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Quantum computers are nearing the thousand qubit mark, with the current focus on scaling to improve computational performance. As quantum processors grow in complexity, new challenges arise such as the management of device variability and the interface with supporting electronics. Spin qubits in silicon quantum dots are poised to address these challenges with their proven control fidelities and potential for compatibility with large-scale integration. Here, we demonstrate the integration of 1024 silicon quantum dots with on-chip digital and analogue electronics, all operating below 1 K. A high-frequency analogue multiplexer provides fast access to all devices with minimal electrical connections, enabling characteristic data across the quantum dot array to be acquired in just 5 minutes. We achieve this by leveraging radio-frequency reflectometry with state-of-the-art signal integrity, reaching a minimum integration time of 160 ps. Key quantum dot parameters are extracted by fast automated machine learning routines to assess quantum dot yield and understand the impact of device design. We find correlations between quantum dot parameters and room temperature transistor behaviour that may be used as a proxy for in-line process monitoring. Our results show how rapid large-scale studies of silicon quantum devices can be performed at lower temperatures and measurement rates orders of magnitude faster than current probing techniques, and form a platform for the future on-chip addressing of large scale qubit arrays., Comment: Main text: 14 pages, 8 figures, 1 table Supplementary: 8 pages, 6 figures

Fast measurements of quantum devices is important in areas such as quantum sensing, quantum computing and nanodevice quality analysis. Here, we develop a superconductor-semiconductor multi-module microwave assembly to demonstrate charge state readout at the state-of-the-art. The assembly consist of a superconducting readout resonator interfaced to a silicon-on-insulator (SOI) chiplet containing quantum dots (QDs) in a high-$\kappa$ nanowire transistor. The superconducting chiplet contains resonant and coupling elements as well as $LC$ filters that, when interfaced with the silicon chip, result in a resonant frequency $f=2.12$ GHz, a loaded quality factor $Q=850$, and a resonator impedance $Z=470$ $\Omega$. Combined with the large gate lever arms of SOI technology, we achieve a minimum integration time for single and double QD transitions of 2.77 ns and 13.5 ns, respectively. We utilize the assembly to measure charge noise over 9 decades of frequency up to 500 kHz and find a 1/$f$ dependence across the whole frequency spectrum as well as a charge noise level of 4 $\mu$eV/$\sqrt{\text{Hz}}$ at 1 Hz. The modular microwave circuitry presented here can be directly utilized in conjunction with other quantum device to improve the readout performance as well as enable large bandwidth noise spectroscopy, all without the complexity of superconductor-semiconductor monolithic fabrication., Comment: Main: 8 pages, 4 figures. Supplementary: 4 pages, 7 figures

Recent progress in the application of color centers to nanoscale spin sensing makes the combined use of noise spectroscopy and scanning probe imaging an attractive route for the characterization of arbitrary material systems. Unfortunately, the traditional approach to characterizing the environmental magnetic field fluctuations from the measured probe signal typically requires the experimenter's input, thus complicating the implementation of automated imaging protocols based on spectrally resolved noise. Here, we probe the response of color centers in diamond in the presence of externally engineered random magnetic signals, and implement a deep neural network to methodically extract information on their associated spectral densities. Building on a long sequence of successive measurements under different types of stimuli, we show that our network manages to efficiently reconstruct the spectral density of the underlying fluctuating magnetic field with good fidelity under a broad set of conditions and with only a minimal measured data set, even in the presence of substantial experimental noise. These proof-of-principle results create opportunities for the application of machine-learning methods to color-center-based nanoscale sensing and imaging.

Understanding the spectrum of noise acting on a qubit can yield valuable information about its environment, and crucially underpins the optimization of dynamical decoupling protocols that can mitigate such noise. However, extracting accurate noise spectra from typical time-dynamics measurements on qubits is intractable using standard methods. Here, we propose to address this challenge using deep learning algorithms, leveraging the remarkable progress made in the field of image recognition, natural language processing, and more recently, structured data. We demonstrate a neural network based methodology that allows for extraction of the noise spectrum associated with any qubit surrounded by an arbitrary bath, with significantly greater accuracy than the current methods of choice. The technique requires only a two-pulse echo decay curve as input data and can further be extended either for constructing customized optimal dynamical decoupling protocols or for obtaining critical qubit attributes such as its proximity to the sample surface. Our results can be applied to a wide range of qubit platforms, and provide a framework for improving qubit performance with applications not only in quantum computing and nanoscale sensing but also in material characterization techniques such as magnetic resonance., Comment: Accepted for publication, 15 pages, 10 figures