Understanding and control of the spin relaxation time T1 is among the key challenges for spin-based qubits. A larger T1 is generally favored, setting the fundamental upper limit to the qubit coherence and spin readout fidelity. In GaAs quantum dots at low temperatures and high in-plane magnetic fields B, the spin relaxation relies on phonon emission and spin–orbit coupling. The characteristic dependence T1 ∝ B−5 and pronounced B-field anisotropy were already confirmed experimentally. However, it has also been predicted 15 years ago that at low enough fields, the spin–orbit interaction is replaced by the coupling to the nuclear spins, where the relaxation becomes isotropic, and the scaling changes to T1 ∝ B−3. Here, we establish these predictions experimentally, by measuring T1 over an unprecedented range of magnetic fields—made possible by lower temperature—and report a maximum T1 = 57 ± 15 s at the lowest fields, setting a record electron spin lifetime in a nanostructure.

Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies.

This work reports a study of the nonlinear Hall effect (HE) in the semimetallic ferromagnet EuB(6). A distinct switch in its Hall resistivity slope is observed in the paramagnetic phase, which occurs at a single critical magnetization over a wide temperature range. The observation is interpreted as the point of percolation for entities of a more conducting and magnetically ordered phase in a less ordered background. With an increasing applied magnetic field, the conducting regions either increase in number or expand beyond the percolation limit, hence increasing the global conductivity and effective carrier density. An empirical two-component model provides excellent scaling and a quantitative fit to the HE data and may be applicable to other correlated electron systems.

Scalable quantum technologies will present challenges for characterizing and tuning quantum devices. This is a time-consuming activity, and as the size of quantum systems increases, this task will become intractable without the aid of automation. We present measurements on a quantum dot device performed by a machine learning algorithm. The algorithm selects the most informative measurements to perform next using information theory and a probabilistic deep-generative model, the latter capable of generating multiple full-resolution reconstructions from scattered partial measurements. We demonstrate, for two different measurement configurations, that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times. Our contribution goes beyond the use of machine learning for data search and analysis, and instead presents the use of algorithms to automate measurement. This work lays the foundation for automated control of large quantum circuits. † Both authors contributed equally and are displayed in alphabetical order.

We show that in-plane-magnetic-field assisted spectroscopy allows extraction of the in-plane orientation and full 3D shape of the quantum mechanical orbitals of a single electron GaAs lateral quantum dot with sub-nm precision. The method is based on measuring orbital energies in a magnetic field with various strengths and orientations in the plane of the 2D electron gas. As a result, we deduce the microscopic quantum dot confinement potential landscape, and quantify the degree by which it differs from a harmonic oscillator potential. The spectroscopy is used to validate shape manipulation with gate voltages, agreeing with expectations from the gate layout. Our measurements demonstrate a versatile tool for quantum dots with one dominant axis of strong confinement.