Natural quantum reservoir computing for temporal information processing

• URL: http://arxiv.org/abs/2107.05808v2
• Date: Fri, 4 Mar 2022 10:21:11 GMT
• Title: Natural quantum reservoir computing for temporal information processing
• Authors: Yudai Suzuki, Qi Gao, Ken C. Pradel, Kenji Yasuoka, Naoki Yamamoto
• Abstract summary: 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.
• Score: 4.785845498722406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reservoir computing is a temporal information processing system that exploits artificial or physical dissipative dynamics to learn a dynamical system and generate the target time-series. 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. The performance of this natural quantum reservoir is demonstrated in a benchmark time-series regression problem and a practical problem classifying different objects based on temporal sensor data. In both cases the proposed reservoir computer shows a higher performance than a linear regression or classification model. The results indicate that a noisy quantum device potentially functions as a reservoir computer, and notably, the quantum noise, which is undesirable in the conventional quantum computation, can be used as a rich computation resource.

Related papers

• Evaluation of phase shifts for non-relativistic elastic scattering using quantum computers [39.58317527488534]
  This work reports the development of an algorithm that makes it possible to obtain phase shifts for generic non-relativistic elastic scattering processes on a quantum computer.
  arXiv  Detail & Related papers  (2024-07-04T21:11:05Z)
• Reaction dynamics with qubit-efficient momentum-space mapping [42.408991654684876]
  We study quantum algorithms for response functions, relevant for describing different reactions governed by linear response. We consider a qubit-efficient mapping on a lattice, which can be efficiently performed using momentum-space basis states.
  arXiv  Detail & Related papers  (2024-03-30T00:21:46Z)
• QuantumReservoirPy: A Software Package for Time Series Prediction [44.99833362998488]
  We have developed a software package to allow for quantum reservoirs to fit a common structure. Our package results in simplified development and logical methods of comparison between quantum reservoir architectures.
  arXiv  Detail & Related papers  (2024-01-19T13:31:29Z)
• 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)
• Solving the time-complexity problem and tuning the performance of quantum reservoir computing by artificial memory restriction [0.0]
  The suitability of quantum reservoir computing for solving temporal tasks is hindered by the collapse of the quantum system when measurements are made. We propose artificially restricting the memory of the quantum reservoir by only using a small number inputs. We numerically study the linear and quadratic algorithms for a fully connected transverse Ising model and a quantum processor model.
  arXiv  Detail & Related papers  (2023-06-22T13:37:34Z)
• Noisy Quantum Kernel Machines [58.09028887465797]
  An emerging class of quantum learning machines is that based on the paradigm of quantum kernels. We study how dissipation and decoherence affect their performance. We show that decoherence and dissipation can be seen as an implicit regularization for the quantum kernel machines.
  arXiv  Detail & Related papers  (2022-04-26T09:52:02Z)
• 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)
• Online quantum time series processing with random oscillator networks [0.0]
  Reservoir computing is a powerful machine learning paradigm for online time series processing. We propose a reservoir computing inspired approach to online processing of time series consisting of quantum information.
  arXiv  Detail & Related papers  (2021-08-02T08:06:38Z)
• Continuous-time dynamics and error scaling of noisy highly-entangling quantum circuits [58.720142291102135]
  We simulate a noisy quantum Fourier transform processor with up to 21 qubits. We take into account microscopic dissipative processes rather than relying on digital error models. We show that depending on the dissipative mechanisms at play, the choice of input state has a strong impact on the performance of the quantum algorithm.
  arXiv  Detail & Related papers  (2021-02-08T14:55:44Z)
• 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)
• 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.