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Hilbert space as a computational resource in reservoir computing

William D. Kalfus, Guilhem Ribeill, Graham E. Rowlands, Hari Krovi, Thomas Ohki, Luke C. G. Govia

2022Physical Review Research31 citationsDOIOpen Access PDF

Abstract

Accelerating computation with quantum resources is limited by the challenges of high-fidelity control of quantum systems. Reservoir computing presents an attractive alternative, as precise control and full calibration of system dynamics are not required. Instead, complex internal trajectories in a large state space are leveraged as a computational resource. Quantum systems offer a unique venue for reservoir computing, given the presence of interactions unavailable in classical systems and a potentially exponentially-larger computational space. With a reservoir comprised of a single $d$-dimensional quantum system, we demonstrate clear performance improvement with Hilbert space dimension at two benchmark tasks and advantage over the physically analogous classical reservoir. Quantum reservoirs as realized by current-era quantum hardware offer immediate practical implementation and a promising outlook for increased performance in larger systems.

Topics & Concepts

Reservoir computingQuantum computerComputer scienceComputationFidelityQuantumDimension (graph theory)Hilbert spaceBenchmark (surveying)Theoretical computer scienceState spaceDistributed computingResource (disambiguation)Quantum stateComputer engineeringComputational scienceMathematicsAlgorithmPhysicsArtificial intelligenceQuantum mechanicsArtificial neural networkGeographyPure mathematicsRecurrent neural networkTelecommunicationsStatisticsGeodesyComputer networkNeural Networks and Reservoir ComputingAdvanced Memory and Neural ComputingOptical Network Technologies
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