Papers
Topics
Authors
Recent
Search
2000 character limit reached

QDsim: A user-friendly toolbox for simulating large-scale quantum dot devices

Published 3 Apr 2024 in cond-mat.mes-hall and quant-ph | (2404.02712v2)

Abstract: We introduce QDsim, a python package tailored for the rapid generation of charge stability diagrams in large-scale quantum dot devices, extending beyond traditional double or triple dots. QDsim is founded on the constant interaction model from which we rephrase the task of finding the lowest energy charge configuration as a convex optimization problem. Therefore, we can leverage the existing package CVXPY, in combination with an appropriate powerful solver, for the convex optimization which streamlines the creation of stability diagrams and polytopes. Through multiple examples, we demonstrate how QDsim enables the generation of large-scale dataset that can serve a basis for the training of machine-learning models for automated tuning algorithms. While the package currently does not support quantum effects beyond the constant interaction model, QDsim is a tool that directly addresses the critical need for cost-effective and expeditious data acquisition for better tuning algorithms in order to accelerate the development of semiconductor quantum devices.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (10)
  1. D. Loss and D. P. DiVincenzo, Quantum computation with quantum dots, Physical Review A 57, 120 (1998).
  2. S. Yang, X. Wang, and S. D. Sarma, Generic hubbard model description of semiconductor quantum-dot spin qubits, Physical Review B 83, 161301 (2011).
  3. T. Ihn, Semiconductor Nanostructures: Quantum states and electronic transport (OUP Oxford, 2009).
  4. J. C. Maxwell, A treatise on electricity and magnetism, Vol. 1 (Oxford: Clarendon Press, 1873).
  5. G. H. Golub and C. F. Van Loan, Matrix computations (JHU press, 2013).
  6. M. Gil’, On invertibility and positive invertibility of matrices, Linear Algebra and its Applications 327, 95 (2001).
  7. S. P. Boyd and L. Vandenberghe, Convex optimization (Cambridge University Press, Cambridge, UK ; New York, 2004).
  8. Ray Project Contributors, Ray: a unified framework for scaling ai and python applications, https://github.com/ray-project/ray (2023), version 2.7.1.
  9. J. Waldmann, pyplnoise: Arbitrarily long streams of power law noise using NumPy and SciPy, https://github.com/janwaldmann/pyplnoise/ (2022), version 1.4.
  10. QuTech-Delft Contributors, Qtt, https://github.com/QuTech-Delft/qtt/ (2023).

Summary

No one has generated a summary of this paper yet.

Paper to Video (Beta)

No one has generated a video about this paper yet.

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Open Problems

We found no open problems mentioned in this paper.

Continue Learning

We haven't generated follow-up questions for this paper yet.

Collections

Sign up for free to add this paper to one or more collections.

Tweets

Sign up for free to view the 3 tweets with 61 likes about this paper.