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We have entered the Noisy Intermediate-Scale Quantum Era. A plethora ofquantum processor prototypes allow evaluation of potential of the QuantumComputing paradigm in applications to pressing computational problems of thefuture. Growing data input rates and detector resolution foreseen inHigh-Energy LHC (2030s) experiments expose the often high time and/or spacecomplexity of classical algorithms. Quantum algorithms can potentially becomethe lower-complexity alternatives in such cases. In this work we discuss thepotential of Quantum Associative Memory (QuAM) in the context of LHC datatriggering. We examine the practical limits of storage capacity, as well asstore and recall errorless efficiency, from the viewpoints of thestate-of-the-art IBM quantum processors and LHC real-time charged track patternrecognition requirements. We present a software prototype implementation of theQuAM protocols and analyze the topological limitations for porting the simplestQuAM instances to the public IBM 5Q and 14Q cloud-based superconducting chips.

Quantum Associative Memory in HEP Track Pattern Recognition