Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics

Abstract

Large-scale quantum devices provide insights beyond the reach of classical simulations. However, for a reliable and verifiable quantum simulation, the building blocks of the quantum device require exquisite benchmarking. This benchmarking of large scale dynamical quantum systems represents a major challenge due to lack of efficient tools for their simulation. Here, we present a scalable algorithm based on neural networks for Hamiltonian tomography in out-of-equilibrium quantum systems. We illustrate our approach using a model for a forefront quantum simulation platform: ultracold atoms in optical lattices. Specifically, we show that our algorithm is able to reconstruct the Hamiltonian of an arbitrary size quasi-1D bosonic system using an accessible amount of experimental measurements. We are able to significantly increase the previously known parameter precision.

arXiv comment13 pages, 13 figures, code: https://gitlab.com/QMAI/papers/manybodydynlearning

References in corpus (53)

• TensorFlow: A system for large-scale machine learning
• Many-Body Physics with Ultracold Gases
• Cold bosonic atoms in optical lattices
• Probing many-body dynamics on a 51-atom quantum simulator
• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• A quantum gas microscope - detecting single atoms in a Hubbard regime optical lattice
• Realization of the Hofstadter Hamiltonian with ultracold atoms in optical lattices
• Single-Atom Resolved Fluorescence Imaging of an Atomic Mott Insulator
• Quantum many-body systems out of equilibrium
• Observation of a Many-Body Dynamical Phase Transition with a 53-Qubit Quantum Simulator
• Quantum Phases of Matter on a 256-Atom Programmable Quantum Simulator
• Matrix Product States and Projected Entangled Pair States: Concepts, Symmetries, and Theorems
• Real-time dynamics of lattice gauge theories with a few-qubit quantum computer
• Probing entanglement in a many-body-localized system
• Chiral groundstate currents of interacting photons in a synthetic magnetic field
• A Dissipatively Stabilized Mott Insulator of Photons
• Quantum simulation of a Fermi-Hubbard model using a semiconductor quantum dot array
• Observation of topological phenomena in a programmable lattice of 1,800 qubits
• Realization of an anomalous Floquet topological system with ultracold atoms
• Observation of gauge invariance in a 71-site Bose-Hubbard quantum simulator
• Does a single eigenstate encode the full Hamiltonian?
• Hamiltonian Learning and Certification Using Quantum Resources
• Quantum critical behavior at the many-body-localization transition
• Experimental neural network enhanced quantum tomography
• Learning a local Hamiltonian from local measurements
• Ultra-precise holographic beam shaping for microscopic quantum control
• Integrating Neural Networks with a Quantum Simulator for State Reconstruction
• Determining a local Hamiltonian from a single eigenstate
• Enhancement and sign change of magnetic correlations in a driven quantum many-body system
• Coupling strength estimation for spin chains despite restricted access
• Machine Learning techniques for state recognition and auto-tuning in quantum dots
• Architectures for quantum simulation showing a quantum speedup
• Computational inverse method for constructing spaces of quantum models from wave functions
• Identifying an Experimental Two-State Hamiltonian to Arbitrary Accuracy
• Hamiltonian Tomography via Quantum Quench
• Unsupervised identification of topological order using predictive models
• Experimental Hamiltonian identification for controlled two-level systems
• Hamiltonian Learning for Quantum Error Correction
• Learning the dynamics of open quantum systems from their steady states
• Interpreting machine learning of topological quantum phase transitions
• Scheme for direct measurement of a general two-qubit Hamiltonian
• Learning Quantum Hamiltonians from Single-qubit Measurements
• Entanglement guided search for parent Hamiltonians
• Closing gaps of a quantum advantage with short-time Hamiltonian dynamics
• Analyzing non-equilibrium quantum states through snapshots with artificial neural networks
• Direct measurement of non-local interactions in the many-body localized phase
• Direct reconstruction of the quantum master equation dynamics of a trapped ion qubit
• Real time evolution with neural-network quantum states
• Supervised learning in Hamiltonian reconstruction from local measurements on eigenstates
• Precision characterisation of two-qubit Hamiltonians via entanglement mapping
• Scalable Bayesian Hamiltonian learning
• Parent Hamiltonian Reconstruction of Jastrow-Gutzwiller Wavefunctions
• Quantum parameter estimation with a neural network

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• Maximum-Likelihood-Estimate Hamiltonian learning via efficient and robust quantum likelihood gradient
• Hamiltonian Learning of Triplon Excitations in an Artificial Nanoscale Molecular Quantum Magnet
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• Non-Hermitian Parent Hamiltonian from Generalized Quantum Covariance Matrix
• Machine-learning-enabled characterization of individual ring resonators in integrated photonic lattices
• Artificial intelligence for representing and characterizing quantum systems
• Learning the dynamics of Markovian open quantum systems from experimental data
• Machine Learning out of equilibrium correlations in the Bose-Hubbard model
• One-to-one correspondence between thermal structure factors and coupling constants of general bilinear Hamiltonians
• Transfer learning of many-body electronic correlation entropy from local measurements
• Learning and simulating bosonic systems via finite-energy locality