期刊
NEW JOURNAL OF PHYSICS
卷 21, 期 -, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1367-2630/ab14b5
关键词
quantum circuit learning; generative models; unsupervised learning
资金
- UK Engineering and Physical Sciences Research Council (EPSRC)
- Cambridge Quantum Computing Limited (CQCL)
- ESPRC [EP/P510270/1]
- Royal Society
- NVIDIA Corporation
- EPSRC
- National Natural Science Foundation of China
- [ARO-MURI W911NF17-1-0304]
Adversarial learning is one of the most successful approaches to modeling high-dimensional probability distributions from data. The quantum computing community has recently begun to generalize this idea and to look for potential applications. In this work, we derive an adversarial algorithm for the problem of approximating an unknown quantum pure state. Although this could be done on universal quantum computers, the adversarial formulation enables us to execute the algorithm on near-term quantum computers. Two parametrized circuits are optimized in tandem: one tries to approximate the target state, the other tries to distinguish between target and approximated state. Supported by numerical simulations, we show that resilient backpropagation algorithms perform remarkably well in optimizing the two circuits. We use the bipartite entanglement entropy to design an efficient heuristic for the stopping criterion. Our approach may find application in quantum state tomography.
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