Quantum reservoir computing in atomic lattices

• URL: http://arxiv.org/abs/2411.13401v1
• Date: Wed, 20 Nov 2024 15:39:15 GMT
• Title: Quantum reservoir computing in atomic lattices
• Authors: Guillem Llodrà, Pere Mujal, Roberta Zambrini, Gian Luca Giorgi,
• Abstract summary: Quantum reservoir computing exploits the dynamical properties of quantum systems to perform machine learning tasks. We show that optimal performance in QRC can be achieved without relying on disordered systems.
• Score: 0.8437187555622164
• License:
• Abstract: Quantum reservoir computing (QRC) exploits the dynamical properties of quantum systems to perform machine learning tasks. We demonstrate that optimal performance in QRC can be achieved without relying on disordered systems. Systems with all-to-all topologies and random couplings are generally considered to minimize redundancies and enhance performance. In contrast, our work investigates the one-dimensional Bose-Hubbard model with homogeneous couplings, where a chaotic phase arises from the interplay between coupling and interaction terms. Interestingly, we find that performance in different tasks can be enhanced either in the chaotic regime or in the weak interaction limit. Our findings challenge conventional design principles and indicate the potential for simpler and more efficient QRC implementations tailored to specific tasks in Bose-Hubbard lattices.

Related papers

• Role of coherence in many-body Quantum Reservoir Computing [3.4078654008228924]
  We show how different quantum effects, such as quantum coherence and correlations, contribute to improving the performance in temporal tasks. We critically assess the impact of finite measurement resources and noise on the reservoir's dynamics in different regimes.
  arXiv  Detail & Related papers  (2024-09-26T11:06:08Z)
• Effective Modeling of Open Quantum Systems by Low-rank Discretization of Structured Environments [0.0]
  We pioneer a new strategy to create discrete low-rank models of the system-environment interaction. We demonstrate the effectiveness of our methodology by combining it with tensor-network methodologies. The new modeling framework sets the basis for a leap in the analysis of open quantum systems.
  arXiv  Detail & Related papers  (2024-07-26T17:27:09Z)
• Projective Quantum Eigensolver via Adiabatically Decoupled Subsystem Evolution: a Resource Efficient Approach to Molecular Energetics in Noisy Quantum Computers [0.0]
  We develop a projective formalism that aims to compute ground-state energies of molecular systems accurately using Noisy Intermediate Scale Quantum (NISQ) hardware. We demonstrate the method's superior performance under noise while concurrently ensuring requisite accuracy in future fault-tolerant systems.
  arXiv  Detail & Related papers  (2024-03-13T13:27:40Z)
• Single entanglement connection architecture between multi-layer bipartite Hardware Efficient Ansatz [18.876952671920133]
  We propose a single entanglement connection architecture (SECA) for a bipartite hardware efficient ansatz. Our results indicate the superiority of SECA over the common full entanglement connection architecture (FECA) in terms of computational performance.
  arXiv  Detail & Related papers  (2023-07-23T13:36:30Z)
• Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks [68.8204255655161]
  We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms. We consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator. We observe high quantum fidelities and resilience to noise, despite the algorithm being trained in the ideal noise-free setting.
  arXiv  Detail & Related papers  (2022-06-29T14:34:00Z)
• Quantum communication complexity beyond Bell nonlocality [87.70068711362255]
  Efficient distributed computing offers a scalable strategy for solving resource-demanding tasks. Quantum resources are well-suited to this task, offering clear strategies that can outperform classical counterparts. We prove that a new class of communication complexity tasks can be associated to Bell-like inequalities.
  arXiv  Detail & Related papers  (2021-06-11T18:00:09Z)
• Error mitigation and quantum-assisted simulation in the error corrected regime [77.34726150561087]
  A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations. We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
  arXiv  Detail & Related papers  (2021-03-12T20:58:41Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• Quantum Non-equilibrium Many-Body Spin-Photon Systems [91.3755431537592]
  dissertation concerns the quantum dynamics of strongly-correlated quantum systems in out-of-equilibrium states. Our main results can be summarized in three parts: Signature of Critical Dynamics, Driven Dicke Model as a Test-bed of Ultra-Strong Coupling, and Beyond the Kibble-Zurek Mechanism.
  arXiv  Detail & Related papers  (2020-07-23T19:05:56Z)
• Higher-Order Quantum Reservoir Computing [0.0]
  We propose a hybrid quantum-classical framework consisting of multiple but small quantum systems that are mutually communicated via classical connections like linear feedback. We demonstrate the effectiveness of our framework in emulating large-scale nonlinear dynamical systems.
  arXiv  Detail & Related papers  (2020-06-16T08:54:04Z)
• Entanglement Classification via Neural Network Quantum States [58.720142291102135]
  In this paper we combine machine-learning tools and the theory of quantum entanglement to perform entanglement classification for multipartite qubit systems in pure states. We use a parameterisation of quantum systems using artificial neural networks in a restricted Boltzmann machine (RBM) architecture, known as Neural Network Quantum States (NNS)
  arXiv  Detail & Related papers  (2019-12-31T07:40:23Z)

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.