February 15, 2024
Conference Paper

Distributed Quantum Learning with co-Management in a Multi-tenant Quantum System


The rapid advancement of quantum computing has pushed classical designs into the quantum domain, breaking physical boundaries for computing-intensive and data-hungry applications with the hope that some systems may provide a quantum speedup. For example, variational quantum algorithms have been proposed for quantum neural networks to train deep learning models on qubits, achieving promising results. Existing quantum learning architectures and systems rely on single, monolithic quantum machines with abundant and stable resources, such as qubits. However, fabricating a large, monolithic quantum device is considerably more challenging than producing an array of smaller devices. In this paper, we investigate a distributed quantum system that combines multiple quantum machines into a unified system. We propose DQuLearn, which divides a quantum learning task into multiple subtasks. Each subtask can be executed distributively on individual quantum machines, with the results looping back to classical machines for subsequent training iterations. Additionally, our system supports multiple concurrent clients and dynamically manages their circuits according to the runtime status of quantum workers. Through extensive experiments, we demonstrate that DQuLearn achieves similar accuracies with significant runtime reduction, by up to 68.7% and an increase per-second circuit processing speed, by up to 3.99 times, in a 4-worker multi-tenant setting.

Published: February 15, 2024


D'Onofrio A., A. Hossain, L. Santana, N. Machlovi, S.A. Stein, J. Liu, and A. Li, et al. 2023. Distributed Quantum Learning with co-Management in a Multi-tenant Quantum System. In IEEE International Conference on Big Data (BigData 2023), December 15-18, 2023, Sorrento, Italy, 221-228. Piscataway, New Jersey:IEEE. PNNL-SA-192282. doi:10.1109/BigData59044.2023.10386676