Efficient online quantum circuit learning with no upfront training

Abstract

Optimization is a promising candidate for studying the utility of variational quantum algorithms (VQAs). However, evaluating cost functions using quantum hardware introduces runtime overheads that limit exploration. Surrogate-based methods can reduce calls to a quantum computer, yet existing approaches require hyperparameter pre-training and have been tested only on small problems. Here, we show that surrogate-based methods can enable successful optimization at scale, without pre-training, by using radial basis function interpolation (RBF) to construct an adaptive, hyperparameter-free surrogate. Using the surrogate as an acquisition function drives hardware queries to the vicinity of the true optima. For 16-qubit random 3-regular Max-Cut instances with the Quantum Approximate Optimization Algorithm (QAOA), our method outperforms state-of-the-art approaches, without considering their upfront training costs. Furthermore, we successfully optimize QAOA circuits for 127-qubit random Ising models on an IBM processor using 104−105 measurements. Strong empirical performance demonstrates the promise of automated surrogate-based learning for large-scale VQA applications. Optimization is a promising application for quantum computing; however, progress is limited by the time needed to score candidate solutions using quantum hardware. Here, the authors show that a small number of hardware measurements can train a classical model to predict scores, substantially reducing the number of quantum evaluations.