Agnostic benchmarking through Quantum Amplitude Estimation

Quantum technology is advancing rapidly, particularly Noisy Intermediate Scale Quantum (NISQ) systems. With their increasing accessibility and industry interest, there’s a need to measure their performance, especially in the presence of non-Markovian noise. Currently, error correction methods are in development, and information on Quantum Processing Unit (QPU) performance is scarce. Our research addresses this by exploring “agnostic benchmarking.” We assess various quantum hardware platforms using a specialized technique. We focus on the Quantum Amplitude Estimation (QAE) algorithm, relevant in finance. By testing it on different quantum hardware, we track indicators like Kullback-Leibler divergence and Grover iteration fidelity as complexity grows, facilitating fair comparisons. We also study how different hardware affects QAE performance, providing insights into circuit design’s influence. Our goal is to develop benchmarking techniques for informed decisions on quantum hardware and algorithm deployment.

Faculty Supervisor:

Roman Krems;Olivia Di Matteo

Student:

Partner:

Resonance Alliance

Discipline:

Physics

Sector:

Information and cultural industries

University:

The University of British Columbia

Program: