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Symmetric Pruning in Quantum Neural Networks. (arXiv:2208.14057v1 [quant-ph])
Aug. 31, 2022, 1:10 a.m. | Xinbiao Wang, Junyu Liu, Tongliang Liu, Yong Luo, Yuxuan Du, Dacheng Tao
cs.LG updates on arXiv.org arxiv.org
Many fundamental properties of a quantum system are captured by its
Hamiltonian and ground state. Despite the significance of ground states
preparation (GSP), this task is classically intractable for large-scale
Hamiltonians. Quantum neural networks (QNNs), which exert the power of modern
quantum machines, have emerged as a leading protocol to conquer this issue. As
such, how to enhance the performance of QNNs becomes a crucial topic in GSP.
Empirical evidence showed that QNNs with handcraft symmetric ansatzes generally
experience better …
arxiv networks neural networks pruning quantum quantum neural networks
More from arxiv.org / cs.LG updates on arXiv.org
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