2024/12/06 by Alberto Marchisio, Emman Sychiuco, Marchisio, Alberto +5 · 2 citations
Computer Science · Engineering · Physics and Astronomy · #Advancements in Semiconductor Devices and Circuit Design #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Physical sciences #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph) #Quantum and electron transport phenomena
paper · pdf · doi:10.48550/arxiv.2412.04844
openalex publication_date 2024/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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.