A hardware-realizable tunable partial-SWAP is introduced to control the rate of memory dissipation in recurrent quantum reservoir computing architectures, validated via simulation and IBM QPUs.
Feedback-enhanced quantum reservoir com- puting with weak measurements
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Recurrent quantum feature maps achieve lower mean squared error than echo state networks and multilayer perceptrons on Mackey-Glass prediction using compact quantum circuits.
Spin-network quantum reservoirs with finite entanglement and coherence are more resilient to statistical noise from finite measurements than unentangled incoherent ones.
citing papers explorer
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Controllable Quantum Memory Capacity in Quantum Reservoir Networks with Tunable partial-SWAPs
A hardware-realizable tunable partial-SWAP is introduced to control the rate of memory dissipation in recurrent quantum reservoir computing architectures, validated via simulation and IBM QPUs.
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Recurrent Quantum Feature Maps for Reservoir Computing
Recurrent quantum feature maps achieve lower mean squared error than echo state networks and multilayer perceptrons on Mackey-Glass prediction using compact quantum circuits.
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Quantumness can enhance resilience to statistical noise in spin-network quantum reservoir computing
Spin-network quantum reservoirs with finite entanglement and coherence are more resilient to statistical noise from finite measurements than unentangled incoherent ones.