Introduces tunable partial-SWAP for controllable memory capacity in quantum reservoir networks, modeled as controlled amplitude-damping and validated via STMC and NARMA-5 benchmarks on simulators 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
Introduces tunable partial-SWAP for controllable memory capacity in quantum reservoir networks, modeled as controlled amplitude-damping and validated via STMC and NARMA-5 benchmarks on simulators 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.