Surrogate-based optimization for variational quantum algorithms

Abstract

Variational quantum algorithms are a class of techniques intended to be used on near-term quantum computers. The goal of these algorithms is to perform large quantum computations by breaking the problem down into a large number of shallow quantum circuits, complemented by classical optimization and feedback between each circuit execution. One path for improving the performance of these algorithms is to enhance the classical optimization technique. Given the relative ease and abundance of classical computing resources, there is ample opportunity to do so. In this work, we introduce the idea of learning surrogate models for variational circuits using few experimental measurements, and then performing parameter optimization using these models as opposed to the original data. We demonstrate this idea using a surrogate model based on kernel approximations, through which we reconstruct local patches of variational cost functions using batches of noisy quantum circuit results. Through application to the quantum approximate optimization algorithm and preparation of ground states for molecules, we demonstrate the superiority of surrogate-based optimization over commonly-used optimization techniques for variational algorithms.

arXiv commentPublished version

References in corpus (15)

• Quantum Computing in the NISQ era and beyond
• A variational eigenvalue solver on a quantum processor
• Variational Quantum Algorithms
• The theory of variational hybrid quantum-classical algorithms
• Error mitigation for short-depth quantum circuits
• Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
• Extending the computational reach of a noisy superconducting quantum processor
• Practical Quantum Error Mitigation for Near-Future Applications
• Stochastic gradient descent for hybrid quantum-classical optimization
• An Adaptive Optimizer for Measurement-Frugal Variational Algorithms
• Performance comparison of optimization methods on variational quantum algorithms
• From pulses to circuits and back again: A quantum optimal control perspective on variational quantum algorithms
• Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems
• Using models to improve optimizers for variational quantum algorithms
• Variational quantum algorithm with information sharing

Cited by in corpus (10)

• Quantum approximate optimization via learning-based adaptive optimization
• Two quantum algorithms for solving the one-dimensional advection-diffusion equation
• Lie-algebraic classical simulations for quantum computing
• A Parameter Setting Heuristic for the Quantum Alternating Operator Ansatz
• Faster variational quantum algorithms with quantum kernel-based surrogate models
• Numerical solution of nonlinear Schrödinger equation by a hybrid pseudospectral-variational quantum algorithm
• Efficient quantum-enhanced classical simulation for patches of quantum landscapes
• A Monte Carlo Tree Search approach to QAOA: finding a needle in the haystack
• Efficient Online Quantum Circuit Learning with No Upfront Training
• Optimal Quantum Likelihood Estimation