Speaker
Description
The data input rates foreseen in High-Luminosity LHC (circa 2026) and High-Energy LHC (2030s) High Energy Physics (HEP) experiments impose new challenging requirements on data processing. Polynomial algorithmic complexity and other limitations of classical approaches to many central HEP problems induce searches for alternative solutions featuring better scalability, higher performance and efficiency. For certain types of problems, the Quantum Computing paradigm can offer such asymmetrical-response solutions. We discuss the potential of quantum pattern recognition in the context of ATLAS data processing. In particular, we examine Quantum Associative Memory (QuAM) – a quantum variant of content-addressable memory based on quantum storage medium and two quantum algorithms for content handling. We examine the limits of storage capacity, as well as store and recall efficiencies, from the viewpoints of state-of-the-art quantum hardware and ATLAS real-time charged track pattern recognition requirements. We present QuAM simulations performed on LIQUi|> - the Microsoft’s Quantum Simulator toolsuite. We also review several difficulties integrating the end-to-end quantum pattern recognition into a real-time production workflow, and discuss possible mitigations.
