  Variational quantum algorithms (VQAs) provide a promising approach to achieve
quantum advantage in the noisy intermediate-scale quantum era. In this era,
quantum computers experience high error rates and quantum error detection and
correction is not feasible. VQAs can utilize noisy qubits in tandem with
classical optimization algorithms to solve hard problems. However, VQAs are
still slow relative to their classical counterparts. Hence, improving the
performance of VQAs will be necessary to make them competitive. While VQAs are
expected perform better as the problem sizes increase, increasing their
performance will make them a viable option sooner. In this work we show that
circuit-level concurrency provides a means to increase the performance of
variational quantum algorithms on noisy quantum computers. This involves
mapping multiple instances of the same circuit (program) onto the quantum
computer at the same time, which allows multiple samples in a variational
quantum algorithm to be gathered in parallel for each training iteration. We
demonstrate that this technique provides a linear increase in training speed
when increasing the number of concurrently running quantum circuits.
Furthermore, even with pessimistic error rates concurrent quantum circuit
sampling can speed up the quantum approximate optimization algorithm by up to
20x with low mapping and run time overhead.
