Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session T47: Machine Learning for Quantum Matter IIIFocus Recordings Available

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Sponsoring Units: DCOMP GDS DMP Chair: Javier Robledo Moreno, New York University (NYU) Room: McCormick Place W470B 
Thursday, March 17, 2022 11:30AM  12:06PM 
T47.00001: Tuning quantum dot arrays with rays Invited Speaker: Justyna P Zwolak Current semiconductorbased quantum computing approaches rely upon achieving control of nanocircuits at the singleelectron level and using them as quantum bits (qubits). Establishing a stable configuration of spins in quantum dot (QD) devices is accomplished by a combination of electrostatic confinement, bandgap engineering, and dynamical control via nearby electrical gates. However, with an increasing number of QD qubits, the relevant parameter space grows exponentially, making heuristic control unfeasible. In semiconductor quantum computing, devices now have tens of individual electrostatic and dynamical gate voltages that must be carefully set to isolate the system to the singleelectron regime and to realize good qubit performance. Largescale quantum processors hinge on fully autonomous tuning processes that can be parallelized for practical applications. 
Thursday, March 17, 2022 12:06PM  12:18PM 
T47.00002: Unsupervised learning of quantum phases with topological order Yanting Teng, Subir Sachdev, Mathias S Scheurer Machine learning methods are emerging as powerful tools for detecting quantum phase transitions from simulated or experimental data. Most successes in this regard, have been achieved in the context of phases with a local order parameter, which can be described by the Landau theory of symmetry breaking. However, it was shown that phases that do not have such local order parameters but depend on the underlying global topology are much more difficult to capture. While there have been recent successes with unsupervised learning of topological phase transitions of classical models or noninteracting quantum systems, it remains an open question whether machine learning methods can identify interacting quantum phases of topological nature without involved feature engineering. 
Thursday, March 17, 2022 12:18PM  12:30PM 
T47.00003: Machinelearning discovery of descriptors for Topological Semimetals YANJUN LIU, Wesley J Maddox, Milena Jovanovic, Sebastian Klemenz, Andrew G Wilson, Leslie M Schoop, EunAh Kim The accumulation of massive amounts of materials data motivates databased machine learning(ML) approaches. 
Thursday, March 17, 2022 12:30PM  12:42PM 
T47.00004: Bragg glass signatures in the disordered charge density wave material PdxErTe3 from Xray diffraction data using unsupervised machine learning Krishnanand M Mallayya, Michael Matty, Joshua A Straquadine, Matthew J Krogstad, Maja D Bachmann, Anisha G Singh, Stephan Rosenkranz, Raymond Osborn, Ian R Fisher, EunAh Kim Vestigial nematic and Bragg glass are new phases that emerge when an incommensurate longrange ordered charge density wave (CDW) gets suppressed by a quenched disorder. Pd intercalated rare earth tritelluride (PdxRTe3) has emerged as a promising model system to systematically investigate these emergent phases when the bidirectional orthogonal CDW order of RTe3 interacts with a controlled amount of disorder (Pd intercalation). Using Xray diffraction Temperature Clustering (XTEC), an unsupervised and interpretable machine learning technique introduced in Ref. [1], we extract signatures of Bragg glass and vestigial nematic phases in PdxErTe3 from a large volume of Xray temperature series data spanning 20000 Brillouin zones, collected using the Pilatus 2M CdTe detector on Sector 6IDD at the Advanced Photon Source. In addition to identifying the two order parameters from the CDW peak intensities, XTEC probes the temperature dependence of the CDW peak widths and the disorder induced asymmetry in the diffuse scattering. XTECenabled extraction of these otherwise subtle signatures from the data helps us arrive at a phase diagram. Our results suggest that Pd intercalation turns the longrange ordered CDW into the Bragg glass phase, providing first Xray based detection of the Bragg glass phase. 
Thursday, March 17, 2022 12:42PM  12:54PM 
T47.00005: Neural networkbased approach to analytic continuation Maciej M Maska, Maksymilian Kliczkowski While most quantum Monte Carlo methods provide Green's function of imaginary time, the dynamical quantities relevant to experiments need to be expressed in real frequency. The standard approach that allows one to obtain these functions is based on the MaxEnt method. Unfortunately, this is an illconditioned problem where small errors in the imaginary time Green's functions lead to large errors in the real frequency quantities. And since the Monte Carlo results always have some statistical errors, in order to obtain reliable results this procedure must be iteratively repeated multiple times and then averaged. 
Thursday, March 17, 2022 12:54PM  1:06PM 
T47.00006: Machine learning discovery of new phases in programmable Rydberg quantum simulator snapshots Cole M Miles, Rhine Samajdar, Sepehr Ebadi, Tout T Wang, Hannes Pichler, Markus Greiner, Kilian Q Weinberger, Subir Sachdev, Mikhail Lukin, EunAh Kim Machine learning has recently emerged as a promising approach for studying the complex and rich datasets produced by projective measurements of quantum simulators. In particular, datacentric approaches lend to the possibility of automatically discovering structure in the experimental dataset which may be missed by manual inspection. Here, we introduce a unsupervisedsupervised hybrid machine learning approach, hybridcorrelation convolutional neural network (HybridCCNN), and apply it to study experimental squarelattice Rydberg atom simulator snapshots to discover two new hidden phases. The initial unsupervised dimensionality reduction and clustering first revealed five distinct phase regions. We then refined these boundaries and identified each phase by training CCNNs with learnable spatial weighting and interpreting their learning. The characteristic spatial weightings and snippets of correlations specifically recognized in each phase captured the nature of quantum fluctuations in the striated phase and mapped out the phase space regions for two previously unknown phases, the rhombic and edgeordered phases. Hence, we establish that machine learning approaches can enable discoveries of correlated and entangled quantum states hidden in large volumes of experimental snapshot data from finite size quantum simulators. 
Thursday, March 17, 2022 1:06PM  1:18PM 
T47.00007: Construction of Bayesian mixture model for efficient calculation of electronhole interaction kernel in quantum dots Arindam Chakraborty, Nicole Spanedda, Chengpeng Gao This work presents the development and implementation of the Bayesian mixture model for a computationally efficient calculation of electronically excited states in semiconductor quantum dots. In the Bayesian quasiparticle kernel method, the electronhole interaction kernel is constructed iteratively from realspace sampling of the singleparticle states in the quantum dot. For a given particlehole excitation, the transition density matrix is expressed as a Gaussian Mixture Model and the mixing coefficients are obtained using an iterative Bayesian updating procedure. The quasiparticle kenel was constructed using 2ndorder manybody perturbation theory and was applied for the calculation of exciton binding energies and biexciton binding energies in PbS, CdS, PbSe, and CdSe quantum dots. The quality of the Bayesian quasiparticle kernel was independently tested by comparing the ionization potentials obtained from nonmachinelearning based methods for these quantum dots. The results from these calculations highlight the application of Bayesian quasiparticle kernel method as a fast and accurate firstprinciples method for investigating excited states properties of large chemical systems. 
Thursday, March 17, 2022 1:18PM  1:30PM 
T47.00008: Autonomous identification of quantum dot device failure modes Josh E Ziegler, Florian Luthi, Mick Ramsey, Thomas F Watson, Justyna P Zwolak Gatedefined quantum dots have appealing attributes as a quantum computing platform, however nearterm devices possess a range of possible imperfections. Recent efforts have introduced synthetic noise into simulated data[1,2] and a framework to avoid failures when tuning up nonideal devices [2]. However, resolving certain failure modes requires targeted identification of specific nonidealities. By incorporating into a quantum dot simulator sources of physical imperfections that go beyond noise, we expand the framework proposed in Ref. [2] to identify common defects and enable autonomous targeted device recalibration. Among others, we’re developing systems to autonomously flag devices with unintended dots near the operating regime and for identifying high levels of telegraph noise. These autonomous systems will enable both high throughput screening of quantum dot devices as well as more reliable tuning to a regime suitable for qubit operations. 
Thursday, March 17, 2022 1:30PM  1:42PM 
T47.00009: Machine learning for quantum spin dynamics in and out of equilibrium Puhan Zhang, Sheng Zhang, GiaWei Chern We propose a new numerical framework based on machine learning (ML) potentials to enable largescale adiabatic quantum LandauLifshitzGilbert (LLG) dynamics simulations of itinerant electron magnets. Such metallic spin systems are central to novel phenomena such as colossal magnetoresistance and spintransfer torques. Our approach is similar in spirit to the BehlerParrinello ML scheme that has become a cornerstone of largescale molecular dynamics method with the accuracy of quantum calculation. Based on the principle of locality for electronic systems, the total electronic energy is partitioned into contributions from individual spins which depend only on the local environment. A neural network model is then trained from exact solutions on small systems to approximate the complex dependence of the local energy on the neighborhood spin configuration. We further develop a novel descriptor to ensure the spin rotation symmetry as well as the discrete lattice symmetry. Our work opens new avenues for using deeplearning models to simulate and understand largescale dynamical phenomena in functional magnetic systems. 
Thursday, March 17, 2022 1:42PM  1:54PM 
T47.00010: Automated derivation of effective models for quantum impurity models Jonas B Rigo, Andrew K Mitchell The theoretical description of quantum transport through nanodevices such as molecular junctions necessitates the formulation of models that are sufficiently complex to capture strong electronic interactions and orbital/chemical structure, but simple enough to be treated with methods like the Numerical Renormalisation Group (NRG). The sheer diversity of different molecules requires extremely flexible effective models, capable of faithfully representing the properties of such systems. To overcome these difficulties we introduce a Machine Learning algorithm that combines convex and nonconvex optimization to automatically derive a symmetryinspired effective impurity model, that is simple enough to be simulated using NRG. The resulting model can be used to carry out highly accurate conductance calculations, which we benchmark against bruteforce calculations for the simplest systems. The methodology will allow an accurate treatment of larger strongly correlated systems, beyond present capabilities. 
Thursday, March 17, 2022 1:54PM  2:06PM 
T47.00011: Application of Variational Autoencoder to Detect Critical Points of Anisotropic Classical Model Anshumitra Baul, Nicholas Walker, Juana Moreno, KaMing Tam The macroscopic properties of a physical system change at a phase transition. In most instances, the phase is identified using an order parameter consistent with the symmetry of the underlying Hamiltonian. We use recent advances in Machine Learning, in particular Autoencoders, to detect a phase transition just by identifying changes in the patterns of Quantum Monte Carlo data across the transition region, and without explicitly constructing any order parameter. We generalize previous studies on the application of variational autoencoders to the anisotropic twodimensional Ising model. We identify the phase diagram for a wide range of anisotropic couplings and temperatures via a variational autoencoder without the explicit construction of an order parameter. Our phase diagram agrees with the one produced using the selfduality property of the model. Considering that the partition function of d + 1dimensional anisotropic models can be mapped to that of the ddimensional quantum spin models, this study provides numerical evidence that a variational autoencoder can be applied to analyze quantum systems. 
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