Inferring interpretable dynamical generators of local quantum observables from projective measurements through machine learning

Abstract

To characterize the dynamical behavior of many-body quantum systems, one is usually interested in the evolution of so-called order-parameters rather than in characterizing the full quantum state. In many situations, these quantities coincide with the expectation value of local observables, such as the magnetization or the particle density. In experiment, however, these expectation values can only be obtained with a finite degree of accuracy due to the effects of the projection noise. Here, we utilize a machine-learning approach to infer the dynamical generator governing the evolution of local observables in a many-body system from noisy data. To benchmark our method, we consider a variant of the quantum Ising model and generate synthetic experimental data, containing the results of $N$ projective measurements at $M$ sampling points in time, using the time-evolving block-decimation algorithm. As we show, across a wide range of parameters the dynamical generator of local observables can be approximated by a Markovian quantum master equation. Our method is not only useful for extracting effective dynamical generators from many-body systems, but may also be applied for inferring decoherence mechanisms of quantum simulation and computing platforms.

arXiv comment7+4 pages, 3+5 figures

References in corpus (36)

• Quantum Simulation
• Quantum information with Rydberg atoms
• Efficient simulation of one-dimensional quantum many-body systems
• A Rydberg Quantum Simulator
• Mixed-state dynamics in one-dimensional quantum lattice systems: a time-dependent superoperator renormalization algorithm
• Experimental control of the transition from Markovian to non-Markovian dynamics of open quantum systems
• Quantum Simulators: Architectures and Opportunities
• Tools for quantum simulation with ultracold atoms in optical lattices
• Quantum simulation and computing with Rydberg-interacting qubits
• Can One Trust Quantum Simulators?
• Characterization of the Positivity of the Density Matrix in Terms of the Coherence Vector Representation
• Experimental Quantum Hamiltonian Learning
• Non-Markovian Dynamical Maps: Numerical Processing of Open Quantum Trajectories
• A concise review of Rydberg atom based quantum computation and quantum simulation
• Learning a local Hamiltonian from local measurements
• Learning Quantum Systems
• Entanglement Dynamics of Two Qubits in a Common Bath
• A scalable superconducting quantum simulator with long-range connectivity based on a photonic bandgap metamaterial
• Hamiltonian tomography in an access-limited setting without state initialization
• Computational inverse method for constructing spaces of quantum models from wave functions
• Theoretical and Experimental Perspectives of Quantum Verification
• Identifying an Experimental Two-State Hamiltonian to Arbitrary Accuracy
• Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks
• Simulating quantum-optical phenomena with cold atoms in optical lattices
• Learning models of quantum systems from experiments
• Role of interactions in a dissipative many-body localized system
• Tomographically reconstructed master equations for any open quantum dynamics
• Extracting the field theory description of a quantum many-body system from experimental data
• Scheme for direct measurement of a general two-qubit Hamiltonian
• Quantum simulation of exact electron dynamics can be more efficient than classical mean-field methods
• Quantum-tailored machine-learning characterization of a superconducting qubit
• Machine learning time-local generators of open quantum dynamics
• Deep Learning of Quantum Many-Body Dynamics via Random Driving
• Tomography of time-dependent quantum spin networks with machine learning
• Markovianity of an emitter coupled to a structured spin chain bath
• Inferring Markovian quantum master equations of few-body observables in interacting spin chains

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