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Compressed Optimization of Device Architectures for Semiconductor Quantum Devices

Journal Article · · Physical Review Applied
 [1];  [2];  [3];  [3];  [1];  [1];  [4]
  1. Univ. of Wisconsin, Madison, WI (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Microsoft Research, Redmond, WA (United States)
  3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  4. Univ. of Wisconsin, Madison, WI (United States); Univ. of New South Wales, Sydney, NSW (Australia)
+ Show Author Affiliations
Recent advances in nanotechnology have enabled researchers to manipulate small collections of quantum-mechanical objects with unprecedented accuracy. In semiconductor quantum-dot qubits, this manipulation requires controlling the dot orbital energies, the tunnel couplings, and the electron occupations. These properties all depend on the voltages placed on the metallic electrodes that define the device, the positions of which are fixed once the device is fabricated. While there has been much success with small numbers of dots, as the number of dots grows, it will be increasingly useful to control these systems with as few electrode voltage changes as possible. Here, we introduce a protocol, which we call the "compressed optimization of device architectures" (CODA), in order both to efficiently identify sparse sets of voltage changes that control quantum systems and to introduce a metric that can be used to compare device designs. As an example of the former, we apply this method to simulated devices with up to 100 quantum dots and show that CODA automatically tunes devices more efficiently than other common nonlinear optimizers. To demonstrate the latter, we determine the optimal lateral scale for a triple quantum dot, yielding a simulated device that can be tuned with small voltage changes on a limited number of electrodes.
Research Organization:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1497629
Alternate ID(s):
OSTI ID: 1334235
Report Number(s):
SAND2019--1977J; 672814
Journal Information:
Physical Review Applied, Journal Name: Physical Review Applied Journal Issue: 2 Vol. 11; ISSN 2331-7019; ISSN PRAHB2
Publisher:
American Physical Society (APS)Copyright Statement
Country of Publication:
United States
Language:
English

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Efficiently measuring a quantum device using machine learning journal September 2019

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Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS