Bulletin of the American Physical Society
2023 APS March Meeting
Volume 68, Number 3
Las Vegas, Nevada (March 510)
Virtual (March 2022); Time Zone: Pacific Time
Session S62: Machine Learning for Quantum Matter IVFocus

Hide Abstracts 
Sponsoring Units: DCOMP Chair: Shiming Lei, Rice University Room: Room 417 
Thursday, March 9, 2023 8:00AM  8:36AM 
S62.00001: Enhancing Variational Monte Carlo with Neural Network Quantum States Invited Speaker: Stefanie Czischek Rydberg atom arrays are promising candidates for highquality quantum computation and quantum simulation. However, long state preparation times limit the amount of measurement data that can be generated at reasonable timescales. This restriction directly affects the estimation of operator expectation values, as well as the reconstruction and characterization of quantum states. 
Thursday, March 9, 2023 8:36AM  8:48AM 
S62.00002: Revealing phase diagrams of quantum systems with optimal predictors Julian Arnold, Frank Schäfer Neural networks (NNs) have been successfully used to determine phase diagrams from experimentally accessible data with little prior physical knowledge. Recently, we have replaced the NNs in several such phase classification methods for onedimensional phase diagrams featuring two distinct phases of matter by optimal nonparametric predictive models [1]. These optimal predictors can be constructed based on the probability distributions underlying the physical system and capture the output of NNs in the limit of high model capacity without explicit training, which allows for a more efficient detection of phase transitions from data. Here, we extend this framework to higherdimensional phase diagrams that may feature multiple distinct phases of matter. As an example, we map out the twodimensional phase diagram of a quantum manybody system from numerical data. 
Thursday, March 9, 2023 8:48AM  9:00AM 
S62.00003: Mitigating semiconductor device variability with machine learning Natalia Ares A concerning consequence of quantum device variability is that the tuning of each qubit in a quantum circuit constitutes a timeconsuming nontrivial process that has to be independently performed for each device, requiring a deep understanding of the particular device to be tuned and "muscle memory". I will show machinelearningbased approaches that can tune and characterise quantum devices completely automatically, regardless of the device architecture and the material realisation. Our algorithms are able to tune double quantum dot devices defined in Si FinFETs, Ge/Sicore/shell nanowires, and both SiGe and AlGaAs/GaAs heterostructures, successfully accommodating the different modes of gate operation (depletion/accumulation), disorder and noise characteristics. We report tuning times as fast as 10 minutes starting from scratch – well over an order of magnitude faster than what would be achievable by a dedicated expert human operator. Just as AlphaZero showed that the achievements of AlphaGo could be extended to learning to win at different board games without needing to be reprogrammed for each, so our result shows that control of complex quantum device circuits can be achieved using machine learning. 
Thursday, March 9, 2023 9:00AM  9:12AM 
S62.00004: A convolutional hamming distance metric for unsupervised learning of topological order Gebremedhin A Dagnew, Owen Myers, Chris M Herdman, Lauren E Hayward Sierens Machine learning algorithms have proven to be effective tools for the exploration of phases and phase transitions in manybody systems. Much prior work has focused on applying supervised and unsupervised machine learning algorithms to distinguish phases and identify phase transitions and crossovers in systems with local order parameters as well as those with topological phases that lack a local order parameter. In this work we study how the performance of unsupervised machine learning algorithms depend on the choice of a distance metric in configuration space. We introduce a metric based on the Hamming distance and a convolution of local patches of spins, which we call the convolutional hamming distance. We show that this distance metric allows us to identify topological order in classical Z_{2} and Z_{3} lattice gauge theories. Additionally we demonstrate how the choice of topologically nontrivial patches can distinguish the topological sectors of the ground state of Z_{2} gauge theory. We study various performance metrics as a function of patch and system size using a combination of unsupervised machine learning algorithms. The method introduced in this work has the potential to reduce the amount of physical input required for using unsupervised machine learning to identify phases and phase transitions from raw experimental or simulation data. 
Thursday, March 9, 2023 9:12AM  9:24AM 
S62.00005: Machine Learning for Optical Scanning Probe Nanoscopy Suheng Xu, Xinzhong Chen, Sara Shabani, Yueqi Zhao, Matthew Fu, Andrew Millis, Michael M Fogler, Abhay N Pasupathy, Mengkun Liu, Dmitri N Basov The ability to perform nanometerscale optical imaging and spectroscopy is key to deciphering the lowenergy effects in quantum materials, as well as vibrational fingerprints in planetary and extraterrestrial particles, catalytic substances, and aqueous biological samples. These tasks can be accomplished by scatteringtype scanning nearfield optical microscopy (sSNOM) technique that has recently spread to many research fields and enabled notable discoveries. Here, we would like to show that the sSNOM, together with scanning probe research in general, can benefit in many ways from artificial intelligence (AI) and machine learning (ML) algorithms. Augmented with AI and MLenhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent. 
Thursday, March 9, 2023 9:24AM  10:00AM 
S62.00006: Invited Talk: Cristian BonatoBayesian inference for quantum sensing and model learning Invited Speaker: Cristian Bonato The development of techniques for the characterization of quantum states and their dynamics is crucial for applications in quantum communication, computing, sensing and simulation. In quantum sensing, for example, physical parameters of interest can be measured, with quantumlimited sensitivity and high spatial resolution, by optimizing information extraction from a quantum sensor. 
Thursday, March 9, 2023 10:00AM  10:12AM 
S62.00007: Towards improving generalization of a neural network by interpretation for topological phases of matter Kacper J Cybinski, Marcin Plodzien, Michal Tomza, Maciej A Lewenstein, Alexandre Dauphin, Anna Dawid Machine learning (ML) promises a revolution in science, similarly as it has already revolutionized our everyday lives. In quantum physics, this tool is especially promising in the detection of phases of matter. However, ML models are also known for their blackbox construction, which hinders understanding of what they learn from the data and makes their application to novel data risky. Moreover, the central challenge of ML is to ensure its good generalization abilities, i.e., good performance on data outside the training set. Here, we show how the informed use of an interpretability method called class activation mapping (CAM) and its extensions increases the reliability of a neural network (NN) trained to classify quantum phases. In particular, we show that we can ensure better generalization in the complex classification problem by choosing such a model that, in the simplified version of the problem, learns a known characteristics of the phase. We show this on an example of the topological Su–Schrieffer–Heeger (SSH) model with and without the disorder. This work is an example of how the routine use of interpretability methods can improve the performance of ML in scientific problems. 
Thursday, March 9, 2023 10:12AM  10:24AM 
S62.00008: Learning by confusion: detecting phase transitions from Quantum Monte Carlo data Owen Bradley, Max Cohen, Richard T Scalettar We study the 'learning by confusion' technique for detecting phase transitions, applied to Quantum Monte Carlo (QMC) simulations of both the twodimensional Holstein model (a description of the electronphonon interaction) and the Hubbard model. Using a convolutional neural network (CNN) architecture, we compare the efficacy of various training data sets including snapshots of HubbardStratonovich fields and other imaginarytime resolved measurements, and discuss how these can be used to locate critical points. 
Thursday, March 9, 2023 10:24AM  10:36AM 
S62.00009: Digital Discovery of a Scientific Concept at the Core of Experimental Quantum Optics Sören Arlt, Mario Krenn, Carlos Ruiz Gonzalez, Mario Krenn Entanglement is a crucial resource for quantum technologies ranging from quantum communication to quantumenhanced measurements and computation. Finding experimental setups for these tasks is a conceptual challenge for human scientists due to the counterintuitive behavior of multiparticle interference and the enormously large combinatorial search space. Recently, new possibilities have been opened by artificial discovery where artificial intelligence proposes experimental setups for the creation and manipulation of highdimensional multiparticle entanglement. While digitally discovered experiments go beyond what has been conceived by human experts, a crucial goal is to understand the underlying concepts which enable these new useful experimental blueprints. We present Halo (Hyperedge Assembly by Linear Optics), a concept that was used by our digital discovery framework to solve previously open questions. We were able to apply Halo to the generation of highly entangled states, communication in quantum networks, and photonic quantum gates. Our work demonstrates how artificial intelligence can act as a source of inspiration for scientific discovery and how it can be used to formulate new actionable concepts in physics. 
Thursday, March 9, 2023 10:36AM  10:48AM 
S62.00010: From 4DSTEM data to interpretable physics — an unsupervised learning approach to the charge order physics in TaS_{2} Haining Pan, Krishnanand M Mallayya, James L Hart, Judy J Cha, EunAh Kim The increasing volume and complexity of data from modern probes call for new datacentric approaches for connecting the data to scientific insight. 4dimensional scanning transmission electron microscopy (4DSTEM) data provides complete imaging of the 2D electron diffraction patterns at each spatial pixel, opening access to rich physics of large scale spatial variation in charge distribution and atomic structure. However, the large dimensionality of the dataset, especially when taken under a variation in a control knob such as temperature, quickly overwhelms traditional mode of data analysis. To harness the new information 4DSTEM can offer, we adopt an unsupervised machinelearning technique, Xray TEmperature series Clustering (XTEC) recently developed for voluminous Xray data [1]. We focus on rich charge density wave ordering phenomenology of transition metal dichalcogenide, specifically, 1TTaS_{2}, to distinguish between the commensurate and nearlycommensurate charge density wave. Extending XTEC to 4DSTEM, we reproduce known charge density wave ordering temperature and find nearly commensurate regions. I will discuss the implications of the findings for the physics of TaS_{2} as well as the prospect of using the new approach more broadly. 
Follow Us 
Engage
Become an APS Member 
My APS
Renew Membership 
Information for 
About APSThe American Physical Society (APS) is a nonprofit membership organization working to advance the knowledge of physics. 
© 2024 American Physical Society
 All rights reserved  Terms of Use
 Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 207403844
(301) 2093200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 5914000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 200452001
(202) 6628700