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Machine learning quantum states in the NISQ era

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arxiv 1905.04312 v1 pith:KY2EH74T submitted 2019-05-10 quant-ph cond-mat.dis-nncond-mat.quant-gascond-mat.str-el

classification quant-phcond-mat.dis-nncond-mat.quant-gascond-mat.str-el
keywords machinequantumlearningnisqreconstructionstatesdiscussexperimental
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We review the development of generative modeling techniques in machine learning for the purpose of reconstructing real, noisy, many-qubit quantum states. Motivated by its interpretability and utility, we discuss in detail the theory of the restricted Boltzmann machine. We demonstrate its practical use for state reconstruction, starting from a classical thermal distribution of Ising spins, then moving systematically through increasingly complex pure and mixed quantum states. Intended for use on experimental noisy intermediate-scale quantum (NISQ) devices, we review recent efforts in reconstruction of a cold atom wavefunction. Finally, we discuss the outlook for future experimental state reconstruction using machine learning, in the NISQ era and beyond.

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  1. The learnability scaling of quantum states: restricted Boltzmann machines

    quant-ph 2019-08 conditional novelty 6.0 of 10

    To reproduce the ground-state energy of a one-dimensional transverse-field Ising chain near its critical point, a restricted Boltzmann machine needs a number of weights that grows as the square of the number of qubits...

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