Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information
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Date
2021-03
Publication Type
Journal Article
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yes
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Abstract
The Quantum Fisher Information matrix (QFIM) is a central metric in promising algorithms, such as Quantum Natural Gradient Descent and Variational Quantum Imaginary Time Evolution. Computing the full QFIM for a model with d parameters, however, is computation-ally expensive and generally requires O(d(2)) function evaluations. To remedy these increasing costs in high-dimensional parameter spaces, we propose using simultaneous perturbation stochastic approximation techniques to approximate the QFIM at a constant cost. We present the resulting algorithm and successfully apply it to prepare Hamiltonian ground states and train Variational Quantum Boltzmann Machines.
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published
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5
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567
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Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften
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03781 - Renner, Renato / Renner, Renato
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