  The rapid advancement of quantum computing has pushed classical designs into
the quantum domain, breaking physical boundaries for computing-intensive and
data-hungry applications. Given its immense potential, quantum-based computing
systems have attracted increasing attention 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.
