Learning and inference on generative adversarial quantum circuits
Open accessAbstract
Quantum mechanics is inherently probabilistic in light of Born's rule. Using quantum circuits as probabilistic generative models for classical data exploits their superior expressibility and efficient direct sampling ability. However, training of quantum circuits can be more challenging compared to classical neural networks due to the lack of an efficient differentiable learning algorithm. We devise an adversarial quantum-classical hybrid training scheme via coupling a quantum circuit generator and a classical neural network discriminator together. After training, the quantum circuit generative model can infer missing data with quadratic speed-up via amplitude amplification. We numerically simulate the learning and inference of generative adversarial quantum circuits using the prototypical bars-and-stripes dataset. Generative adversarial quantum circuits are a fresh approach to machine learning which may enjoy the practically useful quantum advantage of near-term quantum devices.
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In many cases generators can be chosen such that only real amplitudes are generated.
“In many cases generators can be chosen such that only real amplitudes are generated, see for instance [70].”
From Solving nonlinear differential equations with differentiable quantum circuits (2021) · cites this paper as [70]
Trainability can change depending on the variational ansatz type and cost function choice.
“This can change depending on the variational ansatz type and cost function choice [70].”
From Solving nonlinear differential equations with differentiable quantum circuits (2021) · cites this paper as [70]
The alternating blocks ansatz's motivation is to entangle qubits locally first and gradually form a correlated state by interleaving subblocks, which helps improve trainability while maintaining high expressibility.
“The motivation behind ABA is that we want to entangle qubits locally first, and gradually form a correlated state by interleaving subblocks.This helps to improve trainability of the circuit together while maintaining high expressibility [70,164].”
From Solving nonlinear differential equations with differentiable quantum circuits (2021) · cites this paper as [70]
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F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
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Quantum Generative Adversarial Networks in a Silicon Photonic Chip with Maximum Expressibility
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Self-citationAdvancing single-cell omics and cell-based therapeutics with quantum computing
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Quantum subspace alignment for domain adaptation
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Efficient Online Quantum Generative Adversarial Learning Algorithms with ApplicationsPreprint
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Adversarial learning in quantum artificial intelligence
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Information flow in parameterized quantum circuits
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Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework
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An Empirical Study of Optimizers for Quantum Machine Learning
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Quantum Generative Adversarial Networks
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Quantum Earth Mover's Distance: A New Approach to Learning Quantum DataPreprint
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Generative Quantum Machine LearningPreprint
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Multiqubit state learning with entangling quantum generative adversarial networks
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- Authors
- Zeng, J., Wu, Y., Liu, J.-G., Wang, L., & Hu, J.
- Affiliations
- Chinese Academy of Sciences (CN)
- Jilin University (CN)
- Venue
- Physical Review A
- Publisher
- American Physical Society (APS)
- Published
- 2019-05-06
- Volume
- 99
- Issue
- 5
- ISSN
- 0556-2791 (ISSN-L)
- 2469-9926
- 2469-9934
- OpenAlex
- W2949863591
- Cited by
- 94
- References
- 47
- Citations by year
- 2026: 11 · 2025: 5 · 2024: 10 · 2023: 10 · 2022: 12 · 2021: 26 · 2020: 14 · 2019: 5 · 2018: 1
- FWCI
- 6.58
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical Review A (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Ministry of Science and Technology of the People's Republic of China (2016YFA0300603, 2015CB921300, 2017YFA0303100)
- National Natural Science Foundation of China (11774398, NSFC-1190020, NSFC-11534014, NSFC-11334012)
- Chinese Academy of Sciences (XDPB0803)
- Center for Advanced Study, University of Illinois at Urbana-Champaign (XDB07000000, Y7CY031D31)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Keywords
- quantum generative models, quantum circuit training, amplitude amplification, missing data inference, quantum advantage, quantum machine learning
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