Memory-Nonlinearity Trade-off across Quantum Reservoir Computing Frameworks

• URL: http://arxiv.org/abs/2603.21371v1
• Date: Sun, 22 Mar 2026 19:06:36 GMT
• Title: Memory-Nonlinearity Trade-off across Quantum Reservoir Computing Frameworks
• Abstract summary: Quantum reservoir computing harnesses quantum dynamics for time-series processing.<n>We show that apparently unrelated approaches-including memory restriction-are governed by the same underlying principle.
• Score: 0.9786690381850356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum reservoir computing (QRC) harnesses driven quantum dynamics for time-series processing, yet the mechanisms behind the differing performance levels across its many implementations remain unclear. We show that apparently unrelated approaches-including memory restriction, weak measurements, operation near the edge of quantum chaos, and dissipative dynamics-are in fact governed by the same underlying principle, namely a tunable balance between memory retention and nonlinear response. Using the information processing capacity, a dynamical measure from nonlinear systems theory, we place these behaviors in a unified framework and identify the regimes in which quantum reservoirs surpass the standard protocol. Our results reveal a fundamental connection between memory and nonlinear response. This provides a general design principle for enhanced information processing and enables systematic analysis and optimization inspired by classical dynamical quantifiers.

Related papers

• Learnable yet not simulable: a quantum resource theory of learning models [57.695822250391906]
  It remains unclear which quantum resource governs the classical learnability of quantum circuits.<n>We study the expectation-value functions of families of tunable quantum circuits.<n>These results establish a quantitative resource-theoretic framework for delineating the boundary between classical simulation and learning.
  arXiv  Detail & Related papers  (2026-08-03T14:48:03Z)
• General theory of monitored Quantum Reservoir Computing [0.47248250311484113]
  We develop a theory of monitored quantum reservoir computing based on indirect quantum measurements.<n>We derive general criteria under which monitored dynamics satisfy the echo-state property, fading memory, and input separability.
  arXiv  Detail & Related papers  (2026-07-15T19:18:07Z)
• Geometric Approach to Zero-Memory Quantum Dot Reservoir Computing [0.11999555634662634]
  Physical reservoir computing offers an energy-efficient alternative to conventional neural networks.<n>We show that memory capacity can be engineered extrinsically in memoryless systems.<n>We show that memoryless quantum reservoir can achieve high performance on both chaotic Mackey-Glass future prediction and nonlinear transformation tasks.
  arXiv  Detail & Related papers  (2026-06-28T10:26:43Z)
• Dynamical learning and quantum memory with non-Hermitian many-body systems [37.69303106863453]
  Non-Hermitian (NH) systems provide a fertile platform for quantum technologies.<n>We investigate this relationship using the example of an interacting NH spin system defined on random graphs.<n>We show that the onset of the first exceptional point corresponds to an abrupt change in the system's learning capacity.
  arXiv  Detail & Related papers  (2025-06-09T11:58:40Z)
• Minimal Quantum Reservoirs with Hamiltonian Encoding [72.27323884094953]
  We investigate a minimal architecture for quantum reservoir computing based on Hamiltonian encoding.<n>This approach circumvents many of the experimental overheads typically associated with quantum machine learning.
  arXiv  Detail & Related papers  (2025-05-28T16:50:05Z)
• Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems [49.819436680336786]
  We propose an efficient transformed Gaussian process state-space model (ETGPSSM) for scalable and flexible modeling of high-dimensional, non-stationary dynamical systems.<n>Specifically, our ETGPSSM integrates a single shared GP with input-dependent normalizing flows, yielding an expressive implicit process prior that captures complex, non-stationary transition dynamics.<n>Our ETGPSSM outperforms existing GPSSMs and neural network-based SSMs in terms of computational efficiency and accuracy.
  arXiv  Detail & Related papers  (2025-03-24T03:19:45Z)
• Feedback-enhanced quantum reservoir computing with weak measurements [1.474723404975345]
  We develop a feedback-enhanced quantum reservoir computing framework.<n>We show that our model outperforms conventional QRC approaches in many cases.<n>These findings highlight the potential of feedback-enhanced QRC for next-generation quantum machine learning applications.
  arXiv  Detail & Related papers  (2025-03-23T04:47:06Z)
• Quantum reservoir computing on random regular graphs [0.0]
  Quantum reservoir computing (QRC) is a low-complexity learning paradigm that combines input-driven many-body quantum systems with classical learning techniques.<n>We study information localization, dynamical quantum correlations, and the many-body structure of the disordered Hamiltonian.<n>Our findings thus provide guidelines for the optimal design of disordered analog quantum learning platforms.
  arXiv  Detail & Related papers  (2024-09-05T16:18:03Z)
• Theoretical framework for quantum associative memories [0.8437187555622164]
  Associative memory refers to the ability to relate a memory with an input and targets the restoration of corrupted patterns. We develop a comprehensive framework for a quantum associative memory based on open quantum system dynamics.
  arXiv  Detail & Related papers  (2024-08-26T13:46:47Z)
• Structure-Preserving Learning Using Gaussian Processes and Variational Integrators [62.31425348954686]
  We propose the combination of a variational integrator for the nominal dynamics of a mechanical system and learning residual dynamics with Gaussian process regression. We extend our approach to systems with known kinematic constraints and provide formal bounds on the prediction uncertainty.
  arXiv  Detail & Related papers  (2021-12-10T11:09:29Z)
• Physical reservoir computing using finitely-sampled quantum systems [0.0]
  Reservoir computing exploits the nonlinear dynamics of a physical reservoir to perform complex time-series processing tasks. Here we describe a framework for reservoir computing with nonlinear quantum reservoirs under continuous measurement.
  arXiv  Detail & Related papers  (2021-10-26T16:46:14Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• On dissipative symplectic integration with applications to gradient-based optimization [77.34726150561087]
  We propose a geometric framework in which discretizations can be realized systematically. We show that a generalization of symplectic to nonconservative and in particular dissipative Hamiltonian systems is able to preserve rates of convergence up to a controlled error.
  arXiv  Detail & Related papers  (2020-04-15T00:36:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.