The spin splitting defines the qubit energy and is one of the fundamental properties of a semiconductor electron in a magnetic field. Here, we measure and separate the isotropic and anisotropic corrections to the bulk g-factor in two GaAs spin qubit devices, finding good agreement with recent theory. This also has implications for GaAs spin qubits, recently enjoying a revival due to mitigation of hyperfine decoherence by active stabilization. Collaboration between Basel (exp.& th.), RIKEN (theory) and UCSB (2DEG).
Camenzind, Svab et al., arXiv:2010.11185 (Oct 21, 2020)
The act of measurement in quantum mechanics is still mysterious and not microscopically understood. In our work, we show that even a weak (non-projective) measurement of a quantum dot system with an adjacent detector can result in a fundamental change of the occupied state, involving also the state’s many-body environment. Collaboration with Oded Zilberberg and Group (ETHZ), Bernd Braunecker (St. Andrews) and Clemens Müller (IBM Zürich).
Ferguson, Camenzind et al., arXiv:2010.04635 (Oct 9, 2020)
Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from actions and rewards similar to the way humans learn from experience. Here, we use this approach for fully automatic identification of double dot bias triangles in a surface gated GaAs device. Oxford - Basel Collaboration lead by Natalia Ares (Oxford).
Nguyen et al., arXiv:2009.14825 (Sep 30, 2020)
We demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement, scores it using a variational auto-encoder, and optimizes gate voltage via the score in real-time in an unsupervised fashion. New Journal of Physics 22, 095003 (Sept 22, 2020)