Faster variational quantum algorithms with quantum kernel-based surrogate models

TL;DR

This paper introduces a quantum kernel-based surrogate model to optimize variational quantum algorithms more efficiently, reducing quantum circuit evaluations and improving accuracy and convergence on noisy near-term quantum devices.

Contribution

The paper presents a novel quantum kernel-based surrogate model for variational algorithms, enabling fewer quantum evaluations and better performance compared to classical kernels and gradient methods.

Findings

Quantum kernel surrogate models outperform classical kernels in VQE accuracy.

The approach requires significantly fewer quantum circuit evaluations than gradient-based methods.

The method is effective on both noiseless and noisy quantum simulations.

Abstract

We present a new optimization method for small-to-intermediate scale variational algorithms on noisy near-term quantum processors which uses a Gaussian process surrogate model equipped with a classically-evaluated quantum kernel. Variational algorithms are typically optimized using gradient-based approaches however these are difficult to implement on current noisy devices, requiring large numbers of objective function evaluations. Our scheme shifts this computational burden onto the classical optimizer component of these hybrid algorithms, greatly reducing the number of queries to the quantum processor. We focus on the variational quantum eigensolver (VQE) algorithm and demonstrate numerically that such surrogate models are particularly well suited to the algorithm's objective function. Next, we apply these models to both noiseless and noisy VQE simulations and show that they exhibit better performance than widely-used classical kernels in terms of final accuracy and convergence speed. Compared to the typically-used stochastic gradient-descent approach for VQAs, our quantum kernel-based approach is found to consistently achieve significantly higher accuracy while requiring less than an order of magnitude fewer quantum circuit evaluations. We analyse the performance of the quantum kernel-based models in terms of the kernels' induced feature spaces and explicitly construct their feature maps. Finally, we describe a scheme for approximating the best-performing quantum kernel using a classically-efficient tensor network representation of its input state and so provide a pathway for scaling these methods to larger systems.