Efficient Estimation and Sequential Optimization of Cost Functions in Variational Quantum Algorithms

TL;DR

This paper introduces a novel optimization method for variational quantum algorithms that efficiently evaluates cost functions and derivatives, leading to faster convergence and improved accuracy in complex quantum problems.

Contribution

The work presents a new optimization framework that models quantum circuits as weighted sums of unitaries, enabling efficient evaluation of nonlocal cost functions and derivatives.

Findings

Enhanced convergence speed over traditional methods

Improved accuracy in high-dimensional landscapes

Successful application to fluid dynamics and quantum ground state problems

Abstract

Classical optimization is a cornerstone of the success of variational quantum algorithms, which often require determining the derivatives of the cost function relative to variational parameters. The computation of the cost function and its derivatives, coupled with their effective utilization, facilitates faster convergence by enabling smooth navigation through complex landscapes, ensuring the algorithm's success in addressing challenging variational problems. In this work, we introduce a novel optimization methodology that conceptualizes the parameterized quantum circuit as a weighted sum of distinct unitary operators, enabling the cost function to be expressed as a sum of multiple terms. This representation facilitates the efficient evaluation of nonlocal characteristics of cost functions, as well as their arbitrary derivatives. The optimization protocol then utilizes the nonlocal information on the cost function to facilitate a more efficient navigation process, ultimately enhancing the performance in the pursuit of optimal solutions. We utilize this methodology for two distinct cost functions. The first is the squared residual of the variational state relative to a target state, which is subsequently employed to examine the nonlinear dynamics of fluid configurations governed by the one-dimensional Burgers' equation. The second cost function is the expectation value of an observable, which is later utilized to approximate the ground state of the nonlinear Schr\"{o}dinger equation. Our findings reveal substantial enhancements in convergence speed and accuracy relative to traditional optimization methods, even within complex, high-dimensional landscapes. Our work contributes to the advancement of optimization strategies for variational quantum algorithms, establishing a robust framework for addressing a range of computationally intensive problems across numerous applications.