction in
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the required number of experiments over the best-known classical
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lower bounds. —YS Quantum-enhanced strategies can provide a
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dramatic performance boost in learning useful information from
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quantum experiments.},
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}
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@article{expressibility-and-entanglement,
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author = {Sim, Sukin and Johnson, Peter D. and Aspuru-Guzik, Alán},
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title = {Expressibility and Entangling Capability of Parameterized Quantum
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Circuits for Hybrid Quantum-Classical Algorithms},
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journal = {Advanced Quantum Technologies},
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volume = {2},
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number = {12},
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pages = {1900070},
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keywords = {quantum algorithms, quantum circuits, quantum computation},
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doi = {https://doi.org/10.1002/qute.201900070},
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url = {
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https://advanced.onlinelibrary.wiley.com/doi/abs/10.1002/qute.201900070
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},
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eprint = {
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https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/qute.201900070
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},
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abstract = {Abstract Parameterized quantum circuits (PQCs) play an essential
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role in the performance of many variational quantum algorithms.
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One challenge in implementing such algorithms is choosing an
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effective circuit that well represents the solution space while
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maintaining a low circuit depth and parameter count. To
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characterize and identify expressible, yet compact, circuits,
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several descriptors are proposed, including expressibility and
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entangling capability, that are statistically estimated from
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classical simulations. These descriptors are computed for
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different circuit structures, varying the qubit connectivity and
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selection of gates. From these simulations, circuit fragments
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that perform well with respect to the descriptors are identified.
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In particular, a substantial improvement in performance of
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two-qubit gates in a ring or all-to-all connected arrangement,
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compared to that of those on a line, is observed. Furthermore,
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improvement in both descriptors is achieved by sequences of
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controlled X-rotation gates compared to sequences of controlled
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Z-rotation gates. In addition, it is investigated how
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expressibility “saturates” with increased circuit depth, finding
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that the rate and saturated value appear to be distinguishing
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features of a PQC. While the correlation between each descriptor
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and algorithm performance remains to be investigated, methods and
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results from this study can be useful for algorithm development
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and design of experiments.},
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year = {2019},
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}
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@article{quantum-dynamics-physical-resource,
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title = {Quantum dynamics as a physical resource},
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author = {Nielsen, Michael A. and Dawson, Christopher M. and Dodd, Jennifer
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L. and Gilchrist, Alexei and Mortimer, Duncan and Osborne, Tobias
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J. and Bremner, Michael J. and Harrow, Aram W. and Hines, Andrew},
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journal = {Phys. Rev. A},
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volume = {67},
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issue = {5},
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pages = {052301},
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numpages = {19},
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year = {2003},
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month = {May},
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publisher = {American Physical Society},
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doi = {10.1103/PhysRevA.67.052301},
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url = {https://link.aps.org/doi/10.1103/PhysRevA.67.052301},
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}
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@article{scaling-variational-circuit-depth,
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doi = {10.22331/q-2020-05-28-272},
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url = {https://doi.org/10.22331/q-2020-05-28-272},
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title = {Scaling of variational quantum circuit depth for condensed matter
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systems},
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author = {Bravo-Prieto, Carlos and Lumbreras-Zarapico, Josep and Tagliacozzo
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, Luca and Latorre, Jos{\'{e}} I.},
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journal = {{Quantum}},
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issn = {2521-327X},
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publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den
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Quantenwissenschaften}},
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volume = {4},
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pages = {272},
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month = may,
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year = {2020},
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}
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@misc{akash,
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title = {Reinforcement learning-assisted quantum architecture search for
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variational quantum algorithms},
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author = {Akash Kundu},
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year = {2024},
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eprint = {2402.13754},
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archivePrefix = {arXiv},
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primaryClass = {quant-ph},
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url = {https://arxiv.org/abs/2402.13754},
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}
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@article{architecture-search,
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author = {Du, Yuxuan and Huang, Tao and You, Shan and Hsieh, Min-Hsiu and
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Tao, Dacheng},
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title = {Quantum circuit architecture search for variational quantum
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algorithms},
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journal = {npj Quantum Information},
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year = {2022},
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month = {May},
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day = {23},
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volume = {8},
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number = {1},
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pages = {62},
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abstract = {Variational quantum algorithms (VQAs) are expected to be a path
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to quantum advantages on noisy intermediate-scale quantum
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devices. However, both empirical and theoretical results exhibit
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that the deployed ansatz heavily affects the performance of VQAs
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such that an ansatz with a larger number of quantum gates enables
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a stronger expressivity, while the accumulated noise may render a
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poor trainability. To maximally improve the robustness and
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trainability of VQAs, here we devise a resource and runtime
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efficient scheme termed quantum architecture search (QAS). In
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particular, given a learning task, QAS automatically seeks a
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near-optimal ansatz (i.e., circuit architecture) to balance
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benefits and side-effects brought by adding more noisy quantum
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gates to achieve a good performance. We implement QAS on both the
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numerical simulator and real quantum hardware, via the IBM cloud,
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to accomplish data classification and quantum chemistry tasks. In
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the problems studied, numerical and experimental results show
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that QAS cannot only alleviate the influence of quantum noise and
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barren plateaus but also outperforms VQAs with pre-selected
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ansatze.},
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issn = {2056-6387},
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doi = {10.1038/s41534-022-00570-y},
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url = {https://doi.org/10.1038/s41534-022-00570-y},
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}
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@article{evolutionary-architecture-search,
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AUTHOR = {Ding, Li and Spector, Lee},
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TITLE = {Multi-Objective Evolutionary Architecture Search for Parameterized
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Quantum Circuits},
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JOURNAL = {Entropy},
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VOLUME = {25},
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YEAR = {2023},
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NUMBER = {1},
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ARTICLE-NUMBER = {93},
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URL = {https://www.mdpi.com/1099-4300/25/1/93},
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PubMedID = {36673234},
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ISSN = {1099-4300},
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ABSTRACT = {Recent work on hybrid quantum-classical machine learning systems
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has demonstrated success in utilizing parameterized quantum
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circuits (PQCs) to solve the challenging reinforcement learning
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(RL) tasks, with provable learning advantages over classical
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systems, e.g., deep neural networks. While existing work
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demonstrates and exploits the strength of PQC-based models, the
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design choices of PQC architectures and the interactions between
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different quantum circuits on learning tasks are generally
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underexplored. In this work, we introduce a Multi-objective
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Evolutionary Architecture Search framework for parameterized
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quantum circuits (MEAS-PQC), which uses a multi-objective genetic
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algorithm with quantum-specific configurations to perform
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efficient searching of optimal PQC architectures. Experimental
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results show that our method can find architectures that have
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superior learning performance on three benchmark RL tasks, and
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are also optimized for additional objectives including reductions
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in quantum noise and model size. Further analysis of patterns and
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probability distributions of quantum operations helps identify
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performance-critical design choices of hybrid quantum-classical
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learning systems.},
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DOI = {10.3390/e25010093},
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}
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@misc{generative-quantum-eigensolver,
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title = {The generative quantum eigensolver (GQE) and its application for
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ground state search},
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author = {Kouhei Nakaji and Lasse Bjørn Kristensen and Ryota Kemmoku and
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Jorge A. Campos-Gonzalez-Angulo and Mohammad Ghazi Vakili and
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Haozhe Huang and Mohsen Bagherimehrab and Christoph Gorgulla and
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FuTe Wong and Alex McCaskey and Jin-Sung Kim and Thien Nguyen and
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Pooja Rao and Qi Gao and Michihiko Sugawara and Naoki Yamamoto and
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Alán Aspuru-Guzik},
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year = {2025},
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eprint = {2401.09253},
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archivePrefix = {arXiv},
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primaryClass = {quant-ph},
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url = {https://arxiv.org/abs/2401.09253},
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}
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@inproceedings{calibration-aware-transpilation,
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author = {Ji, Yanjun and Brandhofer, Sebastian and Polian, Ilia},
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booktitle = {2022 IEEE International Conference on Quantum Computing and
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Engineering (QCE)},
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title = {Calibration-Aware Transpilation for Variational Quantum
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Optimization},
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year = {2022},
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volume = {},
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number = {},
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pages = {204-214},
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keywords = {Computers;Quantum computing;Quantum algorithm;Program
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processors;Error analysis;Logic
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gates;Calibration;Calibration-Aware;Transpilation;NISQ;QAOA;Benchmarking;Quantum
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Computing},
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doi = {10.1109/QCE53715.2022.00040},
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}
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@article{topology-driven-search,
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author = {Su, Junjian and Fan, Jiacheng and Wu, Shengyao and Li, Guanghui
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and Qin, Sujuan and Gao, Fei},
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title = {Topology-driven quantum architecture search framework},
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journal = {Science China Information Sciences},
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year = {2025},
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month = {Jul},
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day = {03},
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volume = {68},
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number = {8},
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pages = {180507},
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abstract = {The limitations of noisy intermediate-scale quantum (NISQ)
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devices have motivated the development of variational quantum
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algorithms (VQAs), which are designed to potentially achieve
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quantum advantage for specific tasks. Quantum architecture search
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(QAS) algorithms play a critical role in automating the design of
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high-performance parameterized quantum circuits (PQCs) for VQAs.
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However, existing QAS approaches struggle with large search
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spaces, leading to substantial computational overhead when
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optimizing large-scale quantum circuits. Extensive empirical
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analysis reveals that circuit topology has a greater impact on
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quantum circuit performance than gate types. Based on this
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insight, we propose the topology-driven quantum architecture
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search (TD-QAS) framework, which first identifies optimal circuit
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topologies and then fine-tunes the gate types. In the fine-tuning
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phase, the QAS inherits parameters from the topology search phase
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, eliminating the need for training from scratch. By decoupling
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the large search space into separate topology and gate-type
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components, TD-QAS avoids exploring gate configurations within
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low-performance topologies, thereby significantly reducing
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computational complexity. Numerical simulations across various
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tasks, under both noiseless and noisy conditions, validate the
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effectiveness of the TD-QAS framework. This framework advances
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standard QAS algorithms by enabling the identification of
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high-performance quantum circuits while minimizing computational
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demands. These findings indicate that TD-QAS deepens our
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understanding of VQAs and offers broad potential for the
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development of future QAS algorithms.},
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issn = {1869-1919},
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doi = {10.1007/s11432-024-4486-x},
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url = {https://doi.org/10.1007/s11432-024-4486-x},
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}
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@article{Hirsbrunner2024beyondmp,
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doi = {10.22331/q-2024-11-26-1538},
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url = {https://doi.org/10.22331/q-2024-11-26-1538},
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title = {Beyond {MP}2 initialization for unitary coupled cluster quantum
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circuits},
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author = {Hirsbrunner, Mark R. and Chamaki, Diana and Mullinax, J. Wayne and
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Tubman, Norm M.},
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journal = {{Quantum}},
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issn = {2521-327X},
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publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den
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Quantenwissenschaften}},
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volume = {8},
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pages = {1538},
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month = nov,
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year = {2024},
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}
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@misc{liu2025haqgnnhardwareawarequantumkernel,
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title = {HaQGNN: Hardware-Aware Quantum Kernel Design Based on Graph Neural
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Networks},
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author = {Yuxiang Liu and Fanxu Meng and Lu Wang and Yi Hu and Sixuan Li and
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Xutao Yu and Zaichen Zhang},
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year = {2025},
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eprint = {2506.21161},
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archivePrefix = {arXiv},
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primaryClass = {quant-ph},
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url = {https://arxiv.org/abs/2506.21161},
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}
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@article{training-free,
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title = {Training-Free Quantum Architecture Search},
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volume = {38},
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url = {https://ojs.aaai.org/index.php/AAAI/article/view/29135},
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DOI = {10.1609/aaai.v38i11.29135},
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abstractNote = {Variational quantum algorithm (VQA) derives advantages from
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its error resilience and high flexibility in quantum resource
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requirements, rendering it broadly applicable in the noisy
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intermediate-scale quantum era. As the performance of VQA
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highly relies on the structure of the parameterized quantum
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circuit, it is worthwhile to propose quantum architecture
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search (QAS) algorithms to automatically search for
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high-performance circuits. Nevertheless, existing QAS methods
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are time-consuming, requiring circuit training to assess
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circuit performance. This study pioneers training-free QAS by
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utilizing two training-free proxies to rank quantum circuits,
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in place of the expensive circuit training employed in
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conventional QAS. Taking into account the precision and
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computational overhead of the path-based and
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expressibility-based proxies, we devise a two-stage
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progressive training-free QAS (TF-QAS). Initially, directed
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acyclic graphs (DAGs) are employed for circuit representation
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, and a zero-cost proxy based on the number of paths in the
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DAG is designed to filter out a substantial portion of
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unpromising circuits. Subsequently, an expressibility-based
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proxy, finely reflecting circuit performance, is employed to
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identify high-performance circuits from the remaining
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candidates. These proxies evaluate circuit performance
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without circuit training, resulting in a remarkable reduction
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in computational cost compared to current training-based QAS
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methods. Simulations on three VQE tasks demonstrate that
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TF-QAS achieves a substantial enhancement of sampling
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efficiency ranging from 5 to 57 times compared to
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state-of-the-art QAS, while also being 6 to 17 times faster.},
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number = {11},
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journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
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author = {He, Zhimin and Deng, Maijie and Zheng, Shenggen and Li, Lvzhou and
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Situ, Haozhen},
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year = {2024},
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month = {Mar.},
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pages = {12430-12438},
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}
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@article{npqas,
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doi = {10.1088/2632-2153/ac28dd},
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url = {https://doi.org/10.1088/2632-2153/ac28dd},
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year = {2021},
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month = {oct},
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publisher = {IOP Publishing},
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volume = {2},
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number = {4},
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pages = {045027},
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author = {Zhang, Shi-Xin and Hsieh, Chang-Yu and Zhang, Shengyu and Yao,
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Hong},
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title = {Neural predictor based quantum architecture search},
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journal = {Machine Learning: Science and Technology},
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abstract = {Variational quantum algorithms (VQAs) are widely speculated to
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deliver quantum advantages for practical problems under the
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quantum–classical hybrid computational paradigm in the near term.
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Both theoretical and practical developments of VQAs share many
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similarities with those of deep learning. For instance, a key
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component of VQAs is the design of task-dependent parameterized
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quantum circuits (PQCs) as in the case of designing a good neural
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architecture in deep learning. Partly inspired by the recent
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success of AutoML and neural architecture search (NAS), quantum
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architecture search (QAS) is a collection of methods devised to
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engineer an optimal task-specific PQC. It has been proven that
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QAS-designed VQAs can outperform expert-crafted VQAs in various
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scenarios. In this work, we propose to use a neural network based
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predictor as the evaluation policy for QAS. We demonstrate a
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neural predictor guided QAS can discover powerful quantum circuit
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ansatz, yielding state-of-the-art results for various examples
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from quantum simulation and quantum machine learning. Notably,
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neural predictor guided QAS provides a better solution than that
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by the random-search baseline while using an order of magnitude
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less of circuit evaluations. Moreover, the predictor for QAS as
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well as the optimal ansatz found by QAS can both be transferred
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and generalized to address similar problems.},
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}
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@article{hea-kandala,
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author = {Kandala, Abhinav and Mezzacapo, Antonio and Temme, Kristan and
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Takita, Maika and Brink, Markus and Chow, Jerry M. and Gambetta,
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Jay M.},
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title = {Hardware-efficient variational quantum eigensolver for small
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molecules and quantum magnets},
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journal = {Nature},
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year = {2017},
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month = {Sep},
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day = {01},
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volume = {549},
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number = {7671},
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pages = {242-246},
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abstract = {The ground-state energy of small molecules is determined
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efficiently using six qubits of a superconducting quantum
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processor.},
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issn = {1476-4687},
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doi = {10.1038/nature23879},
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url = {https://doi.org/10.1038/nature23879},
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}
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@article{tensorcircuit,
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||
doi = {10.22331/q-2023-02-02-912},
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url = {https://doi.org/10.22331/q-2023-02-02-912},
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title = {Tensor{C}ircuit: a {Q}uantum {S}oftware {F}ramework for the {NISQ}
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{E}ra},
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author = {Zhang, Shi-Xin and Allcock, Jonathan and Wan, Zhou-Quan and Liu,
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Shuo and Sun, Jiace and Yu, Hao and Yang, Xing-Han and Qiu,
|
||
Jiezhong and Ye, Zhaofeng and Chen, Yu-Qin and Lee, Chee-Kong and
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||
Zheng, Yi-Cong and Jian, Shao-Kai and Yao, Hong and Hsieh, Chang-Yu
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and Zhang, Shengyu},
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journal = {{Quantum}},
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issn = {2521-327X},
|
||
publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den
|
||
Quantenwissenschaften}},
|
||
volume = {7},
|
||
pages = {912},
|
||
month = feb,
|
||
year = {2023},
|
||
}
|
||
|
||
@misc{genetic-expressibility,
|
||
title = {Genetic optimization of ansatz expressibility for enhanced
|
||
variational quantum algorithm performance},
|
||
author = {Manish Mallapur and Ronit Raj and Ankur Raina},
|
||
year = {2025},
|
||
eprint = {2509.05804},
|
||
archivePrefix = {arXiv},
|
||
primaryClass = {quant-ph},
|
||
url = {https://arxiv.org/abs/2509.05804},
|
||
}
|