Nonlinear input transformations are ubiquitous in quantum reservoir computing

• URL: http://arxiv.org/abs/2107.00147v1
• Date: Wed, 30 Jun 2021 23:08:21 GMT
• Title: Nonlinear input transformations are ubiquitous in quantum reservoir computing
• Authors: L. C. G. Govia, G. J. Ribeill, G. E. Rowlands, and T. A. Ohki
• Abstract summary: We study the input encoding component of contemporary quantum reservoir computing schemes. We find that across the majority of schemes the input encoding implements a nonlinear transformation on the input data. Our findings will impact the design of future quantum reservoirs.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The nascent computational paradigm of quantum reservoir computing presents an attractive use of near-term, noisy-intermediate-scale quantum processors. To understand the potential power and use cases of quantum reservoir computing, it is necessary to define a conceptual framework to separate its constituent components and determine their impacts on performance. In this manuscript, we utilize such a framework to isolate the input encoding component of contemporary quantum reservoir computing schemes. We find that across the majority of schemes the input encoding implements a nonlinear transformation on the input data. As nonlinearity is known to be a key computational resource in reservoir computing, this calls into question the necessity and function of further, post-input, processing. Our findings will impact the design of future quantum reservoirs, as well as the interpretation of results and fair comparison between proposed designs.

Related papers

• Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms [50.573955644831386]
  Quantum-enhanced Computer Vision (QeCV) is a new research field at the intersection of computer vision, machine learning and quantum computing.<n>It has high potential to transform how visual signals are processed and interpreted with the help of quantum computing.<n>This survey contributes to the existing literature on QeCV with a holistic review of this research field.
  arXiv  Detail & Related papers  (2025-10-08T17:59:51Z)
• 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)
• An Efficient Quantum Classifier Based on Hamiltonian Representations [50.467930253994155]
  Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks. We propose an efficient approach that circumvents the costs associated with data encoding by mapping inputs to a finite set of Pauli strings. We evaluate our approach on text and image classification tasks, against well-established classical and quantum models.
  arXiv  Detail & Related papers  (2025-04-13T11:49:53Z)
• Expressivity Limits of Quantum Reservoir Computing [0.0]
  We establish a formal connection to parametrized quantum circuit quantum machine learning (PQC-QML)<n>We analytically prove, and numerically corroborate, that in QRC the number of non-linear functions that can be generated from classical data is bounded linearly by the number of input encoding gates.<n>Our results challenge the common assumption that exponential Hilbert space scaling confers a corresponding computational advantage in QRC.
  arXiv  Detail & Related papers  (2025-01-26T13:43:03Z)
• Input-dependence in quantum reservoir computing [4.903263899016404]
  Quantum reservoir computing is an emergent field in which quantum dynamical systems are exploited for temporal information processing. In previous work, it was found a feature that makes a quantum reservoir valuable: contractive dynamics of the quantum reservoir channel toward input-dependent fixed points. This work contributes to analyzing valuable quantum reservoirs in terms of their input dependence.
  arXiv  Detail & Related papers  (2024-12-11T11:59:11Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• The curse of random quantum data [62.24825255497622]
  We quantify the performances of quantum machine learning in the landscape of quantum data. We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits. Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
  arXiv  Detail & Related papers  (2024-08-19T12:18:07Z)
• Classical and quantum reservoir computing: development and applications in machine learning [0.0]
  Reservoir computing is a novel machine learning algorithm that uses a nonlinear dynamical system to learn complex temporal patterns from data. The research demonstrates the algorithm's robustness and adaptability across very different domains, including agricultural time series forecasting. The last contribution of this thesis focuses on optimizing algorithm designs for quantum reservoir computing.
  arXiv  Detail & Related papers  (2023-10-11T13:01:05Z)
• Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits [77.04894470683776]
  We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production. We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-09-11T18:00:00Z)
• The Basis of Design Tools for Quantum Computing: Arrays, Decision Diagrams, Tensor Networks, and ZX-Calculus [55.58528469973086]
  Quantum computers promise to efficiently solve important problems classical computers never will. A fully automated quantum software stack needs to be developed. This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
  arXiv  Detail & Related papers  (2023-01-10T19:00:00Z)
• Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
  We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
  arXiv  Detail & Related papers  (2022-04-12T19:39:31Z)
• 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)
• Natural quantum reservoir computing for temporal information processing [4.785845498722406]
  Reservoir computing is a temporal information processing system that exploits artificial or physical dissipative dynamics. This paper proposes the use of real superconducting quantum computing devices as the reservoir, where the dissipative property is served by the natural noise added to the quantum bits.
  arXiv  Detail & Related papers  (2021-07-13T01:58:57Z)
• Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems [0.0]
  Quantum reservoir computing (QRC) with Ising spin networks was introduced as a quantum version of classical reservoir computing. We characterize the performance of the spin-based QRC model with the Information Processing Capacity (IPC) This work establishes a clear picture of the computational capabilities of a quantum network of spins for reservoir computing.
  arXiv  Detail & Related papers  (2020-10-13T13:26:34Z)
• Efficient simulatability of continuous-variable circuits with large Wigner negativity [62.997667081978825]
  Wigner negativity is known to be a necessary resource for computational advantage in several quantum-computing architectures. We identify vast families of circuits that display large, possibly unbounded, Wigner negativity, and yet are classically efficiently simulatable. We derive our results by establishing a link between the simulatability of high-dimensional discrete-variable quantum circuits and bosonic codes.
  arXiv  Detail & Related papers  (2020-05-25T11:03:42Z)
• Temporal Information Processing on Noisy Quantum Computers [3.4180402210147243]
  We propose quantum reservoir computing that harnesses complex dissipative quantum dynamics. Proof-of-principle experiments on remotely accessed cloud-based superconducting quantum computers demonstrate that small and noisy quantum reservoirs can tackle high-order nonlinear temporal tasks. Our results pave the path for attractive temporal processing applications of near-term gate-model quantum computers of increasing fidelity but without quantum error correction.
  arXiv  Detail & Related papers  (2020-01-26T19:00:03Z)

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.