Quantum Reservoir Computing Implementations for Classical and Quantum Problems

• URL: http://arxiv.org/abs/2211.08567v2
• Date: Mon, 01 Sep 2025 09:42:42 GMT
• Title: Quantum Reservoir Computing Implementations for Classical and Quantum Problems
• Authors: Adam Burgess, Marian Florescu,
• Abstract summary: Quantum reservoir computing has emerged as a promising paradigm within the field of quantum machine learning.<n>We explore the potential of quantum-inspired machine learning methodologies by leveraging the complex dynamics of quantum reservoirs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum reservoir computing has emerged as a promising paradigm within the field of quantum machine learning, harnessing the inherent properties of quantum systems to optimise and enhance information processing capabilities. Here, we explore the potential of quantum-inspired machine learning methodologies by leveraging the complex dynamics of quantum reservoirs to address computationally challenging tasks with enhanced efficiency and accuracy. To this end, we employ an open quantum system model comprising two-level atomic ensembles coupled to Lorentzian photonic cavities to construct a quantum physical reservoir computer layer for a recurrent neural network. We evaluate the effectiveness of this approach by applying it to a standard machine learning image-recognition problem and benchmarking its performance against a conventional neural network of similar architecture, but lacking the quantum physical reservoir computer layer. Remarkably, as the dataset size increases, the quantum physical reservoir computer outperforms the conventional neural network, requiring fewer training epochs and a smaller dataset to achieve comparable accuracy. Furthermore, we employ the quantum physical reservoir computing approach to model the dynamics of open quantum systems, focusing on atomic system ensembles interacting with a structured photonic reservoir associated with a photonic band-gap material. Our results reveal that the quantum reservoir computer provides equally powerful representations for quantum dynamical problems, maintaining effectiveness even under constraints of limited training data.

Related papers

• Robust and Efficient Quantum Reservoir Computing with Discrete Time Crystal [7.504145813547092]
  We introduce a gradient-free, noise-robust quantum reservoir computing algorithm that harnesses discrete time crystal dynamics as a reservoir.<n>For ten-class classification, both noisy simulations and experimental results on superconducting quantum processors match ideal simulations.<n>It establishes the correlation between quantum many-body non-equilibrium phase transitions and quantum machine learning performance.
  arXiv  Detail & Related papers  (2025-08-21T04:40:46Z)
• Enhanced image classification via hybridizing quantum dynamics with classical neural networks [0.0]
  We present a hybrid protocol which combines classical neural networks with non-equilibrium dynamics of a quantum many-body system for image classification.<n>This architecture leverages classical neural networks to efficiently process high-dimensional data and encode it effectively on a quantum many-body system.
  arXiv  Detail & Related papers  (2025-07-18T00:15:14Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Photonic Quantum Computers [0.0]
  Review captures a pivotal moment of photonic quantum computing in the noisy intermediate-scale quantum (NISQ) era. Offers insights into how photonic quantum computers might reshape the future of quantum computing.
  arXiv  Detail & Related papers  (2024-09-12T17:16:38Z)
• Practical Few-Atom Quantum Reservoir Computing [0.0]
  Quantum Reservoir Computing (QRC) harnesses quantum systems to tackle intricate computational problems with exceptional efficiency and minimized energy usage. This paper presents a QRC framework that utilizes a minimalistic quantum reservoir, consisting of only a few two-level atoms within an optical cavity.
  arXiv  Detail & Related papers  (2024-05-08T04:14:31Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Coreset selection can accelerate quantum machine learning models with provable generalization [6.733416056422756]
  Quantum neural networks (QNNs) and quantum kernels stand as prominent figures in the realm of quantum machine learning. We present a unified approach: coreset selection, aimed at expediting the training of QNNs and quantum kernels.
  arXiv  Detail & Related papers  (2023-09-19T08:59:46Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• 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)
• Simulation of Entanglement Generation between Absorptive Quantum Memories [56.24769206561207]
  We use the open-source Simulator of QUantum Network Communication (SeQUeNCe), developed by our team, to simulate entanglement generation between two atomic frequency comb (AFC) absorptive quantum memories. We realize the representation of photonic quantum states within truncated Fock spaces in SeQUeNCe. We observe varying fidelity with SPDC source mean photon number, and varying entanglement generation rate with both mean photon number and memory mode number.
  arXiv  Detail & Related papers  (2022-12-17T05:51:17Z)
• Impact of the form of weighted networks on the quantum extreme reservoir computation [0.0]
  The quantum extreme reservoir computation (QERC) is a versatile quantum neural network model. We show how a simple Hamiltonian model based on a disordered discrete time crystal with its simple implementation route provides nearly-optimal performance.
  arXiv  Detail & Related papers  (2022-11-15T01:50:47Z)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• Efficient criteria of quantumness for a large system of qubits [58.720142291102135]
  We discuss the dimensionless combinations of basic parameters of large, partially quantum coherent systems. Based on analytical and numerical calculations, we suggest one such number for a system of qubits undergoing adiabatic evolution.
  arXiv  Detail & Related papers  (2021-08-30T23:50:05Z)
• Comparing concepts of quantum and classical neural network models for image classification task [0.456877715768796]
  This material includes the results of experiments on training and performance of a hybrid quantum-classical neural network. Although its simulation is time-consuming, the quantum network, although its simulation is time-consuming, overcomes the classical network.
  arXiv  Detail & Related papers  (2021-08-19T18:49:30Z)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)
• Experimental quantum memristor [0.5396401833457565]
  We introduce and experimentally demonstrate a novel quantum-optical memristor based on integrated photonics and acts on single photons. Our device could become a building block of immediate and near-term quantum neuromorphic architectures.
  arXiv  Detail & Related papers  (2021-05-11T08:42:14Z)
• Quantum Deformed Neural Networks [83.71196337378022]
  We develop a new quantum neural network layer designed to run efficiently on a quantum computer. It can be simulated on a classical computer when restricted in the way it entangles input states.
  arXiv  Detail & Related papers  (2020-10-21T09:46:12Z)

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.