280 lines
14 KiB
BibTeX
280 lines
14 KiB
BibTeX
@article{quantum-advantage-bounds,
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title = {Information-Theoretic Bounds on Quantum Advantage in Machine Learning
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},
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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 with
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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 that
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a substantial quantum advantage is possible with today’s quantum
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processors. There is considerable interest in extending the recent
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success of quantum computers in outperforming their conventional
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classical counterparts (quantum advantage) from some model
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mathematical problems to more meaningful tasks. Huang et al. show
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how manipulating multiple quantum states can provide an exponential
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advantage over classical processing of measurements of
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single-quantum states for certain learning tasks. These include
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predicting properties of physical systems, performing quantum
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principal component analysis on noisy states, and learning
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approximate models of physical dynamics (see the Perspective by
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Dunjko). In their proof-of-principle experiments using up to 40
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qubits on a Google Sycamore quantum processor, the authors achieved
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almost four orders of magnitude of reduction in the required number
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of experiments over the best-known classical lower bounds. —YS
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Quantum-enhanced strategies can provide a dramatic performance
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boost in learning useful information from 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 = {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. One
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challenge in implementing such algorithms is choosing an effective
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circuit that well represents the solution space while maintaining a
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low circuit depth and parameter count. To characterize and identify
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expressible, yet compact, circuits, several descriptors are
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proposed, including expressibility and entangling capability, that
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are statistically estimated from classical simulations. These
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descriptors are computed for different circuit structures, varying
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the qubit connectivity and selection of gates. From these
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simulations, circuit fragments that perform well with respect to
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the descriptors are identified. In particular, a substantial
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improvement in performance of two-qubit gates in a ring or
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all-to-all connected arrangement, compared to that of those on a
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line, is observed. Furthermore, improvement in both descriptors is
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achieved by sequences of controlled X-rotation gates compared to
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sequences of controlled Z-rotation gates. In addition, it is
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investigated how expressibility “saturates” with increased circuit
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depth, finding that the rate and saturated value appear to be
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distinguishing features of a PQC. While the correlation between
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each descriptor and algorithm performance remains to be
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investigated, methods and results from this study can be useful for
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algorithm development 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 L.
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and Gilchrist, Alexei and Mortimer, Duncan and Osborne, Tobias J. and
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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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@article{architecture-search,
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author = {Du, Yuxuan and Huang, Tao and You, Shan and Hsieh, Min-Hsiu and Tao,
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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 to
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quantum advantages on noisy intermediate-scale quantum devices.
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However, both empirical and theoretical results exhibit that the
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deployed ansatz heavily affects the performance of VQAs such that
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an ansatz with a larger number of quantum gates enables a stronger
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expressivity, while the accumulated noise may render a poor
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trainability. To maximally improve the robustness and trainability
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of VQAs, here we devise a resource and runtime efficient scheme
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termed quantum architecture search (QAS). In particular, given a
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learning task, QAS automatically seeks a near-optimal ansatz (i.e.,
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circuit architecture) to balance benefits and side-effects brought
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by adding more noisy quantum gates to achieve a good performance.
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We implement QAS on both the numerical simulator and real quantum
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hardware, via the IBM cloud, to accomplish data classification and
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quantum chemistry tasks. In the problems studied, numerical and
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experimental results show that QAS cannot only alleviate the
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influence of quantum noise and barren plateaus but also outperforms
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VQAs with pre-selected 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 efficient
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searching of optimal PQC architectures. Experimental results show
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that our method can find architectures that have superior learning
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performance on three benchmark RL tasks, and are also optimized for
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additional objectives including reductions in quantum noise and
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model size. Further analysis of patterns and probability
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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 Jorge
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A. Campos-Gonzalez-Angulo and Mohammad Ghazi Vakili and Haozhe Huang
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and Mohsen Bagherimehrab and Christoph Gorgulla and FuTe Wong and
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Alex McCaskey and Jin-Sung Kim and Thien Nguyen and Pooja Rao and Qi
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Gao and Michihiko Sugawara and Naoki Yamamoto and 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 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 and
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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) devices
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have motivated the development of variational quantum algorithms
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(VQAs), which are designed to potentially achieve quantum advantage
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for specific tasks. Quantum architecture search (QAS) algorithms
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play a critical role in automating the design of high-performance
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parameterized quantum circuits (PQCs) for VQAs. However, existing
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QAS approaches struggle with large search spaces, leading to
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substantial computational overhead when optimizing large-scale
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quantum circuits. Extensive empirical analysis reveals that circuit
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topology has a greater impact on quantum circuit performance than
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gate types. Based on this insight, we propose the topology-driven
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quantum architecture search (TD-QAS) framework, which first
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identifies optimal circuit topologies and then fine-tunes the gate
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types. In the fine-tuning phase, the QAS inherits parameters from
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the topology search phase, eliminating the need for training from
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scratch. By decoupling the large search space into separate
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topology and gate-type components, TD-QAS avoids exploring gate
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configurations within low-performance topologies, thereby
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significantly reducing computational complexity. Numerical
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simulations across various tasks, under both noiseless and noisy
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conditions, validate the effectiveness of the TD-QAS framework.
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This framework advances standard QAS algorithms by enabling the
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identification of high-performance quantum circuits while
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minimizing computational demands. These findings indicate that
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TD-QAS deepens our understanding of VQAs and offers broad potential
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for the 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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