Constructing k-local parent Lindbladians for matrix product density operators

Bondarenko, Dmytro

doi:10.48550/arxiv.2110.13134

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2023

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Cited by 1 publication

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References 69 publications

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“…Are QRNNs particularly suited for matrix-product states and density matrices, and is there a deeper connection to CC ML tensor network methods [101,102]? If so, can QRNNs be used for state preparation and to extend the notion of the parent Hamiltonian [103][104][105][106][107] and Lindbladian [108][109][110]? Can one use QRNNs for the discovery of catalysts?…”

Section: Discussionmentioning

confidence: 99%

Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networks

Bondarenko¹,

Salzmann²,

Schmiesing³

2023

Preprint

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Recurrent neural networks play an important role in both research and industry. With the advent of quantum machine learning, the quantisation of recurrent neural networks has become recently relevant. We propose fully quantum recurrent neural networks, based on dissipative quantum neural networks, capable of learning general causal quantum automata. A quantum training algorithm is proposed and classical simulations for the case of product outputs with the fidelity as cost function are carried out. We thereby demonstrate the potential of these algorithms to learn complex quantum processes with memory in terms of the exemplary delay channel, the time evolution of quantum states governed by a time-dependent Hamiltonian, and highand low-frequency noise mitigation. Numerical simulations indicate that our quantum recurrent neural networks exhibit a striking ability to generalise from small training sets.

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