Classical and quantum reservoir computing: development and applications in machine learning

• URL: http://arxiv.org/abs/2310.07455v1
• Date: Wed, 11 Oct 2023 13:01:05 GMT
• Title: Classical and quantum reservoir computing: development and applications in machine learning
• Authors: Laia Domingo
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reservoir computing is a novel machine learning algorithm that uses a nonlinear dynamical system to efficiently learn complex temporal patterns from data. The objective of this thesis is to investigate the principles of reservoir computing and develop state-of-the-art variants capable of addressing diverse applications in machine learning. The research demonstrates the algorithm's robustness and adaptability across very different domains, including agricultural time series forecasting and the time propagation of quantum systems. The first contribution of this thesis consists in developing a reservoir computing-based methodology to predict future agricultural product prices, which is crucial for ensuring the sustainability of the food market. The next contribution of the thesis is devoted to solving the Schr\"odinger equation for complex quantum systems. A novel reservoir computing framework is proposed to efficiently propagate quantum wavefunctions in time, enabling the computation of all eigenstates of a quantum system within a specific energy range. This approach is used to study prominent systems in the field of quantum chemistry and quantum chaos. The last contribution of this thesis focuses on optimizing algorithm designs for quantum reservoir computing. The results demonstrate that families of quantum circuits with higher complexity, according to the majorization criterion, yield superior performance in quantum machine learning. Moreover, the impact of quantum noise on the algorithm performance is evaluated, revealing that the amplitude damping noise can actually be beneficial for the performance of quantum reservoir computing, while the depolarizing and phase damping noise should be prioritized for correction. Furthermore, the optimal design of quantum reservoirs is employed to construct a hybrid quantum-classical neural network that tackles a fundamental problem in drug design.

Related papers

• Comprehensive Survey of QML: From Data Analysis to Algorithmic Advancements [2.5686697584463025]
  Quantum Machine Learning represents a paradigm shift at the intersection of Quantum Computing and Machine Learning. The field faces significant challenges, including hardware constraints, noise, and limited qubit coherence. This survey aims to provide a foundational resource for advancing Quantum Machine Learning toward practical, real-world applications.
  arXiv  Detail & Related papers  (2025-01-16T13:25:49Z)
• 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)
• Large-scale quantum reservoir learning with an analog quantum computer [45.21335836399935]
  We develop a quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning.
  arXiv  Detail & Related papers  (2024-07-02T18:00:00Z)
• Quantum reservoir complexity by Krylov evolution approach [0.0]
  We introduce a precise quantitative method, with strong physical foundations based on the Krylov evolution, to assess the wanted good performance in machine learning tasks. Our results show that the Krylov approach to complexity strongly correlates with quantum reservoir performance, making it a powerful tool in the quest for optimally designed quantum reservoirs.
  arXiv  Detail & Related papers  (2023-10-01T21:06:25Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• Configured Quantum Reservoir Computing for Multi-Task Machine Learning [24.698475208639586]
  We explore the dynamics of programmable NISQ devices for quantum reservoir computing. A single configured quantum reservoir can simultaneously learn multiple tasks. We highlight the unique role of quantum coherence in the quantum reservoir, which underpins its exceptional learning performance.
  arXiv  Detail & Related papers  (2023-03-30T18:00:02Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• 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)
• 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)
• Resource-efficient encoding algorithm for variational bosonic quantum simulations [0.0]
  In the Noisy Intermediate Scale Quantum (NISQ) era of quantum computing, quantum resources are limited. We present a resource-efficient quantum algorithm for bosonic ground and excited state computations.
  arXiv  Detail & Related papers  (2021-02-23T19:00:05Z)
• An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
  We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem. The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
  arXiv  Detail & Related papers  (2020-05-06T14:18:44Z)

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.