Learning parameter curves in feedback-based quantum optimization algorithms

TL;DR

This paper demonstrates that classical machine learning models can accurately predict parameter sequences in feedback-based quantum algorithms, potentially reducing measurement costs and resource overheads.

Contribution

It introduces a method to predict FQA parameter curves using a classical model, bypassing the need for quantum measurements during optimization.

Findings

The model accurately predicts FQA parameter curves across various problem sizes.

Predicted curves perform comparably to actual FQA curves in simulations.

The approach outperforms linear quantum annealing schedules in results.

Abstract

Feedback-based quantum algorithms (FQAs) operate by iteratively growing a quantum circuit to optimize a given task. At each step, feedback from qubit measurements is used to inform the next quantum circuit update. In practice, the sampling cost associated with these measurements can be significant. Here, we ask whether FQA parameter sequences can be predicted using classical machine learning, obviating the need for qubit measurements altogether. To this end, we train a teacher-student model to map a MaxCut problem instance to an associated FQA parameter curve in a single classical inference step. Numerical experiments show that this model can accurately predict FQA parameter curves across a range of problem sizes, including problem sizes not seen during model training. To evaluate performance, we compare the predicted parameter curves in simulation against FQA reference curves and linear quantum annealing schedules. We observe similar results to the former and performance improvements over the latter. These results suggest that machine learning can offer a heuristic, practical path to reducing sampling costs and resource overheads in quantum algorithms.