Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session B53: Autonomous ControlFocus
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Sponsoring Units: GDS DQI Chair: Maria Longobardi, University of Basel, Switzerland Room: Room 307 |
Monday, March 6, 2023 11:30AM - 11:42AM |
B53.00001: System optimization of superconducting qubit readout for quantum error correction Andreas Bengtsson, Alexander M Opremcak, Mostafa Khezri, Daniel T Sank, Paul V Klimov, Julian Kelly, Jimmy Chen An accurate measurement of a qubit register is a crucial component of quantum algorithms and error correction. For mid-circuit measurements there is a tradeoff between accuracy and duration, since other parts of the processors might decohere during the measurement. |
Monday, March 6, 2023 11:42AM - 11:54AM |
B53.00002: Bridging the reality gap in quantum devices with physics-aware machine learning David L Craig, Hyungil Moon, Federico Fedele, Dominic T Lennon, Barnaby van Straaten, Florian Vigneau, Leon C Camenzind, Dominik M Zumbuhl, G. Andrew D Briggs, Michael A Osborne, Dino Sejdinovic, Natalia Ares The discrepancies between reality and simulation impede the optimisation and scalability of solid-state quantum devices. Disorder induced by the unpredictable distribution of material defects is one of the major contributions to the reality gap. We bridge this gap using physics-aware machine learning, in particular, using an approach combining a physical model, deep learning, Gaussian random field, and Bayesian inference. This approach has enabled us to infer the disorder potential of a nanoscale electronic device from electron transport data. This inference is validated by verifying the algorithm's predictions about the gate voltage values required for a laterally-defined quantum dot device in AlGaAs/GaAs to produce current features corresponding to a double quantum dot regime. The generality of our approach and the minimal data required for inference are promising qualities for future utility in understanding nanoscale quantum devices. |
Monday, March 6, 2023 11:54AM - 12:06PM |
B53.00003: Developing robust protocols for calibration of a 25-qubit annealing device Bryce I Fisher, Steven M Disseler, Steven J Weber, Robert Rood, Cyrus F Hirjibehedin, Mallika T Randeria, Vladimir Bolkhovsky, John Cummings, Rabindra Das, David K Kim, Jeffrey Knecht, Justin L Mallek, Bethany M Niedzielski, Ravi Rastogi, Kyle Serniak, Donna-Ruth W Yost, Scott Zarr, Mollie E Schwartz, Steven J Weber, William D Oliver, Jonilyn L Yoder Superconducting qubits require varied and novel tools for calibration. As qubits mature in design and fabrication we should also mature the software utilized for calibration. Sophisticated calibration protocols could enable faster, more accurate, and more robust tune-up of a superconducting qubit processor. We will provide details on our calibration routines for bringing online a 25-qubit quantum annealing testbed comprising XX single-qubit controls and YY coupler controls, all of which must be characterized individually and as a crosstalk matrix. We hope to provide context on what mature calibration software can look like and evidence of the benefits it can provide. |
Monday, March 6, 2023 12:06PM - 12:18PM |
B53.00004: System Optimization of Gate Frequencies for Surface Code Quantum Error Correction Paul V Klimov A critical component of operating quantum processors is mitigating computational errors from energy-relaxation, dephasing, leakage, and control inaccuracies. In superconducting qubits, these sources of error can arise from control-electronics noise, control-pulse distortions, parasitic coupling between computational elements, and more. In frequency-tunable qubit architectures, it’s possible to mitigate these sources of error by choreographing qubit gate frequencies over the course of quantum algorithms. This choreography maps to constructing and optimizing a high-dimensional, high-constraint, non-convex, and time-dependent objective over an astronomical search space. In this talk, I will introduce the frequency optimization problem and the Snake optimizer [1, 2] that we developed to solve it for Google’s flagship quantum processors. Finally, I will discuss the problem in the context of our team’s distance-5 surface code quantum error correction demonstration on 49 qubits of a Sycamore processor [3]. |
Monday, March 6, 2023 12:18PM - 12:30PM |
B53.00005: Towards Robust Automation of Quantum Dot Bootstrapping Danielle J Middlebrooks, Justyna P Zwolak The tuning and management of quantum dot qubits is a large and complex task. The tools used for automated tuning schemes vary from simple fittings to heuristically defined algorithms to traditional computer vision techniques. A host of machine-learning-based techniques have also been utilized. However, most of the tuning efforts focused on the more advanced phases of tuning, assuming that the device is already pre-tuned, with a properly calibrated charge sensor and that the "safety" regimes for all gates are already known. However, the initial phase of tuning – the so-called "device bootstrapping" – is still nearly always done heuristically, requiring a highly trained researcher to be responsible for the subsequent decisions on how to adjust the relevant parameters. We develop an automated routine to bridge the gap between the initial device cool-down and a voltage configuration in which other previously developed automation schemes can take over for a multiple quantum dot device. The result of this autotuning procedure provides a sufficient starting point for the wide-ranging set of tasks for control of quantum dot qubits. The initial device bootstrapping routine can also be used as a device-screening process to determine which devices should be used for qubit formation and which devices should be discarded. |
Monday, March 6, 2023 12:30PM - 12:42PM |
B53.00006: Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning Brandon Severin, Dominic T Lennon, Leon C Camenzind, Florian Vigneau, Federico Fedele, Daniel Jirovec, Andrea Ballabio, Daniel Chrastina, Giovanni Isella, Mathieu de Kruijf, Miguel J Carballido, Simon Svab, Andreas V Kuhlmann, Floris Braakman, Simon Geyer, Florian N Froning, Hyungil Moon, Michael A Osborne, Dino Sejdinovic, Georgios Katsaros, Dominik M Zumbuhl, G. Andrew D Briggs, Natalia Ares The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions. We give a key step towards tackling this variability with an algorithm that, without modification, is capable of tuning a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate Ge/SiGe heterostructure double quantum dot device from scratch. We achieve tuning times of 30, 10, and 92 minutes, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning. |
Monday, March 6, 2023 12:42PM - 12:54PM |
B53.00007: All RF-based tuning algorithm for quantum devices using machine learning Barnaby van Straaten, Federico Fedele, Florian Vigneau, Joseph Hickie, Andrea Ballabio, Daniel Chrastina, Georgios Katsaros, Daniel Jirovec, Natalia Ares Radio-frequency measurements could satisfy DiVincenzo's readout criterion in future large-scale solid-state quantum processors, as they allow for high bandwidths and frequency multiplexing. To realise the potential for scalability of this readout technique, quantum device tuning will have to be performed using just RF measurements, making no use of measurements of current through the device. By exploiting their bandwidth and impedance matching, we demonstrate an algorithm that automatically tunes double quantum dots with only radio-frequency measurements. The tuning was completed within a few minutes without prior knowledge about the device architecture. Our results show that it is possible to eliminate the need for transport measurements for quantum dot tuning, paving the way for more scalable device architectures. |
Monday, March 6, 2023 12:54PM - 1:30PM |
B53.00008: Automating microscopy with machine learning: from object identification to hypothesis learning Invited Speaker: Maxim Ziatdinov Machine learning and artificial intelligence are becoming increasingly important components in physics research, with applications ranging from high-energy physics to materials sciences. Recently, there has been a growing interest in using AI models that interact with physical systems, rather than being pre-trained on large datasets, for tasks such as materials discovery, chemical synthesis, and physical property measurements. Microscopy is particularly well-suited for these active learning tasks as it combines aspects of materials discovery, physics learning, and synthesis through atomic fabrication. In this presentation, I will discuss advances in using machine learning for automated experiments in electron and scanning probe microscopies, including on-the-fly object detection, atomic defects engineering in quantum materials, and physics discovery through active learning. I will also address challenges such as out-of-distribution drift in traditional deep learning methods and the limitations of simple Gaussian processes-based approaches for active learning in complex systems. I will propose solutions such as ensemble learning and iterative training (ELIT), deep kernel learning, and structured Gaussian processes allowing for exploring complex systems and discovering structure-property relationships in an autonomous fashion. Finally, I will discuss the edge computing infrastructure needed for turning modern-day microscopes into autonomous platforms for scientific discovery. |
Monday, March 6, 2023 1:30PM - 1:42PM |
B53.00009: Automated and autonomous scanning probe experiments for manipulating and measuring domain wall properties in ferroelectric thin films Sai M Valleti, Yongtao Liu, Bharat Pant, Shivaranjan Raghuraman, Maxim Ziatdinov, Jan-Chi Yang, Ye Cao, Stephen Jesse, Sergei V Kalinin, Rama K Vasudevan Domain walls in polar materials are a playground for investigating novel physics, due to the symmetry breaking that occurs at the wall, and the corresponding unique functional properties they can exhibit.1
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Monday, March 6, 2023 1:42PM - 1:54PM |
B53.00010: Designing Models for Autonomous Small-Angle Scattering Measurements of Industrial Soft Materials Tyler B Martin, Peter Beaucage The Autonomous Formulation Laboratory (AFL) program at the National Institute of Standards and Technology (NIST) develops autonomous platforms that enhance the characterization of complex industrial formulations. A key challenge of our program is building data pipelines and models that can interpret the measurements of messy and non-ideal material systems brought by our stakeholders. This means that simple machine-learning models built from perfectly ideal theories often do not perform well on the real data we encounter in our work. In this talk, we will cover our efforts to develop tools that can correctly label (i.e., identify, classify, label, cluster) small-angle scattering data of soft-materials. We will highlight the unique challenges of working with soft-material systems including dealing with second-order phase transitions and the presence of mixed and non-equilibrium phases. We will show the benefits and drawbacks of various supervised and unsupervised approaches and how they connect into our autonomous agent. Finally, we will demonstrate how these methods work as part of a phase exploration loop during small-angle neutron and X-ray scattering experiments. |
Monday, March 6, 2023 1:54PM - 2:06PM |
B53.00011: A Bayesian optimized spectral recommender system with dynamic human-guided targets for physics discovery Arpan Biswas, Yongtao Liu, Rama K Vasudevan, Maxim Ziatdinov The combination of computational design, optimization, and machine learning with large scale microscopic data analysis, has led to the automated experiments towards rapid discovery of physics. In finding optimal parameters for desired material properties where the functional maps between them is unknown or expensive, a Bayesian optimization (BO) is well suited due to handling of any black-box objective functions (numerical functional forms are not required) and adaptive sampling technique for minimal expensive evaluations for convergence. However, the BO applications are generally limited to a prior set target material property, and in experimental analysis, often it is very complex to priorly know such properties. Here, we have extended the BO application where the user sequentially learns and update the target properties as the optimization progress, and thereby build a BO-based spectral recommendation system (BO-SRS). As the BO progresses, the recommender first input a spectral, then evaluates the quality of the spectral through a custom build objective function based on the user (domain expert) decision and set/updated current target material properties, then the BO samples the next best location towards spectral of similar material properties as the current target. We demonstrate the performance of BO-SRS workflow on both piezoresponse force spectroscopy as well as current-voltage (I-V) spectroscopy on complex oxide samples, with multiple users and different user goals. This stated automated human-augmented approach allows for rapid device parameter selection for a complex priorly unknown map between the application and the respective material properties. |
Monday, March 6, 2023 2:06PM - 2:18PM Author not Attending |
B53.00012: Updates to an MeV Ultrafast Electron Diffraction (MUED) System for Data Analysis and Control using Machine Learning Trudy B Bolin, Salvador Sosa Guitron, Aasma Aaslam, Sandra G Biedron An MeV ultrafast electron diffraction (MUED) instrument system, such as is located at the Accelerator Test Facility (ATF) of Brookhaven National Laboratory, is a structural characterization technique suited to investigate dynamics in the ultrashort range in a variety of materials via a laser pump method. It is a unique characterization technique especially suitable for highly correlated materials. This technology can be advanced further into a turnkey instrument by using data science and artificial intelligence (AI) mechanisms in conjunction with high-performance computing. This can facilitate automated operation, data acquisition, and real-time or near-real-time processing. The AI-based system controls can provide real-time feedback on the electron beam or provide virtual diagnostics of the beam. Deep learning can be applied to the MUED diffraction patterns to recover valuable information on subtle lattice variations that can lead to a greater understanding of a wide range of material systems. A data-science-enabled MUED facility will also facilitate the application of this technique, expand its user base, and provide a fully automated state-of-the-art instrument. Another beamline enhancement planned is the extension of the beamline sample area to include additional instrumentation for simultaneous measurement of a standard baseline sample. EM modeling of the beamline components facilitates this. Updates on research and development efforts for the MUED instrument are presented. |
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