Sampling on NISQ Devices: "Who's the Fairest One of All?"

TL;DR

This paper evaluates the fairness of ground state sampling on various NISQ quantum devices, revealing current limitations in achieving fair sampling and highlighting the impact of compilation techniques on performance.

Contribution

It provides an empirical analysis of fair sampling on multiple NISQ hardware platforms, comparing quantum annealing and gate-based algorithms, and discusses software stack influences.

Findings

NISQ devices do not yet achieve fair sampling.

Differences observed across hardware and software stacks.

Compilation techniques significantly affect sampling fairness.

Abstract

Modern NISQ devices are subject to a variety of biases and sources of noise that degrade the solution quality of computations carried out on these devices. A natural question that arises in the NISQ era, is how fairly do these devices sample ground state solutions. To this end, we run five fair sampling problems (each with at least three ground state solutions) that are based both on quantum annealing and on the Grover Mixer-QAOA algorithm for gate-based NISQ hardware. In particular, we use seven IBM~Q devices, the Aspen-9 Rigetti device, the IonQ device, and three D-Wave quantum annealers. For each of the fair sampling problems, we measure the ground state probability, the relative fairness of the frequency of each ground state solution with respect to the other ground state solutions, and the aggregate error as given by each hardware provider. Overall, our results show that NISQ devices do not achieve fair sampling yet. We also observe differences in the software stack with a particular focus on compilation techniques that illustrate what work will still need to be done to achieve a seamless integration of frontend (i.e. quantum circuit description) and backend compilation.