Noise-Aware Qubit Allocation Techniques for NISQ Devices

With a growing diversity in devices, control systems, topologies, programming languages, and applications, computation in the NISQ era needs to be navigated through adaptable cloud-based software. In order to provide the highest fidelity results to users, it is essential that this software employs hardware-aware optimizations at all levels of the stack, both in the pre-processing and post-processing stages. We present our work in pre-processing error mitigation through variation-aware qubit allocation techniques for gate-based quantum computers, with a focus on superconducting platforms. We formulate a description of the “allocation problem” and propose several solutions: a deterministic algorithm for finding the optimal solution as well as a more scalable and flexible randomized heuristic approach. We will present and validate the implications of these different techniques on various NISQ devices.