Decoding Quantum Field Theory with Machine Learning
arXiv:1910.03637 · doi:10.1007/JHEP08(2023)031
Abstract
We demonstrate how one can use machine learning techniques to bypass the technical difficulties of designing an experiment and translating its outcomes into concrete claims about fundamental features of quantum fields. In practice, all measurements of quantum fields are carried out through local probes. Despite measuring only a small portion of the field, such local measurements have the capacity to reveal many of the field's global features. This is because, when in equilibrium with their environments, quantum fields store global information locally, albeit in a scrambled way. We show that neural networks can be trained to unscramble this information from data generated from a very simple one-size-fits-all local measurement protocol. To illustrate this general claim we will consider three non-trivial features of the field as case studies: a) how, as long as the field is in a stationary state, a particle detector can learn about the field's boundary conditions even before signals have time to propagate from the boundary to the detector, b) how detectors can determine the temperature of the quantum field even without thermalizing with it, and c) how detectors can distinguish between Fock states and coherent states even when the first and second moments of all their quadrature operators match. Each of these examples uses the exact same simple fixed local measurement protocol and machine-learning ansatz successfully. This supports the claim that the framework proposed here can be applied to nearly any kind of local measurement on a quantum field to reveal nearly any of the field's global properties in a one-size-fits-all manner.
arXiv comment29 pages (14 pages of appendices), 5 figures + 1 figure in Appendices, RevTeX 4.2. v3: Updated to match published version
References in corpus (61)
- Gaussian Quantum Information
- A bound on chaos
- Machine learning phases of matter
- Information in Black Hole Radiation
- Detection of 15 dB Squeezed States of Light and their Application for the Absolute Calibration of Photoelectric Quantum Efficiency
- Many-body quantum state tomography with neural networks
- Continuous variable quantum information: Gaussian states and beyond
- Observation of quantum Hawking radiation and its entanglement in an analogue black hole
- Ultrastrong coupling of a single artificial atom to an electromagnetic continuum in the nonperturbative regime
- Verified Quantum Information Scrambling
- Individual quantum probes for optimal thermometry
- Robust Online Hamiltonian Learning
- How often does the Unruh-DeWitt detector click? Regularisation by a spatial profile
- Entanglement harvesting from the electromagnetic vacuum with hydrogenlike atoms
- Wavepacket detection with the Unruh-DeWitt model
- Spacetime structure and vacuum entanglement
- Quantum Brownian Motion in a Bath of Parametric Oscillators: A model for system-field interactions
- Using Berry's phase to detect the Unruh effect at lower accelerations
- Detectors for probing relativistic quantum physics beyond perturbation theory
- Then again, how often does the Unruh-DeWitt detector click if we switch it carefully?
- Regressive and generative neural networks for scalar field theory
- Causality issues of particle detector models in QFT and Quantum Optics
- Gaussian quantum resource theories
- An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes
- Backreaction and Unruh effect: New insights from exact solutions of uniformly accelerated detectors
- Quantum delocalization, gauge and quantum optics: The light-matter interaction in relativistic quantum information
- General Relativistic Quantum Optics: Finite-size particle detector models in curved spacetimes
- A detector-based measurement theory for quantum field theory
- Broken covariance of particle detector models in relativistic quantum information
- Static, stationary and inertial Unruh-DeWitt detectors on the BTZ black hole
- Relativistic Quantum Optics: On the relativistic invariance of the light-matter interaction models
- Bridging thermodynamics and metrology in non-equilibrium Quantum Thermometry
- Machine learning action parameters in lattice quantum chromodynamics
- Experimental quantum homodyne tomography via machine learning
- Calibration of quantum sensors by neural networks
- Heavy quark potential in quark-gluon Plasma: Deep neural network meets lattice quantum chromodynamics
- Unsupervised machine learning of quantum phase transitions using diffusion maps
- Relativistic causality in particle detector models: Faster-than-light signalling and "Impossible measurements"
- Application of neural network to sign problem via path optimization method
- Deep learning stochastic processes with QCD phase transition
- Quantum Information Processing and Relativistic Quantum Fields
- Einstein-Podolsky-Rosen correlations between two uniformly accelerated oscillators
- Quantum gates and multipartite entanglement resonances realized by non-uniform cavity motion
- Machine-learning physics from unphysics: Finding deconfinement temperature in lattice Yang-Mills theories from outside the scaling window
- Work distributions on quantum fields
- Probing the Full Distribution of Many-Body Observables by Single-Qubit Interferometry
- Optimal nonequilibrium thermometry in Markovian environments
- Looking Inside a Black Hole
- Uninformed Bayesian Quantum Thermometry
- Quantum-tailored machine-learning characterization of a superconducting qubit
- Cavities in curved spacetimes: the response of particle detectors
- Learning a Local Symmetry with Neural-Networks
- Finite sizes and smooth cutoffs in superconducting circuits
- The equivalence principle and QFT: Can a particle detector tell if we live inside a hollow shell?
- Characterization of phase-averaged coherent states
- How measuring a quantum field affects entanglement harvesting
- Machine classification for probe based quantum thermometry
- A classification of open Gaussian dynamics
- Quantum Information Capsule and Information Delocalization by Entanglement in Multiple-qubit Systems
- Casimir effect with machine learning
- Probing geometric information using the Unruh effect in the vacuum