  The public access to noisy intermediate-scale quantum (NISQ) computers
facilitated by IBM, Rigetti, D-Wave, etc., has propelled the development of
quantum applications that may offer quantum supremacy in the future large-scale
quantum computers. Parameterized quantum circuits (PQC) have emerged as a major
driver for the development of quantum routines that potentially improve the
circuit's resilience to the noise. PQC's have been applied in both generative
(e.g. generative adversarial network) and discriminative (e.g. quantum
classifier) tasks in the field of quantum machine learning. PQC's have been
also considered to realize high fidelity quantum gates with the available
imperfect native gates of a target quantum hardware. Parameters of a PQC are
determined through an iterative training process for a target noisy quantum
hardware. However, temporal variations in qubit quality metrics affect the
performance of a PQC. Therefore, the circuit that is trained without
considering temporal variations exhibits poor fidelity over time. In this
paper, we present training methodologies for PQC in a completely classical
environment that can improve the fidelity of the trained PQC on a target NISQ
hardware by as much as 42.5%.

Emerging Technologies