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UID:UW-Physics-Event-6859
DTSTART:20220331T150000Z
DTEND:20220331T160000Z
DTSTAMP:20260414T154051Z
LAST-MODIFIED:20220207T155236Z
LOCATION:5310 Chamberlin
SUMMARY:ML\, ray-based framework for tuning quantum dot devices: Two d
 ots and beyond\, R. G. Herb Condensed Matter Seminar\, Justyna Zwolak\
 , NIST
DESCRIPTION:Arrays of quantum dots (QDs) are one of the many candidate
  systems to realize qubits--the fundamental building blocks of quantum
  computers--and provide a platform for quantum computing [1]. However\
 , the current practice of manually tuning QDs is a relatively time-con
 suming procedure\, inherently impractical for scaling up and other app
 lications. Recently\, we have proposed an auto-tuning paradigm that co
 mbines a machine learning (ML) algorithm with optimization routines to
  assist experimental efforts in tuning semiconductor QD devices [2\,3]
 . Our approach provides a paradigm for fully-automated experimental in
 itialization through a closed-loop system that does not rely on human 
 intuition and experience.<br>
\n <br>
\nTo address the issue of tuning
  arrays in higher dimensions\, we expand upon our prior work and propo
 se a novel approach in which we "fingerprint" the state space instead 
 of working with full-sized 2D scans of the gate voltage space. Using 1
 D traces ("rays") measured ("shone") in multiple directions\, we train
  an ML algorithm to recognize the relative position of the features ch
 aracterizing each state (i.e.\, to "fingerprint") in order to differen
 tiate between various state configurations. I will report the performa
 nce of the ray-based learning when used off-line on experimental scans
  of a double dot device and compare it with our existing\, CNN-based a
 pproach [4]. I will also discuss how it extends to higher-dimensional 
 systems. Using rays not only allows us to automate the recognition of 
 states but also to significantly reduce (e.g.\, by 70 % for the two-do
 ts case) the number of measured points required for tuning.<br>
\n <br
 >
\n[1] D. Loss and D. P. DiVincenzo. Phys. Rev. A\, 57: 120–126\, 1
 998.<br>
\n[2] S. S. Kalantre\, J. P. Zwolak\, S. Ragole\, X. Wu\, N. 
 M. Zimmerman\, M. D. Stewart\, Jr.\, J. M. Taylor. Machine learning te
 chniques for state recognition and auto-tuning in quantum dots. npj Qu
 antum Inf. 5 (6): 1–10 (2019). <br>
\n[3] 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\, J. M. Taylor. Auto
 tuning of Double-Dot Devices In Situ with Machine Learning. Phys. Rev.
  Applied 13\, 034075 (2020).<br>
\n[4] 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\, 020335 (2021).<br>
\n
URL:https://www.physics.wisc.edu/events/?id=6859
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