arXiv AI
Digital Twin Modeling of Quantum Dynamical Systems: Dissipative Quantum Reservoir Computing
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Quantum Reservoir Computing: Recent Advances and Future Directions
arXiv:2607. 18552v1 Announce Type: cross Abstract: Quantum reservoir computing (QRC) uses the dynamics of a fixed or weakly tuned quantum system to transform temporal and sequential inputs into measured features, while training is typically confined to a classical readout.
arXiv Machine Learning
Jul 21
A Novel Hybrid Quantum Reservoir Computing (nHQRC) for Phase Transition Detection in Non-Equilibrium Dynamical Systems
arXiv:2607. 16281v1 Announce Type: cross Abstract: The analysis of highly non-linear stochastic data within non-equilibrium dynamical systems requires computational frameworks capable of detecting latent phase transitions before systemic structural breakdowns occur.
arXiv Machine Learning
Jul 10
A Quantum Reservoir Architecture for Chaotic Forecasting and a Test of Whether Its High Dimension Helps
arXiv:2607. 07978v1 Announce Type: cross Abstract: Quantum reservoir computing uses a fixed quantum circuit as a feature generator and trains only a simple linear readout on top of it.
Hugging Face Trending Papers
Jul 8
A Quantum Reservoir Architecture for Chaotic Forecasting and a Test of Whether Its High Dimension Helps
Quantum reservoir computing uses a fixed quantum circuit as a feature generator and trains only a simple linear readout on top of it. This makes it cheap to train and free of the optimisation problems that affect many quantum machine-learning models.
arXiv AI
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Controllable Quantum Memory Capacity in Quantum Reservoir Networks with Tunable partial-SWAPs
arXiv:2605. 12713v3 Announce Type: replace-cross Abstract: In the field of quantum reservoir computing (QRC), many different computational models and architectures have been proposed.
arXiv Machine Learning
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Task-Resolved Fisher Spectroscopy for Quantum Reservoir Computing
The paper introduces task‑resolved Fisher spectroscopy for quantum reservoir computing, defining orthonormal score coordinates from prediction targets that reweight labeled histories to produce an affine family of reservoir states and measurement outcomes. It establishes a Fisher‑information hierarchy linking state quantum Fisher information, measurement record Fisher information, and moment matrices up to many‑body order, providing a quadratic form that equals the stationary capacity of the optimal linear readout. The method requires only stationary labeled records and measured outcomes, enabling predictions of held‑out capacities, necessary feature order, and measurement‑budget dependence, and demonstrates how optimizing local measurement axes can recover hidden task information in a five‑spin open reservoir.