Memory preservation and cooperative shielding in complex quantum networks
- URL: http://arxiv.org/abs/2503.05655v2
- Date: Tue, 05 Aug 2025 17:48:31 GMT
- Title: Memory preservation and cooperative shielding in complex quantum networks
- Authors: Simone Ausilio, Fausto Borgonovi, Giuseppe Luca Celardo, Jorge Yago Malo, Maria Luisa Chiofalo,
- Abstract summary: We study the transport properties of a quantum network described by the paradigmatic XXZ Hamiltonian.<n>We show how long range interactions induce a memory preserving effects and strongly affect the spreading of the excitations.<n>We discuss the implications of these properties in biology-related problems, such as an application to Weber's law in neuroscience, and their implementation in specific quantum technologies via biomimicry.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Complex quantum networks are powerful tools in the modeling of transport phenomena, particularly for biological systems, and enable the study of emergent phenomena in many-body quantum systems. High connectivity and long range interactions induce strong constraints on the system dynamics. Here, we study the transport properties of a quantum network described by the paradigmatic XXZ Hamiltonian, with non-trivial graph connectivity and topology, and long-range interaction. We show how long range interactions induce a memory preserving effects and strongly affect the spreading of the excitations due to cooperative shielding. We describe the memory-preserving effect in all-to-all connected regular networks with distance-independent couplings. Indeed the memory of the number of initially injected excitations is preserved over long times, being encoded in the number of frequencies present in the dynamics. Interestingly, we find that memory-preserving effects occur also in less regular graphs, such as quantum networks with either power-law node connectivity or complex, small-world type, architectures. We discuss the implications of these properties in biology-related problems, such as an application to Weber's law in neuroscience, and their implementation in specific quantum technologies via biomimicry. We also show how the presence of long range interaction strongly affect the dynamics of the excitations in small-world networks and power law all-to-all coupled networks. Indeed due to cooperative shielding as the connectivity or the range of interaction increase the initial excitation spreads more slowly among the network and becomes strongly dependent on the initial conditions.
Related papers
- On the emergence of quantum memory in non-Markovian dynamics [41.94295877935867]
Non-Markovian dynamics (with memory) is typical in practice, with memory effects being harnessed as a resource for many tasks like quantum error correction and information processing.<n>Yet, the type of memory, classical or quantum, necessary to realize the dynamics of many collision models is not known.<n>In this work, we extend the quantum homogenizer to the non-Markovian regime by introducing intra-ancilla interactions mediated by Fredkin gates, and study the nature of its memory.
arXiv Detail & Related papers (2025-07-29T15:19:26Z) - A comprehensive exploration of interaction networks reveals a connection between entanglement and network structure [0.0]
We investigate the connection between the structure of the interaction network and the eigenstate entanglement of the quantum Ising model.<n>Our results demonstrate that the minimum eigenstate entanglement of the quantum Ising model is governed by the specific structure of the interaction network.
arXiv Detail & Related papers (2025-05-16T17:21:46Z) - Emergence of global receptive fields capturing multipartite quantum correlations [0.565473932498362]
In quantum physics, even simple data with a well-defined structure at the wave function level can be characterized by extremely complex correlations.
We show that monitoring the neural network weight space while learning quantum statistics allows to develop physical intuition about complex multipartite patterns.
Our findings suggest a fresh look at constructing convolutional neural networks for processing data with non-local patterns.
arXiv Detail & Related papers (2024-08-23T12:45:40Z) - Explosive neural networks via higher-order interactions in curved statistical manifolds [43.496401697112695]
We introduce curved neural networks as a class of prototypical models with a limited number of parameters.<n>We show that these curved neural networks implement a self-regulating process that can accelerate memory retrieval.<n>We analytically explore their memory-retrieval capacity using the replica trick near ferromagnetic and spin-glass phase boundaries.
arXiv Detail & Related papers (2024-08-05T09:10:29Z) - Quantifying High-Order Interdependencies in Entangled Quantum States [43.70611649100949]
We introduce the Q-information: an information-theoretic measure capable of distinguishing quantum states dominated by synergy or redundancy.
We show that quantum systems need at least four variables to exhibit high-order properties.
Overall, the Q-information sheds light on novel aspects of the internal organisation of quantum systems and their time evolution.
arXiv Detail & Related papers (2023-10-05T17:00:13Z) - 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) - Dipolar quantum solids emerging in a Hubbard quantum simulator [45.82143101967126]
Long-range and anisotropic interactions promote rich spatial structure in quantum mechanical many-body systems.
We show that novel strongly correlated quantum phases can be realized using long-range dipolar interaction in optical lattices.
This work opens the door to quantum simulations of a wide range of lattice models with long-range and anisotropic interactions.
arXiv Detail & Related papers (2023-06-01T16:49:20Z) - Numerical simulations of long-range open quantum many-body dynamics with
tree tensor networks [0.0]
We introduce a numerical method for open quantum systems, based on tree tensor networks.
Such a structure is expected to improve the encoding of many-body correlations.
We adopt an integration scheme suited for long-range interactions and applications to dissipative dynamics.
arXiv Detail & Related papers (2023-04-12T18:00:03Z) - 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) - Using (1 + 1)D Quantum Cellular Automata for Exploring Collective
Effects in Large Scale Quantum Neural Networks [0.0]
We study the impact of quantum effects on the way in which quantum perceptrons and neural networks process information.
We exploit a class of quantum gates that allow for the introduction of quantum effects, such as those associated with a coherent Hamiltonian evolution.
We identify a change of critical behavior when quantum effects are varied, demonstrating that they can indeed affect the collective dynamical behavior underlying the processing of information in large-scale neural networks.
arXiv Detail & Related papers (2022-07-24T17:10:12Z) - A scalable superconducting quantum simulator with long-range
connectivity based on a photonic bandgap metamaterial [0.0]
We present a quantum simulator architecture based on a linear array of qubits locally connected to a superconducting photonic-bandgap metamaterial.
The metamaterial acts both as a quantum bus mediating qubit-qubit interactions, and as a readout channel for multiplexed qubit-state measurement.
We characterize the Hamiltonian of the system using a measurement-efficient protocol based on quantum many-body chaos.
arXiv Detail & Related papers (2022-06-26T06:51:54Z) - Collisional open quantum dynamics with a generally correlated
environment: Exact solvability in tensor networks [0.0]
We find a natural Markovian embedding for the system dynamics, where the role of an auxiliary system is played by virtual indices of the network.
The results advance tensor-network methods in the fields of quantum optics and quantum transport.
arXiv Detail & Related papers (2022-02-09T19:48:17Z) - Quantum local random networks and the statistical robustness of quantum
scars [68.8204255655161]
We investigate the emergence of quantum scars in a general ensemble of random Hamiltonians.
We find a class of scars, that we call "statistical"
We study the scaling of the number of statistical scars with system size.
arXiv Detail & Related papers (2021-07-02T07:53:09Z) - Tracing Information Flow from Open Quantum Systems [52.77024349608834]
We use photons in a waveguide array to implement a quantum simulation of the coupling of a qubit with a low-dimensional discrete environment.
Using the trace distance between quantum states as a measure of information, we analyze different types of information transfer.
arXiv Detail & Related papers (2021-03-22T16:38:31Z) - Simulation of Collective Neutrino Oscillations on a Quantum Computer [117.44028458220427]
We present the first simulation of a small system of interacting neutrinos using current generation quantum devices.
We introduce a strategy to overcome limitations in the natural connectivity of the qubits and use it to track the evolution of entanglement in real-time.
arXiv Detail & Related papers (2021-02-24T20:51:25Z) - Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor.
We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
arXiv Detail & Related papers (2021-01-21T22:18:49Z) - Controlling many-body dynamics with driven quantum scars in Rydberg atom
arrays [41.74498230885008]
We experimentally investigate non-equilibrium dynamics following rapid quenches in a many-body system composed of 3 to 200 strongly interacting qubits in one and two spatial dimensions.
We discover that scar revivals can be stabilized by periodic driving, which generates a robust subharmonic response akin to discrete time-crystalline order.
arXiv Detail & Related papers (2020-12-22T19:00:02Z) - Continuous and time-discrete non-Markovian system-reservoir
interactions: Dissipative coherent quantum feedback in Liouville space [62.997667081978825]
We investigate a quantum system simultaneously exposed to two structured reservoirs.
We employ a numerically exact quasi-2D tensor network combining both diagonal and off-diagonal system-reservoir interactions with a twofold memory for continuous and discrete retardation effects.
As a possible example, we study the non-Markovian interplay between discrete photonic feedback and structured acoustic phononovian modes, resulting in emerging inter-reservoir correlations and long-living population trapping within an initially-excited two-level system.
arXiv Detail & Related papers (2020-11-10T12:38:35Z) - Probing Criticality in Quantum Spin Chains with Neural Networks [0.0]
We show that even neural networks with no hidden layers can be effectively trained to distinguish between magnetically ordered and disordered phases.
Our results extend to a wide class of interacting quantum many-body systems and illustrate the wide applicability of neural networks to many-body quantum physics.
arXiv Detail & Related papers (2020-05-05T12:34:50Z) - 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.