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 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.
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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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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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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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.