Supervised learning in Hamiltonian reconstruction from local measurements on eigenstates

Cao, Chenfeng; Hou, Shi-Yao; Cao, Ningping; Zeng, Bei

doi:10.1088/1361-648x/abc4cf

Cited by 22 publications

(17 citation statements)

References 33 publications

(44 reference statements)

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“…Therefore, the discriminator detects a strong dependence on N y which appears in the exact tensornetwork calculations. Interestingly, the consistency of that dynamical correlator with several Hamiltonians simultaneously would represent a challenge for parameter extraction purely based on supervised learning due to the non-unique parent Hamiltonian [87], representing an advantage of generative-based parameter estimation.…”

Section: A Hamiltonian Learning With the Generative Modelmentioning

confidence: 99%

Designing quantum many-body matter with conditional generative adversarial networks

Koch¹,

Lado²

2022

Preprint

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The computation of dynamical correlators of quantum many-body systems represents an open critical challenge in condensed matter physics. While powerful methodologies have risen in recent years, covering the full parameter space remains unfeasible for most many-body systems with a complex configuration space. Here we demonstrate that conditional Generative Adversarial Networks (GANs) allow simulating the full parameter space of several many-body systems, accounting both for controlled parameters, and stochastic disorder effects. After training with a restricted set of noisy many-body calculations, the conditional GAN algorithm provides the whole dynamical excitation spectra for a Hamiltonian instantly and with an accuracy analogous to the exact calculation. We further demonstrate how the trained conditional GAN automatically provides a powerful method for Hamiltonian learning from its dynamical excitations, and to flag non-physical systems via outlier detection. Our methodology puts forward generative adversarial learning as a powerful technique to explore complex many-body phenomena, providing a starting point to design large-scale quantum many-body matter.

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Section: A Hamiltonian Learning With the Generative Modelmentioning

confidence: 99%

Designing quantum many-body matter with conditional generative adversarial networks

Koch¹,

Lado²

2022

Preprint

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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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“…Artificial neural network, as a powerful algorithm in machine learning to fit a specific function, has been widely used for solving quantum information problems, such as quantum optimal control [7,8], quantum maximum entropy estimation [9], Hamiltonian reconstruction [10], etc. Neural networks have all also been widely explored as a significant tool for QST, in applications such as recovering the information of local-Hamiltonian ground states from local measurements [11] efficiently, performing tomography on highly entangled state with large system size [12], mitigating the state-preparationand-measurement (SPAM) errors in experiments [13], and improving the state fidelity [14,15].…”

Section: Introductionmentioning

confidence: 99%

All-optical neural network quantum state tomography

Zuo,

Cao,

Cao

et al. 2021

Preprint

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Quantum state tomography (QST) is a crucial ingredient for almost all aspects of experimental quantum information processing. As an analog of the"imaging" technique in the quantum settings, QST is born to be a data science problem, where machine learning techniques, noticeably neural networks, have been applied extensively. In this work, we build an integrated all-optical setup for neural network QST, based on an all-optical neural network (AONN). Our AONN is equipped with built-in nonlinear activation function, which is based on electromagnetically induced transparency. Experiment results demonstrate the validity and efficiency of the all-optical setup, indicating that AONN can mitigate the state-preparation-and-measurement error and predict the phase parameter in the quantum state accurately. Given that optical setups are highly desired for future quantum networks, our all-optical setup of integrated AONN-QST may shed light on replenishing the alloptical quantum network with the last brick.

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Supervised learning in Hamiltonian reconstruction from local measurements on eigenstates

Cited by 22 publications

References 33 publications

Designing quantum many-body matter with conditional generative adversarial networks

Designing quantum many-body matter with conditional generative adversarial networks

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

All-optical neural network quantum state tomography

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