  Quantum Machine Learning (QML) is an accelerating field of study that
leverages the principles of quantum computing to enhance and innovate within
machine learning methodologies. However, Noisy Intermediate-Scale Quantum
(NISQ) computers suffer from noise that corrupts the quantum states of the
qubits and affects the training and inferencing accuracy. Furthermore, quantum
computers have long access queues. A single execution with a pre-defined number
of shots can take hours just to reach the top of the wait queue, which is
especially disadvantageous to Quantum Machine Learning (QML) algorithms that
are iterative in nature. Many vendors provide access to a suite of quantum
hardware with varied qubit technologies, number of qubits, coupling
architectures, and noise characteristics. However, present QML algorithms do
not use them for the training procedure and often rely on local noiseless/noisy
simulators due to cost and training timing overhead on real hardware.
Additionally, inferencing is generally performed on reduced datasets with fewer
datapoints. Taking these constraints into account, we perform a study to
maximize the inferencing performance of QML workloads based on the choice of
hardware selection. Specifically, we perform a detailed analysis of quantum
classifiers (both training and inference through the lens of hardware queue
wait times) on Iris and reduced Digits datasets under noise and varied
conditions such as different hardware and coupling maps. We show that using
multiple readily available hardware for training rather than relying on a
single hardware, especially if it has a long queue depth of pending jobs, can
lead to a performance impact of only 3-4% while providing up to 45X reduction
in training wait time.
