Efficient Online Quantum Circuit Learning with No Upfront Training

TL;DR

This paper introduces a surrogate-based optimization method for quantum circuits that minimizes quantum computer calls, outperforming previous methods on large-scale problems and demonstrating practical feasibility on real hardware.

Contribution

It presents a novel classical surrogate approach using radial basis functions for efficient quantum circuit optimization without hyperparameter tuning.

Findings

Outperforms prior state-of-the-art methods on 16-qubit Max-Cut problems.

Successfully optimizes 127-qubit QAOA circuits on an IBM quantum processor.

Requires fewer quantum measurements, demonstrating practical large-scale application.

Abstract

We propose a surrogate-based method for optimizing parameterized quantum circuits which is designed to operate with few calls to a quantum computer. We employ a computationally inexpensive classical surrogate to approximate the cost function of a variational quantum algorithm. An initial surrogate is fit to data obtained by sparse sampling of the true cost function using noisy quantum computers. The surrogate is iteratively refined by querying the true cost at the surrogate optima, then using radial basis function interpolation with existing and new true cost data. The use of radial basis function interpolation enables surrogate construction without hyperparameters to pre-train. Additionally, using the surrogate as an acquisition function focuses hardware queries in the vicinity of the true optima. For 16-qubit random 3-regular Max-Cut problems solved using the QAOA ansatz, we find that our method outperforms the prior state of the art. Furthermore, we demonstrate successful optimization of QAOA circuits for 127-qubit random Ising models on an IBM quantum processor using measurement counts of the order of $10^4-10^5$. The strong empirical performance of this approach is an important step towards the large-scale practical application of variational quantum algorithms and a clear demonstration of the effectiveness of classical-surrogate-based learning approaches.