  Quantum computers are the next evolution of computing hardware. Quantum
devices are being exposed through the same familiar cloud platforms used for
classical computers, and enabling seamless execution of hybrid applications
that combine quantum and classical components. Quantum devices vary in
features, e.g., number of qubits, quantum volume, CLOPS, noise profile, queuing
delays and resource cost. So, it may be useful to split hybrid workloads with
either large quantum circuits or large number of quantum circuits, into smaller
units. In this paper, we profile two workload splitting techniques on IBM's
Quantum Cloud: (1) Circuit parallelization, to split one large circuit into
multiple smaller ones, and (2) Data parallelization to split a large number of
circuits run on one hardware to smaller batches of circuits run on different
hardware. These can improve the utilization of heterogenous quantum hardware,
but involve trade-offs. We evaluate these techniques on two key algorithmic
classes: Variational Quantum Eigensolver (VQE) and Quantum Support Vector
Machine (QSVM), and measure the impact on circuit execution times, pre- and
post-processing overhead, and quality of the result relative to a baseline
without parallelization. Results are obtained on real hardware and complemented
by simulations. We see that (1) VQE with circuit cutting is ~39\% better in
ground state estimation than the uncut version, and (2) QSVM that combines data
parallelization with reduced feature set yields upto 3x improvement in quantum
workload execution time and reduces quantum resource use by 3x, while providing
comparable accuracy. Error mitigation can improve the accuracy by ~7\% and
resource foot-print by ~4\% compared to the best case among the considered
scenarios.
