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Future Computing Systems and AI


Quantum dots (QDs) are a promising quantum computing approach that over the past years have been gaining popularity as candidate building blocks for solid-state quantum devices. Unfortunately, the difficulty of working with such systems—where fabrication tolerances are tight, impurities punishing, and material considerations vast—has meant that only a few groups around the world have succeeded in advancing the limit of QD performance. The current practice of tuning QDs manually or in a semi-automated fashion is a relatively time-consuming procedure, susceptible to random errors and inherently impractical for scaling up and other applications. The presence of defects and variations in the local composition of the heterostructure disordering the background potential energy further impedes this process. The goal of this project is to replace the current practice of manual tuning to a desirable electronic configuration with a standardized automated method.



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Electrons confined in arrays of semiconductor nanostructures, called quantum dots (QDs), are a promising quantum computing approach. Due to the ease of control of the relevant parameters, fast measurement of the spin and charge states, relatively long decoherence times, and their potential for scalability, QDs are gaining popularity as candidate building blocks for solid-state quantum devices. However, with an increasing number of QD qubits, the relevant parameter space grows sufficiently to make heuristic control unfeasible. In semiconductor quantum systems, devices now have tens of individual electrostatic and dynamical gate voltages that must be carefully set to localize the system into the single-electron regime and to realize good qubit operational performance. It is thus highly desirable to have an automated protocol to achieve a target electronic state. 

The mapping of requisite QD locations and charges to gate voltages presents a challenging classical control problem. In recent years, there has been a considerable effort to automate every phase of device calibration and control, from taking a system from room temperature to quantum operation. The Future Computing Systems and AI project is an effort to automate all stages of device characterization and control, from device bootstrapping to readout and gates optimization. Our efforts rely on combining script-based and machine learning (ML) with classical optimization techniques to establish an automated closed-loop system for experimental control. To train and test all ML algorithms we use synthetic data generated using a physical model. To date, this approach has proven effective in yielding efficient, scalable control of multiple stages of device calibration. Our work serves as a baseline for future investigation of fully automated device control system and to pave the way for similar approaches in a wide range of experiments in physics. Further integration of theoretical, computational, and experimental efforts with computer science and ML holds vast potential in advancing semiconductor and other platforms for quantum computing.

All publications related to project

  • J. Ziegler, F. Luthi, M. Ramsey, F. Borjans, G. Zheng, and J. P. Zwolak, “Tuning arrays with rays: Physics-informed tuning of quantum dot charge states.” Phys. Rev. Applied 20 (3), 034067 (2023). doi:10.1103/PhysRevApplied.19.054077
  • J. Ziegler, F. Luthi, M. Ramsey, F. Borjans, G. Zheng, and J. P. Zwolak, “Automated extraction of capacitive coupling for quantum dot systems.” Phys. Rev. Applied 19 (5), 054077 (2023). doi:10.1103/PhysRevApplied.19.054077
  • J. P. Zwolak and J. M. Taylor, “Colloquium: Advances in automation of quantum dot devices control.” Rev. Mod. Phys. 95 (1), 011006 (2023). doi:10.1103/RevModPhys.95.011006
  • J. Ziegler, T. McJunkin, E. S. Joseph, S. S. Kalantre, B. Harpt, D. E. Savage, M. G. Lagally, M. A. Eriksson, J. M. Taylor, and J. P. Zwolak, “Toward Robust Autotuning of Noisy Quantum Dot Devices.” Phys. Rev. Applied 17(2): 024069 (2022). doi:10.1103/PhysRevApplied.17.024069
  • B. J. Weber, S. S. Kalantre, T. McJunkin, J. M. Taylor, and J. P. Zwolak, “Theoretical bounds on data requirements for the ray-based classification.” SN Comput. Sci. 3(1): 57 (2022). doi:10.1007/s42979-021-00921-0
  • J. P. Zwolak, T. McJunkin, S. S. Kalantre, S. F. Neyens, E. R. MacQuarrie, M. A. Eriksson, and J. M. Taylor, “Ray-based framework for state identification in quantum dot devices.” PRX Quantum 2(2): 020335 (2021). doi:10.1103/PRXQuantum.2.020335
  • J. P. Zwolak, S. S. Kalantre, T. McJunkin, B. J. Weber, and J. M. Taylor, “Ray-based classification framework for high-dimensional data.” Proceedings of Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada [December 11, 2020] (2020).
  • J. P. Zwolak, T. McJunkin, S. S. Kalantre, J. P. Dodson, E. R. MacQuarrie, D. E. Savage, M. G. Lagally, S. N. Coppersmith, M. A. Eriksson, and J. M. Taylor, “Autotuning of double-dot devices in situ with machine learning.” Phys. Rev. Applied 13(3): 034075 (2020). doi:10.1103/PhysRevApplied.13.034075
  • S. S. Kalantre, J. P. Zwolak, S. Ragole, X. Wu, N. M. Zimmerman, M. D. Stewart, and J. M. Taylor, “Machine Learning techniques for state recognition and auto-tuning in quantum dots.” npj Quantum Inf. 5(6): 1--10 (2019). doi:10.1038/s41534-018-0118-7
  • J. P. Zwolak, S. S. Kalantre, X. Wu, S. Ragole, and J. M. Taylor, “QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments.” PLoS ONE 13(10): e0205844 (2018). doi:10.1371/journal.pone.0205844
Created May 31, 2023, Updated October 20, 2023