Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliders

TL;DR

This paper introduces tensor network-based quantum-inspired algorithms for real-time anomaly detection in collider detectors, demonstrating their feasibility for deployment in resource-constrained environments.

Contribution

It develops a spaced matrix product operator (SMPO) and a cascaded SMPO architecture for efficient, real-time anomaly detection suitable for edge hardware in particle colliders.

Findings

SMPO achieves sensitivity to beyond Standard Model benchmarks

SMPO can be implemented on FPGA hardware with suitable resources

Cascaded SMPO offers increased flexibility and efficiency

Abstract

Quantum machine learning offers the ability to capture complex correlations in high-dimensional feature spaces, crucial for the challenge of detecting beyond the Standard Model physics in collider events, along with the potential for unprecedented computational efficiency in future quantum processors. Near-term utilization of these benefits can be achieved by developing quantum-inspired algorithms for deployment in classical hardware to enable applications at the "edge" of current scientific experiments. This work demonstrates the use of tensor networks for real-time anomaly detection in collider detectors. A spaced matrix product operator (SMPO) is developed that provides sensitivity to a variety beyond the Standard Model benchmarks, and can be implemented in field programmable gate array hardware with resources and latency consistent with trigger deployment. The cascaded SMPO architecture is introduced as an SMPO variation that affords greater flexibility and efficiency in ways that are key to edge applications in resource-constrained environments. These results reveal the benefit and near-term feasibility of deploying quantum-inspired ML in high energy colliders.