Machine learning the arrow of time in solid-state spins

• URL: http://arxiv.org/abs/2603.10344v1
• Date: Wed, 11 Mar 2026 02:29:34 GMT
• Title: Machine learning the arrow of time in solid-state spins
• Abstract summary: We harness machine learning to identify the arrow of time from individual trajectories generated by a programmable ten-qubit quantum processor based on a nitrogen-vacancy center in diamond.<n>We demonstrate that an unsupervised clustering algorithm autonomously divides the experimental trajectories into two distinct groups without prior knowledge, while a convolutional neural network identifies the temporal direction of these trajectories with approximately 92% accuracy.
• Score: 5.8922145908069545
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding the emergence of the thermodynamic arrow of time in microscopic systems is of fundamental importance, particularly given that unitary evolution preserves time-reversal symmetry. While projective measurements introduce temporal irreversibility, identifying this asymmetry from single evolution trajectories in the presence of stochastic fluctuations presents a considerable challenge. Here, we harness machine learning to identify the arrow of time from individual trajectories generated by a programmable ten-qubit quantum processor based on a nitrogen-vacancy center in diamond. We implement quantum circuits that realize unitary evolutions where heat flows from hotter to colder subsystems and their time-reversed counterparts. Projective measurements inserted in these processes induce entropy production, and their outcomes constitute the evolution trajectory. We demonstrate that an unsupervised clustering algorithm autonomously divides the experimental trajectories into two distinct groups without prior knowledge, while a convolutional neural network identifies the temporal direction of these trajectories with approximately 92% accuracy. In addition, we show that a diffusion-based generative model reproduces essential signatures of directional energy flow and entropy production. Our results establish machine learning as a powerful tool for uncovering underlying physical processes from complex experimental data, advancing the interface between quantum thermodynamics and artificial intelligence.

Related papers

• Precision quantum simulation of magnon spectra and interactions [94.30337065819249]
  We show high-precision simulation of both linear and non-linear response functions in a 2D XY spin-1/2 magnet using an analog-digital superconducting processor of up to 97 qubits.<n>Our results reveal stark variations in magnon decay rates across the Brillouin zone, with enhancement near van Hove singularities and suppression for edge-localized modes.
  arXiv  Detail & Related papers  (2026-07-14T22:11:22Z)
• Causal Representation Meets Stochastic Modeling under Generic Geometry [49.24293444627916]
  We develop causal representation learning for continuous-time latent point processes.<n>We develop MUTATE, an identifiable variational autoencoder framework with a time-adaptive transition module.<n>Across simulated and empirical studies, we find that MUTATE can effectively answer scientific questions.
  arXiv  Detail & Related papers  (2026-02-04T20:40:53Z)
• Reshaping the Quantum Arrow of Time [0.5188841610098435]
  In quantum physics, an arrow of time emerges when a sequence of measurements is performed on a system.<n>We introduce quantum control tools that can yield dynamics more consistent with time flowing backward than forward.
  arXiv  Detail & Related papers  (2025-03-17T18:07:22Z)
• Realization of Two-dimensional Discrete Time Crystals with Anisotropic Heisenberg Coupling [0.42131793931438133]
  A discrete time crystal (DTC) is the paradigmatic example of a phase of matter that occurs exclusively in systems out of equilibrium.<n>We demonstrate the existence of a DTC in a two-dimensional system governed by anisotropic Heisenberg interactions.
  arXiv  Detail & Related papers  (2025-01-29T22:51:09Z)
• Space-time correlations in monitored kinetically constrained discrete-time quantum dynamics [0.0]
  We show a kinetically constrained many-body quantum system that has a natural implementation on Rydberg quantum simulators.<n>Despite featuring an uncorrelated infinite-temperature average stationary state, the dynamics displays coexistence of fast and slow space-time regions.<n>Our work establishes the large deviation framework for discrete-time open quantum many-body systems as a means to characterize complex dynamics and collective phenomena in quantum processors and simulators.
  arXiv  Detail & Related papers  (2024-08-19T10:24:07Z)
• Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired Neural Networks for the 3D microstructure evolution of materials via Cahn-Hilliard flow [0.0]
  A Convolutional Recurrent Neural Network (CRNN) is trained to reproduce the evolution of the spinodal decomposition process in three dimensions. A specialized, physics-inspired architecture is proven to provide close accordance between the predicted evolutions and the ground truth ones.
  arXiv  Detail & Related papers  (2024-07-29T15:55:52Z)
• Measurement-induced entanglement and teleportation on a noisy quantum processor [105.44548669906976]
  We investigate measurement-induced quantum information phases on up to 70 superconducting qubits. We use a duality mapping, to avoid mid-circuit measurement and access different manifestations of the underlying phases. Our work demonstrates an approach to realize measurement-induced physics at scales that are at the limits of current NISQ processors.
  arXiv  Detail & Related papers  (2023-03-08T18:41:53Z)
• Self-oscillating pump in a topological dissipative atom-cavity system [55.41644538483948]
  We report on an emergent mechanism for pumping in a quantum gas coupled to an optical resonator. Due to dissipation, the cavity field evolves between its two quadratures, each corresponding to a different centrosymmetric crystal configuration. This self-oscillation results in a time-periodic potential analogous to that describing the transport of electrons in topological tight-binding models.
  arXiv  Detail & Related papers  (2021-12-21T19:57:30Z)
• DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals [62.997667081978825]
  We develop a novel statistical point process model-called driven temporal point processes (DriPP) We derive a fast and principled expectation-maximization (EM) algorithm to estimate the parameters of this model. Results on standard MEG datasets demonstrate that our methodology reveals event-related neural responses.
  arXiv  Detail & Related papers  (2021-12-08T13:07:21Z)
• Visualizing spinon Fermi surfaces with time-dependent spectroscopy [62.997667081978825]
  We propose applying time-dependent photo-emission spectroscopy, an established tool in solid state systems, in cold atom quantum simulators. We show in exact diagonalization simulations of the one-dimensional $t-J$ model that the spinons start to populate previously unoccupied states in an effective band structure. The dependence of the spectral function on the time after the pump pulse reveals collective interactions among spinons.
  arXiv  Detail & Related papers  (2021-05-27T18:00:02Z)
• Analyzing non-equilibrium quantum states through snapshots with artificial neural networks [0.0]
  Current quantum simulation experiments are starting to explore non-equilibrium many-body dynamics in previously inaccessible regimes. Using machine learning techniques, we investigate the dynamics and in particular the thermalization behavior of an interacting quantum system. A neural network is trained to distinguish non-equilibrium from thermal equilibrium data, and the network performance serves as a probe for the thermalization behavior of the system.
  arXiv  Detail & Related papers  (2020-12-21T18:59:21Z)

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.