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Mar 20 – 22, 2018
University of Washington Seattle
US/Pacific timezone

Quantum Pattern Recognition for High-Luminosity Era

Mar 22, 2018, 9:00 AM
Physics-Astronomy Auditorium A118 (University of Washington Seattle)

Physics-Astronomy Auditorium A118

University of Washington Seattle

Oral 2: Real-time pattern recognition and fast tracking Session5


Illya Shapoval (Lawrence Berkeley National Laboratory)


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.

Primary authors

Illya Shapoval (Lawrence Berkeley National Laboratory) Paolo Calafiura (Lawrence Berkeley National Lab. (US))

Presentation materials