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This study challenges strictly guaranteeing ``dissipativity'' of a dynamical system represented by neural networks learned from given time-series data. Dissipativity is a crucial indicator for dynamical systems that generalizes stability…

Machine Learning · Computer Science 2024-12-20 Yuji Okamoto , Ryosuke Kojima

I present a quantum-tunnelling oscillator model as a universal dynamical engine for two paradigmatic problems in quantum cognition theory -- optical illusion perception and group decision making -- where individuals are treated as…

Physics and Society · Physics 2026-04-07 Ivan S. Maksymov

Training of neural networks (NNs) has emerged as a major consumer of both computational and energy resources. Quantum computers were coined as a root to facilitate training, but no experimental evidence has been presented so far. Here we…

Quantum Physics · Physics 2025-12-02 Hao Zhang , Alex Kamenev

In this paper we present a new quantum-trajectory based treatment of quantum dynamics suitable for dissipative systems. Starting from a de Broglie/Bohm-like representation of the quantum density matrix, we derive and define quantum…

Quantum Physics · Physics 2007-05-23 Jeremy B. Maddox , Eric R. Bittner

Dissipation can be used as a resource to control and simulate quantum systems. We discuss a modular model based on fast dissipation capable of performing universal quantum computation, and simulating arbitrary Lindbladian dynamics. The…

Quantum Physics · Physics 2016-12-01 Jeffrey Marshall , Lorenzo Campos Venuti , Paolo Zanardi

This paper gives an introduction to \textit{Cognidynamics}, that is to the dynamics of cognitive systems driven by optimal objectives imposed over time when they interact either with a defined virtual or with a real-world environment. The…

Neurons and Cognition · Quantitative Biology 2024-08-26 Marco Gori

We present a quantum algorithm based on the Generalized Quantum Master Equation (GQME) approach to simulate open quantum system dynamics on noisy intermediate-scale quantum (NISQ) computers. This approach overcomes the limitations of the…

A promising application of neural-network quantum states is to describe the time dynamics of many-body quantum systems. To realize this idea, we employ neural-network quantum states to approximate the implicit midpoint rule method, which…

Disordered Systems and Neural Networks · Physics 2022-01-26 Irene López Gutiérrez , Christian B. Mendl

Quantum nuclear dynamics with wavepacket time-evolution is classically intractable and viewed as a promising avenue for quantum information processing. Here, we use an IonQ 11-qubit trapped-ion quantum computer, Harmony, to study the…

Quantum Physics · Physics 2024-12-05 Anurag Dwivedi , A. J. Rasmusson , Philip Richerme , Srinivasan S. Iyengar

The expectation that quantum computation might bring performance advantages in machine learning algorithms motivates the work on the quantum versions of artificial neural networks. In this study, we analyze the learning dynamics of a…

Quantum Physics · Physics 2023-10-17 Ufuk Korkmaz , Deniz Türkpençe

Distributed quantum computing (DQC) combines the computing power of multiple networked quantum processing modules, enabling the execution of large quantum circuits without compromising on performance and connectivity. Photonic networks are…

Quantum computing enables quantum neural networks (QNNs) to have great potentials to surpass artificial neural networks (ANNs). The powerful generalization of neural networks is attributed to nonlinear activation functions. Although various…

Quantum Physics · Physics 2020-11-30 Shilu Yan , Hongsheng Qi , Wei Cui

While quantum simulation is one of the most promising applications of modern quantum devices, accessible simulation times are fundamentally limited by finite coherence times due to omnipresent noise. Based on the ideas of relational…

Quantum Physics · Physics 2025-09-03 Sebastian Gemsheim , Felix Fritzsch

Atoms, molecules or excitonic quasiparticles, for which excitations are induced by external radiation fields and energy is dissipated through radiative decay, are examples of driven open quantum systems. We explain the use of…

Quantum Physics · Physics 2013-01-09 A. Alvermann , H. Fehske , P. B. Littlewood

Neural networks have achieved impressive breakthroughs in both industry and academia. How to effectively develop neural networks on quantum computing devices is a challenging open problem. Here, we propose a new quantum neural network model…

Quantum Physics · Physics 2023-05-16 Min-Gang Zhou , Zhi-Ping Liu , Hua-Lei Yin , Chen-Long Li , Tong-Kai Xu , Zeng-Bing Chen

A stochastic representation of the dynamics of open quantum systems, suitable for non-perturbative system-reservoir interaction, non-Markovian effects and arbitrarily driven systems is presented. It includes the case of driving on…

Statistical Mechanics · Physics 2016-10-05 Jürgen T. Stockburger

Quantum computing employs controllable interactions to perform sequences of logical gates and entire algorithms on quantum registers. This paradigm has been widely explored, e.g., for simulating dynamics of manybody systems by decomposing…

Quantum Physics · Physics 2025-05-21 S. Alipour , A. T. Rezakhani , Alireza Tavanfar , K. Mölmer , T. Ala-Nissila

Quantum technologies are increasingly pervasive, underpinning the operation of numerous electronic, optical and medical devices. Today, we are also witnessing rapid advancements in quantum computing and communication. However, access to…

Machine Learning · Computer Science 2025-03-12 Milan Maksimovic , Ivan S. Maksymov

At its core, Quantum Mechanics is a theory developed to describe fundamental observations in the spectroscopy of solids and gases. Despite these practical roots, however, quantum theory is infamous for being highly counterintuitive, largely…

Quantum Physics · Physics 2020-01-20 Emmanuel Flurin , Leigh S. Martin , Shay Hacohen-Gourgy , Irfan Siddiqi

Due to the exponential growth of the Hilbert space dimension with system size, the simulation of quantum many-body systems has remained a persistent challenge until today. Here, we review a relatively new class of variational states for the…

Disordered Systems and Neural Networks · Physics 2024-07-29 Hannah Lange , Anka Van de Walle , Atiye Abedinnia , Annabelle Bohrdt