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