Parsimonious Optimisation of Parameters in Variational Quantum Circuits

• URL: http://arxiv.org/abs/2306.11842v2
• Date: Thu, 27 Jul 2023 17:50:06 GMT
• Title: Parsimonious Optimisation of Parameters in Variational Quantum Circuits
• Authors: Sayantan Pramanik, Chaitanya Murti, M Girish Chandra
• Abstract summary: We propose a novel Quantum-Gradient Sampling that requires the execution of at most two circuits per iteration to update the optimisable parameters. Our proposed method achieves similar convergence rates to classical gradient descent, and empirically outperforms gradient coordinate descent, and SPSA.
• Score: 1.303764728768944
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Variational quantum circuits characterise the state of a quantum system through the use of parameters that are optimised using classical optimisation procedures that typically rely on gradient information. The circuit-execution complexity of estimating the gradient of expectation values grows linearly with the number of parameters in the circuit, thereby rendering such methods prohibitively expensive. In this paper, we address this problem by proposing a novel Quantum-Gradient Sampling algorithm that requires the execution of at most two circuits per iteration to update the optimisable parameters, and with a reduced number of shots. Furthermore, our proposed method achieves similar asymptotic convergence rates to classical gradient descent, and empirically outperforms gradient descent, randomised coordinate descent, and SPSA.

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