Quantum-like states on complex synchronized networks

• URL: http://arxiv.org/abs/2405.07950v1
• Date: Mon, 13 May 2024 17:25:58 GMT
• Title: Quantum-like states on complex synchronized networks
• Authors: Gregory D. Scholes,
• Abstract summary: We propose a model for quantum-like (QL) states and QL bits. We suggest a way that huge, complex systems can host robust states that can process information in a QL fashion.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent work has exposed the idea that interesting quantum-like probability laws, including interference effects, can be manifest in classical systems. Here we propose a model for quantum-like (QL) states and QL bits. We suggest a way that huge, complex systems can host robust states that can process information in a QL fashion. Axioms that such states should satisfy are proposed. Specifically, it is shown that building blocks suited for QL states are networks, possibly very complex, that we defined based on $k$-regular random graphs. These networks can dynamically encode a lot of information that is distilled into the emergent states we can use for QL like processing. Although the emergent states are classical, they have properties analogous to quantum states. Concrete examples of how QL functions are possible are given. The possibility of a `QL advantage' for computing-type operations and the potential relevance for new kinds of function in the brain are discussed and left as open questions.

Related papers

• Quantum-like states from classical systems [0.0]
  This work studies how a suitably-designed classical system generates with a quantum-like (QL) state space mediated by a graph.<n>The question of whether and, if so, how, entanglement can be exhibited by these QL systems is discussed critically and contrasted to the concept of classical entanglement' in optics.
  arXiv  Detail & Related papers  (2025-07-01T17:17:53Z)
• Operationally classical simulation of quantum states [41.94295877935867]
  A classical state-preparation device cannot generate superpositions and hence its emitted states must commute.<n>We show that no such simulation exists, thereby certifying quantum coherence.<n>Our approach is a possible avenue to understand how and to what extent quantum states defy generic models based on classical devices.
  arXiv  Detail & Related papers  (2025-02-03T15:25:03Z)
• Dynamics in an emergent quantum-like state space generated by a nonlinear classical network [0.0]
  This work exploits a framework whereby a graph serves to connect a classical system to a state space that we call quantum-like' (QL)<n>We study a specific example of a large, dynamical classical system that maps, via a graph, to the QL state space.<n>We show that a no-cloning theorem (that is, a state of a QL bit cannot be copied) applies not only to the QL states, but also to the underlying classical system.
  arXiv  Detail & Related papers  (2025-01-13T17:17:53Z)
• Quantum Homogenization as a Quantum Steady State Protocol on NISQ Hardware [42.52549987351643]
  Quantum homogenization is a reservoir-based quantum state approximation protocol. We extend the standard quantum homogenization protocol to the dynamically-equivalent ($mathttSWAP$)$alpha$ formulation. We show that our proposed protocol yields a completely positive, trace preserving (CPTP) map under which the code subspace is correctable.
  arXiv  Detail & Related papers  (2024-12-19T05:50:54Z)
• Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
  Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML) At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
  arXiv  Detail & Related papers  (2024-11-13T23:56:20Z)
• Multiple-basis representation of quantum states [1.1999555634662633]
  We explore a new hybrid, efficient quantum-classical representation of quantum states, the multiple-basis representation. This representation consists of a linear combination of states that are sparse in some given and different bases, specified by quantum circuits. We find cases in which this representation can be used, namely approximation of ground states, simulation of deeper computations by specifying bases with shallow circuits, and a tomographical protocol to describe states as multiple-basis representations.
  arXiv  Detail & Related papers  (2024-11-05T13:57:57Z)
• Quantum information with quantum-like bits [0.0]
  In previous work we have proposed a construction of quantum-like bits that could endow a large, complex classical system. This paper aims to explore the mathematical structure of quantum-like resources, and shows how arbitrary gates can be implemented by manipulating emergent states.
  arXiv  Detail & Related papers  (2024-08-12T20:40:54Z)
• Unifying (Quantum) Statistical and Parametrized (Quantum) Algorithms [65.268245109828]
  We take inspiration from Kearns' SQ oracle and Valiant's weak evaluation oracle. We introduce an extensive yet intuitive framework that yields unconditional lower bounds for learning from evaluation queries.
  arXiv  Detail & Related papers  (2023-10-26T18:23:21Z)
• State Classification via a Random-Walk-Based Quantum Neural Network [0.0]
  We introduce the quantum neural network (QSNN), and show its capability to accomplish the binary discrimination of quantum states. Other than binary discrimination, the QSNN is also applied to classify an unknown set of states into two types: entangled ones and separable ones. Our results suggest that the QSNN has the great potential to process unknown quantum states in quantum information.
  arXiv  Detail & Related papers  (2023-04-12T07:39:23Z)
• Variational Quantum Eigensolver for Classification in Credit Sales Risk [0.5524804393257919]
  We take into consideration a quantum circuit which is based on the Variational Quantum Eigensolver (VQE) and so-called SWAP-Test. In the utilized data set, two classes may be observed -- cases with low and high credit risk. The solution is compact and requires only logarithmically increasing number of qubits.
  arXiv  Detail & Related papers  (2023-03-05T23:08:39Z)
• Delegated variational quantum algorithms based on quantum homomorphic encryption [69.50567607858659]
  Variational quantum algorithms (VQAs) are one of the most promising candidates for achieving quantum advantages on quantum devices. The private data of clients may be leaked to quantum servers in such a quantum cloud model. A novel quantum homomorphic encryption (QHE) scheme is constructed for quantum servers to calculate encrypted data.
  arXiv  Detail & Related papers  (2023-01-25T07:00:13Z)
• Towards Neural Variational Monte Carlo That Scales Linearly with System Size [67.09349921751341]
  Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
  arXiv  Detail & Related papers  (2022-12-21T19:00:04Z)
• Quantum variational learning for entanglement witnessing [0.0]
  This work focuses on the potential implementation of quantum algorithms allowing to properly classify quantum states defined over a single register of $n$ qubits. We exploit the notion of "entanglement witness", i.e., an operator whose expectation values allow to identify certain specific states as entangled. We made use of Quantum Neural Networks (QNNs) in order to successfully learn how to reproduce the action of an entanglement witness.
  arXiv  Detail & Related papers  (2022-05-20T20:14:28Z)
• Oracle separations of hybrid quantum-classical circuits [68.96380145211093]
  Two models of quantum computation: CQ_d and QC_d. CQ_d captures the scenario of a d-depth quantum computer many times; QC_d is more analogous to measurement-based quantum computation. We show that, despite the similarities between CQ_d and QC_d, the two models are intrinsically, i.e. CQ_d $nsubseteq$ QC_d and QC_d $nsubseteq$ CQ_d relative to an oracle.
  arXiv  Detail & Related papers  (2022-01-06T03:10:53Z)
• 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.