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• W3048705243 abstract "Deep quantum neural networks may provide a promising way to achieve a quantum learning advantage with noisy intermediate-scale quantum devices. Here, we use deep quantum feed-forward neural networks capable of universal quantum computation to represent the mixed states for open quantum many-body systems and introduce a variational method with quantum derivatives to solve the master equation for dynamics and stationary states. Owning to the special structure of the quantum networks, this approach enjoys a number of notable features, including an efficient quantum analog of the back-propagation algorithm, resource-saving reuse of hidden qubits, general applicability independent of dimensionality and entanglement properties, as well as the convenient implementation of symmetries. As proof-of-principle demonstrations, we apply this approach to both one-dimensional transverse field Ising and two-dimensional ${J}_{1}text{ensuremath{-}}{J}_{2}$ models with dissipation, and show that it can efficiently capture their dynamics and stationary states with a desired accuracy." @default.
• W3048705243 created "2020-08-18" @default.
• W3048705243 creator A5034703407 @default.
• W3048705243 creator A5057614578 @default.
• W3048705243 creator A5061987441 @default.
• W3048705243 date "2022-02-09" @default.
• W3048705243 modified "2023-09-23" @default.
• W3048705243 title "Solving quantum master equations with deep quantum neural networks" @default.
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• W3048705243 doi "https://doi.org/10.1103/physrevresearch.4.013097" @default.
• W3048705243 hasPublicationYear "2022" @default.
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