Cutting is All You Need: Execution of Large-Scale Quantum Neural Networks on Limited-Qubit Devices

TL;DR

This paper introduces a novel quantum circuit cutting method for hybrid quantum-classical neural networks, enabling large-scale quantum circuits to run on limited-qubit NISQ devices while maintaining accuracy.

Contribution

It proposes a practical quantum circuit cutting approach with a greedy algorithm and trainable subcircuits, improving the execution of large HQNNs on NISQ hardware.

Findings

Circuit cutting achieves comparable accuracy to original circuits.

Significantly reduces qubit requirements for large quantum circuits.

Supports training of quantum parameters across subcircuits.

Abstract

The rapid advancement in Quantum Computing, particularly through Noisy-Intermediate Scale Quantum (NISQ) devices, has spurred significant interest in Quantum Machine Learning (QML) applications. Despite their potential, fully-quantum algorithms remain impractical due to the limitations of current NISQ devices. Hybrid quantum-classical neural networks (HQNNs) have emerged as a viable alternative, leveraging both quantum and classical computations to enhance machine learning capabilities. However, the constrained resources of NISQ devices, particularly the limited number of qubits, pose significant challenges for executing large-scale quantum circuits. This work addresses these current challenges by proposing a novel and practical methodology for quantum circuit cutting of HQNNs, allowing large quantum circuits to be executed on limited-qubit NISQ devices. Our approach not only preserves the accuracy of the original circuits but also supports the training of quantum parameters across all subcircuits, which is crucial for the learning process in HQNNs. We propose a cutting methodology for HQNNs that employs a greedy algorithm for identifying efficient cutting points, and the implementation of trainable subcircuits, all designed to maximize the utility of NISQ devices in HQNNs. The findings suggest that quantum circuit cutting is a promising technique for advancing QML on current quantum hardware, since the cut circuit achieves comparable accuracy and much lower qubit requirements than the original circuit. The code is available at https://github.com/eBrain4Everyone/QNN-Cutting.