arXiv AI
Aug 12
A Single Atom in Front of a Mirror is a Universal Reservoir Computer
arXiv:2608. 10382v1 Announce Type: cross Abstract: Universal approximation in reservoir computing is typically associated with a class of reservoirs.
A Single Atom in Front of a Mirror is a Universal Reservoir Computer
Universal approximation in reservoir computing is typically associated with a class of reservoirs. We show that universality can be associated with a single reservoir, considering a minimal setup of a single atom in front of a mirror.
Related stories
arXiv Machine Learning
Jul 22
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 AI
Jun 18
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
Jun 2
Quantum Reservoir Computing and Risk Bounds
arXiv:2501. 08640v2 Announce Type: replace Abstract: We propose a way to bound the generalisation errors of several classes of quantum reservoirs using the Rademacher complexity.
arXiv Machine Learning
Jul 14
When Classical Baselines Are Tuned as Carefully as the Quantum Model, Does Quantum Reservoir Computing Still Win?
arXiv:2607. 09905v1 Announce Type: cross Abstract: Can a small quantum computer forecast a changing signal better than an ordinary classical method?
arXiv Machine Learning
Jun 24
Quantum Adaptive Self-Attention for Quantum Transformer Models
arXiv:2504. 05336v4 Announce Type: replace-cross Abstract: A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unclear whether a gain comes from the quantum substrate or from the architectural change that accompanies 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
Jul 23
Hybrid LLM-Guided Search for Quantum Reservoir Architecture Design
arXiv:2607. 19506v1 Announce Type: cross Abstract: Quantum reservoir computing (QRC) uses fixed quantum dynamics as a high-dimensional temporal feature map and trains only a lightweight classical readout.
arXiv Machine Learning
Aug 4
Hybrid Quantum Neural Networks: Theory, Implementations, and Applications
arXiv:2608. 01194v1 Announce Type: cross Abstract: Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues.
arXiv Machine Learning
Jul 30
Shot-based quantum encoding: a data-loading paradigm for quantum neural networks
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
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.