Solving Quantum Master Equations with Deep Quantum Neural Networks
- URL: http://arxiv.org/abs/2008.05488v1
- Date: Wed, 12 Aug 2020 18:00:08 GMT
- Title: Solving Quantum Master Equations with Deep Quantum Neural Networks
- Authors: Zidu Liu, L.-M. Duan, Dong-Ling Deng
- Abstract summary: We use deep quantum feedforward neural networks capable of universal quantum computation to represent the mixed states for open quantum many-body systems.
Owning to the special structure of the quantum networks, this approach enjoys a number of notable features, including the absence of barren plateaus.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep quantum neural networks may provide a promising way to achieve quantum
learning advantage with noisy intermediate scale quantum devices. Here, we use
deep quantum feedforward 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 the absence of barren plateaus, efficient quantum analogue of the
backpropagation 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 $J_1-J_2$ models with dissipation, and show that it
can efficiently capture their dynamics and stationary states with a desired
accuracy.
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