From Hope to Heuristic: Realistic Runtime Estimates for Quantum Optimisation in NHEP

TL;DR

This paper evaluates the practical runtime and scalability of quantum algorithms like QAOA for particle track reconstruction in NHEP, emphasizing heuristic techniques and co-design to enhance quantum advantage prospects.

Contribution

It introduces realistic runtime estimates for quantum optimization algorithms in NHEP and explores heuristic methods to improve resource efficiency and scalability.

Findings

Lower frequency components are key for effective annealing schedules.

Heuristics can achieve near-optimal results with fewer resources.

Co-design approaches are vital for quantum algorithm success in NHEP.

Abstract

Noisy Intermediate-Scale Quantum (NISQ) computers, despite their limitations, present opportunities for near-term quantum advantages in Nuclear and High-Energy Physics (NHEP) when paired with specially designed quantum algorithms and processing units. This study focuses on core algorithms that solve optimisation problems through the quadratic Ising or quadratic unconstrained binary optimisation model, specifically quantum annealing and the Quantum Approximate Optimisation Algorithm (QAOA). In particular, we estimate runtimes and scalability for the task of particle track reconstruction, a key computing challenge in NHEP, and investigate how the classical parameter space in QAOA, along with techniques like a Fourier-analysis based heuristic, can facilitate future quantum advantages. The findings indicate that lower frequency components in the parameter space are crucial for effective annealing schedules, suggesting that heuristics can improve resource efficiency while achieving near-optimal results. Overall, the study highlights the potential of NISQ computers in NHEP and the significance of co-design approaches and heuristic techniques in overcoming challenges in quantum algorithms.