A quantum reservoir network using GHZ-state preparation achieves an order-of-magnitude RMSE improvement over prior QRN designs on latent-space prediction of the Kuramoto-Sivashinsky equation.
Feedback-enhanced quantum reservoir com- puting with weak measurements
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abstract
Quantum reservoir computing (QRC) leverages the natural dynamics of quantum systems to process time-series data efficiently, offering a promising approach for near-term quantum devices. Unlike classical reservoir computing, the efficacy of feedback in QRC has not yet been thoroughly explored. Here, we develop a feedback-enhanced QRC framework with weak measurements. Weak measurements preserve information stored in quantum coherence, while feedback enhances nonlinearity and memory capacity. The implementation of our framework assumes an ensemble quantum system, such as nuclear magnetic resonance. Through linear memory and nonlinear forecasting tasks, we show that our model outperforms conventional QRC approaches in many cases. Our proposed protocol achieves superior performance in systems with small measurement errors and low environmental noise. Furthermore, we theoretically demonstrate that feedback of measurement results reinforces the nonlinearity of the reservoir. These findings highlight the potential of feedback-enhanced QRC for next-generation quantum machine learning applications.
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Hybrid quantum-classical reservoir computing enables nonlinear temporal processing of quantum states and outperforms pure quantum or classical reservoirs in both full-tomography and single-axis measurement regimes.
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.
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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Leveraging Metrologically Useful States in Quantum Reservoir Networks
A quantum reservoir network using GHZ-state preparation achieves an order-of-magnitude RMSE improvement over prior QRN designs on latent-space prediction of the Kuramoto-Sivashinsky equation.
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Temporal processing of quantum states with hybrid quantum-classical reservoirs
Hybrid quantum-classical reservoir computing enables nonlinear temporal processing of quantum states and outperforms pure quantum or classical reservoirs in both full-tomography and single-axis measurement regimes.
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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.
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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.