qcr:2604.58800.1

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

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.

QML

Qubit

Circuit-based

Uploaded 2 months ago

Views

View Publication

GitHub

Citing this entry? Use this QCR ID

Uploaded by

QCR Librarian

eBrain4Everyone/QNN-Cutting

README.md

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

This repository provides the source codes for applying Quantum Circuit Cutting on Quantum Neural Networks. If you used these results in your research, please refer to the paper:

A. Marchisio, E. Sychiuco, M. Kashif, and M. Shafique, "Cutting is All You Need: Execution of Large-Scale Quantum Neural Networks on Limited-Qubit Devices," IEEE International Conference on Quantum Artificial Intelligence (QAI), Naples, Italy, November 2025.

@INPROCEEDINGS{Marchisio2025Cutting,
  author={A. {Marchisio} and E. {Sychiuco} and M. {Kashif} and M. {Shafique}},
  booktitle={IEEE International Conference on Quantum Artificial Intelligence (QAI)}, 
  title={Cutting is All You Need: Execution of Large-Scale Quantum Neural Networks on Limited-Qubit Devices}, 
  year={2025},
  volume={},
  number={},
  pages={}}

INSTALLATION INSTRUCTIONS

This project provides two installation options:

Requirements.txt → For regular Python environments using pip.
Requirements.yml → For Conda environments.

USING Requirements.txt (pip)

Open a terminal in the folder containing Requirements.txt.
Run: pip install -r Requirements.txt

USING Requirements.yml (Conda)

Open a terminal in the folder containing Requirements.yml.
Run: conda env create -f Requirements.yml
Activate the environment: conda activate myenv

NOTES

Use Requirements.txt if you already have a Python environment set up.
Use Requirements.yml if you want a separate Conda environment with a specific Python version.

Join the Discussion

Comments (0)

No comments yet. Be the first to share your thoughts!

Indexed by QCR Librarian

This entry was created automatically from publicly available records. QCR links to public sources and only stores repository content where the license permits redistribution.

Claim this entry →