@article{quantum-advantage-bounds, title = {Information-Theoretic Bounds on Quantum Advantage in Machine Learning }, author = {Huang, Hsin-Yuan and Kueng, Richard and Preskill, John}, journal = {Phys. Rev. Lett.}, volume = {126}, issue = {19}, pages = {190505}, numpages = {7}, year = {2021}, month = {May}, publisher = {American Physical Society}, doi = {10.1103/PhysRevLett.126.190505}, url = {https://link.aps.org/doi/10.1103/PhysRevLett.126.190505}, } @article{quantum-advantage-learning, author = {Hsin-Yuan Huang and Michael Broughton and Jordan Cotler and Sitan Chen and Jerry Li and Masoud Mohseni and Hartmut Neven and Ryan Babbush and Richard Kueng and John Preskill and Jarrod R. McClean }, title = {Quantum advantage in learning from experiments}, journal = {Science}, volume = {376}, number = {6598}, pages = {1182-1186}, year = {2022}, doi = {10.1126/science.abn7293}, URL = {https://www.science.org/doi/abs/10.1126/science.abn7293}, eprint = {https://www.science.org/doi/pdf/10.1126/science.abn7293}, abstract = {Quantum technology promises to revolutionize how we learn about the physical world. An experiment that processes quantum data with a quantum computer could have substantial advantages over conventional experiments in which quantum states are measured and outcomes are processed with a classical computer. We proved that quantum machines could learn from exponentially fewer experiments than the number required by conventional experiments. This exponential advantage is shown for predicting properties of physical systems, performing quantum principal component analysis, and learning about physical dynamics. Furthermore, the quantum resources needed for achieving an exponential advantage are quite modest in some cases. Conducting experiments with 40 superconducting qubits and 1300 quantum gates, we demonstrated that a substantial quantum advantage is possible with today’s quantum processors. There is considerable interest in extending the recent success of quantum computers in outperforming their conventional classical counterparts (quantum advantage) from some model mathematical problems to more meaningful tasks. Huang et al. show how manipulating multiple quantum states can provide an exponential advantage over classical processing of measurements of single-quantum states for certain learning tasks. These include predicting properties of physical systems, performing quantum principal component analysis on noisy states, and learning approximate models of physical dynamics (see the Perspective by Dunjko). In their proof-of-principle experiments using up to 40 qubits on a Google Sycamore quantum processor, the authors achieved almost four orders of magnitude of reduction in the required number of experiments over the best-known classical lower bounds. —YS Quantum-enhanced strategies can provide a dramatic performance boost in learning useful information from quantum experiments.}, } @article{expressibility-and-entanglement, author = {Sim, Sukin and Johnson, Peter D. and Aspuru-Guzik, Alán}, title = {Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum-Classical Algorithms}, journal = {Advanced Quantum Technologies}, volume = {2}, number = {12}, pages = {1900070}, keywords = {quantum algorithms, quantum circuits, quantum computation}, doi = {https://doi.org/10.1002/qute.201900070}, url = {https://advanced.onlinelibrary.wiley.com/doi/abs/10.1002/qute.201900070 }, eprint = { https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/qute.201900070 }, abstract = {Abstract Parameterized quantum circuits (PQCs) play an essential role in the performance of many variational quantum algorithms. One challenge in implementing such algorithms is choosing an effective circuit that well represents the solution space while maintaining a low circuit depth and parameter count. To characterize and identify expressible, yet compact, circuits, several descriptors are proposed, including expressibility and entangling capability, that are statistically estimated from classical simulations. These descriptors are computed for different circuit structures, varying the qubit connectivity and selection of gates. From these simulations, circuit fragments that perform well with respect to the descriptors are identified. In particular, a substantial improvement in performance of two-qubit gates in a ring or all-to-all connected arrangement, compared to that of those on a line, is observed. Furthermore, improvement in both descriptors is achieved by sequences of controlled X-rotation gates compared to sequences of controlled Z-rotation gates. In addition, it is investigated how expressibility “saturates” with increased circuit depth, finding that the rate and saturated value appear to be distinguishing features of a PQC. While the correlation between each descriptor and algorithm performance remains to be investigated, methods and results from this study can be useful for algorithm development and design of experiments.}, year = {2019}, } @article{quantum-dynamics-physical-resource, title = {Quantum dynamics as a physical resource}, author = {Nielsen, Michael A. and Dawson, Christopher M. and Dodd, Jennifer L. and Gilchrist, Alexei and Mortimer, Duncan and Osborne, Tobias J. and Bremner, Michael J. and Harrow, Aram W. and Hines, Andrew}, journal = {Phys. Rev. A}, volume = {67}, issue = {5}, pages = {052301}, numpages = {19}, year = {2003}, month = {May}, publisher = {American Physical Society}, doi = {10.1103/PhysRevA.67.052301}, url = {https://link.aps.org/doi/10.1103/PhysRevA.67.052301}, } @article{scaling-variational-circuit-depth, doi = {10.22331/q-2020-05-28-272}, url = {https://doi.org/10.22331/q-2020-05-28-272}, title = {Scaling of variational quantum circuit depth for condensed matter systems}, author = {Bravo-Prieto, Carlos and Lumbreras-Zarapico, Josep and Tagliacozzo, Luca and Latorre, Jos{\'{e}} I.}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {4}, pages = {272}, month = may, year = {2020}, } @article{architecture-search, author = {Du, Yuxuan and Huang, Tao and You, Shan and Hsieh, Min-Hsiu and Tao, Dacheng}, title = {Quantum circuit architecture search for variational quantum algorithms}, journal = {npj Quantum Information}, year = {2022}, month = {May}, day = {23}, volume = {8}, number = {1}, pages = {62}, abstract = {Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS cannot only alleviate the influence of quantum noise and barren plateaus but also outperforms VQAs with pre-selected ansatze.}, issn = {2056-6387}, doi = {10.1038/s41534-022-00570-y}, url = {https://doi.org/10.1038/s41534-022-00570-y}, } @article{evolutionary-architecture-search, AUTHOR = {Ding, Li and Spector, Lee}, TITLE = {Multi-Objective Evolutionary Architecture Search for Parameterized Quantum Circuits}, JOURNAL = {Entropy}, VOLUME = {25}, YEAR = {2023}, NUMBER = {1}, ARTICLE-NUMBER = {93}, URL = {https://www.mdpi.com/1099-4300/25/1/93}, PubMedID = {36673234}, ISSN = {1099-4300}, ABSTRACT = {Recent work on hybrid quantum-classical machine learning systems has demonstrated success in utilizing parameterized quantum circuits (PQCs) to solve the challenging reinforcement learning (RL) tasks, with provable learning advantages over classical systems, e.g., deep neural networks. While existing work demonstrates and exploits the strength of PQC-based models, the design choices of PQC architectures and the interactions between different quantum circuits on learning tasks are generally underexplored. In this work, we introduce a Multi-objective Evolutionary Architecture Search framework for parameterized quantum circuits (MEAS-PQC), which uses a multi-objective genetic algorithm with quantum-specific configurations to perform efficient searching of optimal PQC architectures. Experimental results show that our method can find architectures that have superior learning performance on three benchmark RL tasks, and are also optimized for additional objectives including reductions in quantum noise and model size. Further analysis of patterns and probability distributions of quantum operations helps identify performance-critical design choices of hybrid quantum-classical learning systems.}, DOI = {10.3390/e25010093}, } @misc{generative-quantum-eigensolver, title = {The generative quantum eigensolver (GQE) and its application for ground state search}, author = {Kouhei Nakaji and Lasse Bjørn Kristensen and Ryota Kemmoku and Jorge A. Campos-Gonzalez-Angulo and Mohammad Ghazi Vakili and Haozhe Huang and Mohsen Bagherimehrab and Christoph Gorgulla and FuTe Wong and Alex McCaskey and Jin-Sung Kim and Thien Nguyen and Pooja Rao and Qi Gao and Michihiko Sugawara and Naoki Yamamoto and Alán Aspuru-Guzik}, year = {2025}, eprint = {2401.09253}, archivePrefix = {arXiv}, primaryClass = {quant-ph}, url = {https://arxiv.org/abs/2401.09253}, } @inproceedings{calibration-aware-transpilation, author = {Ji, Yanjun and Brandhofer, Sebastian and Polian, Ilia}, booktitle = {2022 IEEE International Conference on Quantum Computing and Engineering (QCE)}, title = {Calibration-Aware Transpilation for Variational Quantum Optimization}, year = {2022}, volume = {}, number = {}, pages = {204-214}, keywords = {Computers;Quantum computing;Quantum algorithm;Program processors;Error analysis;Logic gates;Calibration;Calibration-Aware;Transpilation;NISQ;QAOA;Benchmarking;Quantum Computing}, doi = {10.1109/QCE53715.2022.00040}, } // ye old ones @incollection{asmatulu_characterization_2019, title = {Characterization of electrospun nanofibers}, copyright = {https://www.elsevier.com/tdm/userlicense/1.0/}, isbn = {978-0-12-813914-1}, url = {https://linkinghub.elsevier.com/retrieve/pii/B9780128139141000134}, language = {en}, urldate = {2024-11-04}, booktitle = {Synthesis and {Applications} of {Electrospun} {Nanofibers}}, publisher = {Elsevier}, author = {Asmatulu, Ramazan and Khan, Waseem S.}, year = {2019}, doi = {10.1016/B978-0-12-813914-1.00013-4}, pages = {257--281}, } @article{binnig_atomic_1986, title = {Atomic {Force} {Microscope}}, volume = {56}, copyright = {http://link.aps.org/licenses/aps-default-license}, issn = {0031-9007}, url = {https://link.aps.org/doi/10.1103/PhysRevLett.56.930}, doi = {10.1103/PhysRevLett.56.930}, language = {en}, number = {9}, urldate = {2024-10-31}, journal = {Physical Review Letters}, author = {Binnig, G. and Quate, C. F. and Gerber, Ch.}, month = mar, year = {1986}, pages = {930--933}, } @book{boussinesq_application_1885, title = {Application des potentiels à l'étude de l'équilibre et du mouvement des solides élastiques}, copyright = {domaine public}, shorttitle = {Application des potentiels à l'étude de l'équilibre et du mouvement des solides élastiques, principalement au calcul des déformations et des pressions que produisent, dans les solides, des efforts quelquonques exercés sur une petite partie de leur surface ou de leur intérieur}, url = {https://gallica.bnf.fr/ark:/12148/bpt6k9651115r}, language = {EN}, urldate = {2024-10-10}, publisher = {Gauthier-Villars}, author = {Boussinesq, Joseph}, year = {1885}, } @article{yamanaka_nanoscale_2000, title = {Nanoscale elasticity measurement with in situ tip shape estimation in atomic force microscopy}, volume = {71}, issn = {0034-6748, 1089-7623}, url = { https://pubs.aip.org/rsi/article/71/6/2403/351012/Nanoscale-elasticity-measurement-with-in-situ-tip }, doi = {10.1063/1.1150627}, language = {en}, number = {6}, urldate = {2024-10-28}, journal = {Review of Scientific Instruments}, author = {Yamanaka, Kazushi and Tsuji, Toshihiro and Noguchi, Atsushi and Koike, Takayuki and Mihara, Tsuyoshi}, month = jun, year = {2000}, pages = {2403--2408}, }