Bulletin of the American Physical Society
53rd Annual Meeting of the APS Division of Atomic, Molecular and Optical Physics
Volume 67, Number 7
Monday–Friday, May 30–June 3 2022; Orlando, Florida
Session Z03: Focus Session: Machine Learning for Quantum Gases: Theory and ExperimentFocus Live Streamed
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Chair: Shah Saad Alam, Rice Room: Grand Ballroom B |
Friday, June 3, 2022 10:30AM - 11:00AM |
Z03.00001: Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics Invited Speaker: Agnes Valenti 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. 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 sized bosonic ladder system using an accessible amount of experimental measurements. We are able to significantly increase the known parameter precision and benchmark our results using Bayesian inference. |
Friday, June 3, 2022 11:00AM - 11:30AM |
Z03.00002: AI-Assisted Detection of Correlations in Snapshots of Ultracold Atoms Invited Speaker: Ehsan Khatami The physics of strongly correlated phases of matter is often described in terms of straightforward electronic patterns. However, the most interesting phases might not be immediately accessible via observables that couple to simple patterns. In this talk, I will argue that both supervised and unsupervised machine learning methods can sometimes be used as alternative tools for the discovery of such phases and the visualization of correlations. I will focus on the Fermi-Hubbard models realized with ultracold atoms in optical lattices and show examples where artificial intelligence can detect correlations unique to phases with no obvious order parameter or previously known signatures in projective measurements. |
Friday, June 3, 2022 11:30AM - 11:42AM |
Z03.00003: Using Physics to Explain and Interpret Convolutional Neural Network Solutions of Quantum Spin Problems Jonathan Minoff, Shah Saad Alam, Yilong Ju, Fabio Anselmi, Ankit B Patel, Han Pu Convolutional neural networks (CNNs) have been employed alongside Variational Monte Carlo methods for finding the ground state of quantum spin Hamiltonians. In order to do so, however, a CNN with linearly many variational parameters has to successfully approximate a wavefunction on an exponentially large Hilbert space. We examine the details of how a CNN optimizes learning for spin systems, and the role played by physical symmetries during training. We then also demonstrate a method for using the symmetries of the underlying spin system to propose an improved training algorithm. Finally, we present a mapping between the wavefunctions generated by a one-layer CNN, a Correlator Product State (CPS), and the maximum entropy (MaxEnt) principle, thus providing physical insight as to why the CNN is able to solve this problem efficiently. |
Friday, June 3, 2022 11:42AM - 11:54AM |
Z03.00004: Potential energy surface for AlF-AlF collisions Weiqi Wang, Xiangyue Liu, Jesus Perez Rios This work presents a new approach to generating diatom-diatom potential energy surfaces (PES) through machine learning techniques. In particular, after using some energies at given geometries calculated at CCSD(T) (coupled cluster with single and double and perturbative triple excitations) level of theory, we employ a Gaussian process regression method based on a particular set of molecular features valid for all range of distances, i.e., a single model works for the long-range and short-range region of the PES. Finally, as an example, we calculate the PES for AlF-AlF and the density of states and lifetime of intermediate complexes via the developed machine learning approach. |
Friday, June 3, 2022 11:54AM - 12:06PM |
Z03.00005: Entropy: A Lab Manager Software That Lowers the Lab Entropy and Boosts Your Productivity Nikola Sibalic, Guy Kerem, Gal Winer Running computations on Quantum processors requires calibration of a growing number of qubits while allowing execution of complex control sequences. This joins a host of additional challenges such as collecting, sorting, and gaining insight into the experimental data records. However, human real-time cognitive processing capacity in the lab remains limited. To scale up, we need to provide a good contextual interface between experimental protocol, results, and narrative. |
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