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RAP opportunity at National Institute of Standards and Technology     NIST

Autonomous tuning for high-fidelity operations of silicon spin qubits


Information Technology Laboratory, Applied and Computational Mathematics Division

opportunity location
50.77.11.C0797 Gaithersburg, MD

NIST only participates in the February and August reviews.


name email phone
Justyna P Zwolak 301.975.0527


Qubits controlled through a combination of gate voltages and magnetic fields require continuous calibration to compensate for drift and changes in the physical environment [1]. If not detected and corrected for, the noise and drift can directly affect not only the initial calibration of qubits, but also the coherence times and, ultimately, fidelities of the quantum control and readout operations. This problem becomes even more pressing for simultaneous multi-qubit operations. The goal of this project is to develop software tools for the automated tuning of high-fidelity readout and gates in silicon spin qubits. These efforts are necessary to improve the scalability of the silicon spin qubit platform [2].

Initial efforts in autonomous tuning will focus on optimizing readout systems, shifting to the gate and mid-circuit measurements. In particular, the initial phase of the project will focus on developing robust automated tuning protocols to optimize spin readout and control of silicon spin qubits. A key component at this stage will be the development of quantum characterization protocols for spin qubits in the presence of time-correlated noise, as well as reliable tools for simulating the quantum dynamics of spin-qubit arrays. In the later stages of the effort, we investigate the correlations between the hardware noise contribution to the overall fidelity and gate performance to inform the control circuit design and the signal-chain engineering. We will focus on an in-depth analysis of the correlations between the design of the charge circuit and the resulting level of noise and charge sensing bandwidth. We will begin by exploring variations to the parameters defining components currently used in circuits [3], such as amplifiers, resistors, and attenuators, accounting for the noise characteristics of each component as well as for the intrinsic noise and, eventually, the noise dependence on time. We will also collaborate with experimental groups to implement our software tools on real silicon spin-qubit devices and explore possible advances with specialized hardware. 

[1] S. G. Philips, M. T. Madzik, S. V. Amitonov, S. L. de Snoo, M. Russ, N. Kalhor, C. Volk, W. I. Lawrie, D. Brousse, L. Tryputen, and B. P. Wuetz, Universal control of a six-qubit quantum processor in silicon. Nature 609, 919–924 (2022).

[2] J. Zwolak and J. Taylor. Colloquium: Advances in automation of quantum dot devices control. Rev. Mod. Phys. 95, 011006 (2023).

[3] A. R. Mills, C. R. Guinn, M. M. Feldman, A. J. Sigillito, M. J. Gullans, M. Rakher, J. Kerckhoff, A. C. Jackson, and J. R. Petta, High-fidelity state preparation, quantum control, and readout of an isotopically enriched silicon spin qubit. Phys. Rev. Applied 18, 064028 (2022).

key words
machine learning; reinforcement learning; spin readout; optimal control; circuit design


Citizenship:  Open to U.S. citizens
Level:  Open to Postdoctoral applicants


Base Stipend Travel Allotment Supplementation
$82,764.00 $3,000.00
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