Learning the dynamics of open quantum systems from their steady states

Bairey, Eyal; Guo, Chu; Poletti, Dario; Lindner, Netanel H.; Arad, Itai

doi:10.1088/1367-2630/ab73cd

Cited by 54 publications

(29 citation statements)

References 59 publications

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“…In fact, being the only computational demanding part of the algorithm the calculation of the ground state and and the Bisognano-Wichmann expectation values, in principle one can tackle also higher dimensions by using Monte Carlo techniques. From the quantum engineering viewpoint, another intriguing perspective is to search for Liouvillians that have JG wave functions as unique steady states [94,95]. In particular, dissipation may considerably soften the requirement for longrange couplings thanks to correlations induced by the bath.…”

Section: Conclusion and Outlooksmentioning

confidence: 99%

Parent Hamiltonian reconstruction of Jastrow-Gutzwiller wavefunctions

Turkeshi

Dalmonte

2020

SciPost Phys.

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Variational wave functions have been a successful tool to investigate the properties of quantum spin liquids. Finding their parent Hamiltonians is of primary interest for the experimental simulation of these strongly correlated phases, and for gathering additional insights on their stability. In this work, we systematically reconstruct approximate spin-chain parent Hamiltonians for Jastrow-Gutzwiller wave functions, which share several features with quantum spin liquid wave-functions in two dimensions. Firstly, we determine the different phases encoded in the parameter space through their correlation functions and entanglement content. Secondly, we apply a recently proposed entanglement-guided method to reconstruct parent Hamiltonians to these states, which constrains the search to operators describing relativistic low-energy field theories -as expected for deconfined phases of gauge theories relevant to quantum spin liquids. The quality of the results is discussed using different quantities and comparing to exactly known parent Hamiltonians at specific points in parameter space. Our findings provide guiding principles for experimental Hamiltonian engineering of this class of states.

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Section: Conclusion and Outlooksmentioning

confidence: 99%

Parent Hamiltonian reconstruction of Jastrow-Gutzwiller wavefunctions

Turkeshi

Dalmonte

2020

SciPost Phys.

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“…T . While Lindbladian learning has been recently investigated [11], where it was shown that reconstructing strongly dephasing jump operators is difficult under certain conditions, our focus is on precisely estimating the Hamiltonian component, possibly at the expense of the environmental sector.…”

Section: Open System Generalizationmentioning

confidence: 99%

Hamiltonian assignment for open quantum systems

Dumitrescu

Lougovski

2020

Phys. Rev. Research

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We investigate the problem of determining the Hamiltonian of a locally interacting open quantum system. To do so, we construct Hamiltonian estimators based on inverting a set of stationary, or dynamical, Heisenberg-Langevin equations of motion which rely on a polynomial number of measurements and model parameters. To validate our Hamiltonian assignment methods we numerically simulate one-dimensional X X-interacting spin chains coupled to thermal reservoirs. We provide general bounds on the scalability and assignment error in the presence of noise. In addition to discussing some details of practical implementations we find that, in a dynamical setting, the Hamiltonian estimator's accuracy increases when relaxing the environment's physicality constraints.

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“…Recent progress has been made in reconstructing generic local Hamiltonians from measurements on single eigenstates and steady states of closed and open systems [18][19][20][21][22][23][24] as well as in the development of more specialized approaches tailored to concrete models [25][26][27][28][29][30][31][32]. An area of research that poses specific challenges for numerical methods and thus Hamiltonian tomography is out-of-equilibrium physics [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

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

Valenti,

Jin,

Léonard

et al. 2021

Preprint

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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.

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Learning the dynamics of open quantum systems from their steady states

Cited by 54 publications

References 59 publications

Parent Hamiltonian reconstruction of Jastrow-Gutzwiller wavefunctions

Parent Hamiltonian reconstruction of Jastrow-Gutzwiller wavefunctions

Hamiltonian assignment for open quantum systems

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

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