  Quantum circuit execution is the central task in quantum computation. Due to
inherent quantum-mechanical constraints, quantum computing workflows often
involve a considerable number of independent measurements over a large set of
slightly different quantum circuits. Here we discuss a simple model for
parallelizing simulation of such quantum circuit executions that is based on
introducing a large array of virtual quantum processing units, mapped to
classical HPC nodes, as a parallel quantum computing platform. Implemented
within the XACC framework, the model can readily take advantage of its
backend-agnostic features, enabling parallel quantum circuit execution over any
target backend supported by XACC. We illustrate the performance of this
approach by demonstrating strong scaling in two pertinent domain science
problems, namely in computing the gradients for the multi-contracted
variational quantum eigensolver and in data-driven quantum circuit learning,
where we vary the number of qubits and the number of circuit layers. The latter
(classical) simulation leverages the cuQuantum SDK library to run efficiently
on GPU-accelerated HPC platforms.
