QAOA-based Fair Sampling on NISQ Devices

by   John Golden, et al.

We study the status of fair sampling on Noisy Intermediate Scale Quantum (NISQ) devices, in particular the IBM Q family of backends. Using the recently introduced Grover Mixer-QAOA algorithm for discrete optimization, we generate fair sampling circuits to solve six problems of varying difficulty, each with several optimal solutions, which we then run on ten different backends available on the IBM Q system. For a given circuit evaluated on a specific set of qubits, we evaluate: how frequently the qubits return an optimal solution to the problem, the fairness with which the qubits sample from all optimal solutions, and the reported hardware error rate of the qubits. To quantify fairness, we define a novel metric based on Pearson's χ^2 test. We find that fairness is relatively high for circuits with small and large error rates, but drops for circuits with medium error rates. This indicates that structured errors dominate in this regime, while unstructured errors, which are random and thus inherently fair, dominate in noisier qubits and longer circuits. Our results provide a simple, intuitive means of quantifying fairness in quantum circuits, and show that reducing structured errors is necessary to improve fair sampling on NISQ hardware.


page 17

page 18


Layerwise learning for quantum neural networks

With the increased focus on quantum circuit learning for near-term appli...

Compiling Quantum Circuits to Realistic Hardware Architectures using Temporal Planners

To run quantum algorithms on emerging gate-model quantum hardware, quant...

An Algebraic Quantum Circuit Compression Algorithm for Hamiltonian Simulation

Quantum computing is a promising technology that harnesses the peculiari...

Proportionally Fair approach for Tor's Circuits Scheduling

The number of users adopting Tor to protect their online privacy is incr...

High-Round QAOA for MAX k-SAT on Trapped Ion NISQ Devices

The Quantum Alternating Operator Ansatz (QAOA) is a hybrid classical-qua...

Noise and the frontier of quantum supremacy

Noise is the defining feature of the NISQ era, but it remains unclear if...

A Resolution in Algorithmic Fairness: Calibrated Scores for Fair Classifications

Calibration and equal error rates are fundamental conditions for algorit...

Please sign up or login with your details

Forgot password? Click here to reset