327 lines
18 KiB
BibTeX
327 lines
18 KiB
BibTeX
@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},
|
||
}
|
||
|
||
@misc{akash,
|
||
title={Reinforcement learning-assisted quantum architecture search for variational quantum algorithms},
|
||
author={Akash Kundu},
|
||
year={2024},
|
||
eprint={2402.13754},
|
||
archivePrefix={arXiv},
|
||
primaryClass={quant-ph},
|
||
url={https://arxiv.org/abs/2402.13754},
|
||
}
|
||
|
||
@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},
|
||
}
|
||
|
||
@article{topology-driven-search,
|
||
author = {Su, Junjian and Fan, Jiacheng and Wu, Shengyao and Li, Guanghui and
|
||
Qin, Sujuan and Gao, Fei},
|
||
title = {Topology-driven quantum architecture search framework},
|
||
journal = {Science China Information Sciences},
|
||
year = {2025},
|
||
month = {Jul},
|
||
day = {03},
|
||
volume = {68},
|
||
number = {8},
|
||
pages = {180507},
|
||
abstract = {The limitations of noisy intermediate-scale quantum (NISQ) devices
|
||
have motivated the development of variational quantum algorithms
|
||
(VQAs), which are designed to potentially achieve quantum advantage
|
||
for specific tasks. Quantum architecture search (QAS) algorithms
|
||
play a critical role in automating the design of high-performance
|
||
parameterized quantum circuits (PQCs) for VQAs. However, existing
|
||
QAS approaches struggle with large search spaces, leading to
|
||
substantial computational overhead when optimizing large-scale
|
||
quantum circuits. Extensive empirical analysis reveals that circuit
|
||
topology has a greater impact on quantum circuit performance than
|
||
gate types. Based on this insight, we propose the topology-driven
|
||
quantum architecture search (TD-QAS) framework, which first
|
||
identifies optimal circuit topologies and then fine-tunes the gate
|
||
types. In the fine-tuning phase, the QAS inherits parameters from
|
||
the topology search phase, eliminating the need for training from
|
||
scratch. By decoupling the large search space into separate
|
||
topology and gate-type components, TD-QAS avoids exploring gate
|
||
configurations within low-performance topologies, thereby
|
||
significantly reducing computational complexity. Numerical
|
||
simulations across various tasks, under both noiseless and noisy
|
||
conditions, validate the effectiveness of the TD-QAS framework.
|
||
This framework advances standard QAS algorithms by enabling the
|
||
identification of high-performance quantum circuits while
|
||
minimizing computational demands. These findings indicate that
|
||
TD-QAS deepens our understanding of VQAs and offers broad potential
|
||
for the development of future QAS algorithms.},
|
||
issn = {1869-1919},
|
||
doi = {10.1007/s11432-024-4486-x},
|
||
url = {https://doi.org/10.1007/s11432-024-4486-x},
|
||
}
|
||
|
||
@article{Hirsbrunner2024beyondmp,
|
||
doi = {10.22331/q-2024-11-26-1538},
|
||
url = {https://doi.org/10.22331/q-2024-11-26-1538},
|
||
title = {Beyond {MP}2 initialization for unitary coupled cluster quantum circuits},
|
||
author = {Hirsbrunner, Mark R. and Chamaki, Diana and Mullinax, J. Wayne and Tubman, Norm M.},
|
||
journal = {{Quantum}},
|
||
issn = {2521-327X},
|
||
publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
|
||
volume = {8},
|
||
pages = {1538},
|
||
month = nov,
|
||
year = {2024}
|
||
}
|
||
|
||
@misc{liu2025haqgnnhardwareawarequantumkernel,
|
||
title={HaQGNN: Hardware-Aware Quantum Kernel Design Based on Graph Neural Networks},
|
||
author={Yuxiang Liu and Fanxu Meng and Lu Wang and Yi Hu and Sixuan Li and Xutao Yu and Zaichen Zhang},
|
||
year={2025},
|
||
eprint={2506.21161},
|
||
archivePrefix={arXiv},
|
||
primaryClass={quant-ph},
|
||
url={https://arxiv.org/abs/2506.21161},
|
||
}
|
||
|
||
@article{training-free,
|
||
title={Training-Free Quantum Architecture Search},
|
||
volume={38},
|
||
url={https://ojs.aaai.org/index.php/AAAI/article/view/29135},
|
||
DOI={10.1609/aaai.v38i11.29135},
|
||
abstractNote={Variational quantum algorithm (VQA) derives advantages from its error resilience and high flexibility in quantum resource requirements, rendering it broadly applicable in the noisy intermediate-scale quantum era. As the performance of VQA highly relies on the structure of the parameterized quantum circuit, it is worthwhile to propose quantum architecture search (QAS) algorithms to automatically search for high-performance circuits. Nevertheless, existing QAS methods are time-consuming, requiring circuit training to assess circuit performance. This study pioneers training-free QAS by utilizing two training-free proxies to rank quantum circuits, in place of the expensive circuit training employed in conventional QAS. Taking into account the precision and computational overhead of the path-based and expressibility-based proxies, we devise a two-stage progressive training-free QAS (TF-QAS). Initially, directed acyclic graphs (DAGs) are employed for circuit representation, and a zero-cost proxy based on the number of paths in the DAG is designed to filter out a substantial portion of unpromising circuits. Subsequently, an expressibility-based proxy, finely reflecting circuit performance, is employed to identify high-performance circuits from the remaining candidates. These proxies evaluate circuit performance without circuit training, resulting in a remarkable reduction in computational cost compared to current training-based QAS methods. Simulations on three VQE tasks demonstrate that TF-QAS achieves a substantial enhancement of sampling efficiency ranging from 5 to 57 times compared to state-of-the-art QAS, while also being 6 to 17 times faster.},
|
||
number={11},
|
||
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
|
||
author={He, Zhimin and Deng, Maijie and Zheng, Shenggen and Li, Lvzhou and Situ, Haozhen},
|
||
year={2024},
|
||
month={Mar.},
|
||
pages={12430-12438}
|
||
}
|