CutReg: A loss regularizer for enhancing the scalability of QML via adaptive circuit cutting

TL;DR

This paper introduces a novel regularizer for quantum machine learning that improves scalability by balancing circuit cutting overhead and model accuracy, enabling the study of larger quantum problems on limited hardware.

Contribution

A new loss regularizer for QML that penalizes sampling overhead, facilitating larger circuit execution on NISQ devices.

Findings

Regularizer improves scalability of QML models.

Balances circuit cutting overhead with model accuracy.

Enables study of larger quantum circuits on limited hardware.

Abstract

Whether QML can offer a transformative advantage remains an open question. The severe constraints of NISQ hardware, particularly in circuit depth and connectivity, hinder both the validation of quantum advantage and the empirical investigation of major obstacles like barren plateaus. Circuit cutting techniques have emerged as a strategy to execute larger quantum circuits on smaller, less connected hardware by dividing them into subcircuits. However, this partitioning increases the number of samples needed to estimate the expectation value accurately through classical post-processing compared to estimating it directly from the full circuit. This work introduces a novel regularization term into the QML optimization process, directly penalizing the overhead associated with sampling. We demonstrate that this approach enables the optimizer to balance the advantages of gate cutting against the optimization of the typical ML cost function. Specifically, it navigates the trade-off between minimizing the cutting overhead and maintaining the overall accuracy of the QML model, paving the way to study larger complex problems in pursuit of quantum advantage.