Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Networks

Meyer N, Ufrecht C, Periyasamy M, Plinge A, Mutschler C, Scherer DD, Maier A (2024)


Publication Type: Conference contribution

Publication year: 2024

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 1

Pages Range: 817-823

Conference Proceedings Title: Proceedings - IEEE Quantum Week 2024, QCE 2024

Event location: Montreal, QC CA

ISBN: 9798331541378

DOI: 10.1109/QCE60285.2024.00101

Abstract

Quantum computer simulation software is an integral tool for the research efforts in the quantum computing community. An important aspect is the efficiency of respective frameworks, especially for training variational quantum algorithms. Focusing on the widely used Qiskit software environment, we develop the qiskit-torch-module. It improves runtime performance by two orders of magnitude over comparable libraries, while facilitating low-overhead integration with existing code-bases. Moreover, the framework provides advanced tools for integrating quantum neural networks with PyTorch. The pipeline is tailored for single-machine compute systems, which constitute a widely employed setup in day-to-day research efforts.

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How to cite

APA:

Meyer, N., Ufrecht, C., Periyasamy, M., Plinge, A., Mutschler, C., Scherer, D.D., & Maier, A. (2024). Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Networks. In Candace Culhane, Greg T. Byrd, Hausi Muller, Yuri Alexeev, Yuri Alexeev, Sarah Sheldon (Eds.), Proceedings - IEEE Quantum Week 2024, QCE 2024 (pp. 817-823). Montreal, QC, CA: Institute of Electrical and Electronics Engineers Inc..

MLA:

Meyer, Nico, et al. "Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Networks." Proceedings of the 5th IEEE International Conference on Quantum Computing and Engineering, QCE 2024, Montreal, QC Ed. Candace Culhane, Greg T. Byrd, Hausi Muller, Yuri Alexeev, Yuri Alexeev, Sarah Sheldon, Institute of Electrical and Electronics Engineers Inc., 2024. 817-823.

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