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.
Quantum reservoir computing with repeated measurements on superconducting devices
12 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
12
Pith papers citing it
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external citations · Pith
abstract
Reservoir computing is a machine learning framework that uses artificial or physical dissipative dynamics to predict time-series data using nonlinearity and memory properties of dynamical systems. Quantum systems are considered as promising reservoirs, but the conventional quantum reservoir computing (QRC) models have problems in the execution time. In this paper, we develop a quantum reservoir (QR) system that exploits repeated measurement to generate a time-series, which can effectively reduce the execution time. We experimentally implement the proposed QRC on the IBM's quantum superconducting device and show that it achieves higher accuracy as well as shorter execution time than the conventional QRC method. Furthermore, we study the temporal information processing capacity to quantify the computational capability of the proposed QRC; in particular, we use this quantity to identify the measurement strength that best tradeoffs the amount of available information and the strength of dissipation. An experimental demonstration with soft robot is also provided, where the repeated measurement over 1000 timesteps was effectively applied. Finally, a preliminary result with 120 qubits device is discussed.
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quant-ph 12representative citing papers
A new protocol for online amplitude-encoded quantum reservoir computing is proposed that uses mid-circuit measurement and reset to implement partial-trace dynamics and indirect measurements for observables.
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.
Tensor-network frontends compress inputs for MPC-secured federated learning, after which a quantum-enhanced processor refines the aggregated latent features, with TTN+QEP showing the most balanced performance on PneumoniaMNIST.
Quantum information lifetime scales exponentially with system size under continuous monitoring via mid-circuit measurements, proven analytically for Haar random unitaries and confirmed numerically and experimentally, unlike the linear scaling without monitoring.
Quantum reservoir computing using a fully connected transverse-field Ising model with input and memory qubits outperforms econometric and standard ML benchmarks in realized volatility forecasting.
Indirect measurements in quantum reservoir computing improve execution time scaling, overall performance, and memory capacity over projective measurements and classical feedback methods.
A quantum echo-state network is implemented on NISQ superconducting qubits and shown to predict long chaotic trajectories from the Lorenz system with memory persisting over 100 times the median T1/T2 time.
Hybrid quantum reservoir and projected kernel models report 37-62% MAE reductions versus classical baselines for multi-output energy time-series on NISQ hardware and simulators.
A parametrized four-qubit digital QRC model with ridge-regression readout matches the classical Prophet benchmark on dynamic time warping for ATM cash-demand forecasting but underperforms on MAE and NMSE across noiseless simulation, noise emulation, and real IQM hardware.
Qiskit is an open-source SDK that supports quantum circuit design, optimization at multiple abstraction levels, execution on hardware, and dynamic quantum-classical computations.
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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Measurement-enabled online quantum processing with amplitude encoding
A new protocol for online amplitude-encoded quantum reservoir computing is proposed that uses mid-circuit measurement and reset to implement partial-trace dynamics and indirect measurements for observables.
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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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Quantum-Enhanced Processing with Tensor-Network Frontends for Privacy-Aware Federated Medical Diagnosis
Tensor-network frontends compress inputs for MPC-secured federated learning, after which a quantum-enhanced processor refines the aggregated latent features, with TTN+QEP showing the most balanced performance on PneumoniaMNIST.
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Scaling Laws of Quantum Information Lifetime in Monitored Quantum Dynamics
Quantum information lifetime scales exponentially with system size under continuous monitoring via mid-circuit measurements, proven analytically for Haar random unitaries and confirmed numerically and experimentally, unlike the linear scaling without monitoring.
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Quantum Reservoir Computing for Realized Volatility Forecasting
Quantum reservoir computing using a fully connected transverse-field Ising model with input and memory qubits outperforms econometric and standard ML benchmarks in realized volatility forecasting.
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Harnessing quantum back-action for time-series processing
Indirect measurements in quantum reservoir computing improve execution time scaling, overall performance, and memory capacity over projective measurements and classical feedback methods.
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Quantum Observers: A NISQ Hardware Demonstration of Chaotic State Prediction Using Quantum Echo-state Networks
A quantum echo-state network is implemented on NISQ superconducting qubits and shown to predict long chaotic trajectories from the Lorenz system with memory persisting over 100 times the median T1/T2 time.
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Hybrid Quantum-Classical Machine Learning Algorithms for Multi-Output Time-Series Forecasting at Utility Scale
Hybrid quantum reservoir and projected kernel models report 37-62% MAE reductions versus classical baselines for multi-output energy time-series on NISQ hardware and simulators.
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Digital Quantum Reservoir Computing for ATM Time Series Prediction
A parametrized four-qubit digital QRC model with ridge-regression readout matches the classical Prophet benchmark on dynamic time warping for ATM cash-demand forecasting but underperforms on MAE and NMSE across noiseless simulation, noise emulation, and real IQM hardware.
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Quantum computing with Qiskit
Qiskit is an open-source SDK that supports quantum circuit design, optimization at multiple abstraction levels, execution on hardware, and dynamic quantum-classical computations.