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Differentiable models of physical systems provide a powerful platform for gradient-based algorithms, with particular impact on parameter estimation and optimal control. Quantum systems present a particular challenge for such characterisation and control, owing to their inherently stochastic nature and sensitivity to environmental parameters. To address this challenge, we present a versatile differentiable quantum master equation solver, and incorporate this solver into a framework for device characterisation. Our approach utilises gradient-based optimisation and Bayesian inference to provide estimates and uncertainties in quantum device parameters. To showcase our approach, we consider steady state charge transport through electrostatically defined quantum dots. Using simulated data, we demonstrate efficient estimation of parameters for a single quantum dot, and model selection as well as the capability of our solver to compute time evolution for a double quantum dot system. Our differentiable solver stands to widen the impact of physics-aware machine learning algorithms on quantum devices for characterisation and control.

Spanning over two decades, the study of qubits in semiconductors for quantum computing has yielded significant breakthroughs. However, the development of large-scale semiconductor quantum circuits is still limited by challenges in efficiently tuning and operating these circuits. Identifying optimal operating conditions for these qubits is complex, involving the exploration of vast parameter spaces. This presents a real 'needle in the haystack' problem, which, until now, has resisted complete automation due to device variability and fabrication imperfections. In this study, we present the first fully autonomous tuning of a semiconductor qubit, from a grounded device to Rabi oscillations, a clear indication of successful qubit operation. We demonstrate this automation, achieved without human intervention, in a Ge/Si core/shell nanowire device. Our approach integrates deep learning, Bayesian optimization, and computer vision techniques. We expect this automation algorithm to apply to a wide range of semiconductor qubit devices, allowing for statistical studies of qubit quality metrics. As a demonstration of the potential of full automation, we characterise how the Rabi frequency and g-factor depend on barrier gate voltages for one of the qubits found by the algorithm. Twenty years after the initial demonstrations of spin qubit operation, this significant advancement is poised to finally catalyze the operation of large, previously unexplored quantum circuits.

The future development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation and sensing. This poses severe challenges in the efficient control, calibration and validation of quantum states and their dynamics. Although the full simulation of large-scale quantum systems may only be possible on a quantum computer, classical characterization and optimization methods still play an important role. Here, we review different approaches that use classical post-processing techniques, possibly combined with adaptive optimization, to learn quantum systems, their correlation properties, dynamics and interaction with the environment. We discuss theoretical proposals and successful implementations across different multiple-qubit architectures such as spin qubits, trapped ions, photonic and atomic systems, and superconducting circuits. This Review provides a brief background of key concepts recurring across many of these approaches with special emphasis on the Bayesian formalism and neural networks., Comment: Review. 20 pages, 4 figures and 2 boxes (reformatted)

Pauli spin blockade (PSB) can be employed as a great resource for spin qubit initialisation and readout even at elevated temperatures but it can be difficult to identify. We present a machine learning algorithm capable of automatically identifying PSB using charge transport measurements. The scarcity of PSB data is circumvented by training the algorithm with simulated data and by using cross-device validation. We demonstrate our approach on a silicon field-effect transistor device and report an accuracy of 96% on different test devices, giving evidence that the approach is robust to device variability. The approach is expected to be employable across all types of quantum dot devices.

The broad energy response, low electronic read noise, and good energy resolution have made X-ray Charge-Coupled Devices (CCDs) an obvious choice for developing soft X-ray astronomical instruments over the last half century. They also come in large array formats with small pixel sizes which make them a potential candidate for the next generation astronomical X-ray missions. However, the next generation X-ray telescopic experiments propose for significantly larger collecting area compared to the existing observatories in order to explore the low luminosity and high redshift X-ray universe which requires these detectors to have an order of magnitude faster readout. In this context, the Stanford University (SU) in collaboration with the Massachusetts Institute of Technology (MIT) has initiated the development of fast readout electronics for X-ray CCDs. At SU, we have designed and developed a fast and low noise readout module with the goal of achieving a readout speed of 5 Mpixel/s. We successfully ran a prototype CCD matrix of 512 $\times$ 512 pixels at 4 Mpixels/s. In this paper, we describe the details of the readout electronics and report the performance of the detectors at these readout speeds in terms of read noise and energy resolution. In the future, we plan to continue to improve performance of the readout module and eventually converge to a dedicated ASIC based readout system to enable parallel read out of large array multi-node CCD devices., Comment: Submitted in Journal of Astronomical Telescopes, Instruments, and Systems (JATIS)