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Dynamic quantum dots are known to generate very accurate currents and can be used as a source of single electron wavepackets for use in quantum metrology, sensing or information processing. To realise their full technological potential, it is desirable to maximise the frequency of operation to increase the current. However, it has been observed that the mechanism of electron transfer across the quantum dot consistently breaks down for GHz frequencies, and this remains unexplained. Here, we present a novel analysis technique, combined with detailed modelling, to present a mechanism of high frequency breakdown in quantum dots as a rapidly-imparted momentum impulse. Such understanding aids future design and operation protocols, allowing their use in high frequency and real time quantum measurements and technologies., Comment: 8 pages, 3 figures

Leveraging the advanced manufacturing capabilities of the semiconductor industry promises to help scale up silicon-based quantum processors by increasing yield, uniformity and integration. Recent studies of quantum dots fabricated on 300 mm wafer metal-oxide-semiconductor (MOS) processes have shown control and readout of individual spin qubits, yet quantum processors require two-qubit interactions to operate. Here, we use a 300 mm wafer MOS process customized for spin qubits and demonstrate coherent control of two electron spins using the spin-spin exchange interaction, forming the basis of an entangling gate such as $\sqrt{\text{SWAP}}$. We observe gate dephasing times of up to $T_2^{*}\approx500$ ns and a gate quality factor of 10. We further extend the coherence by up to an order of magnitude using an echo sequence. For readout, we introduce a dispersive readout technique, the radiofrequency electron cascade, that amplifies the signal while retaining the spin-projective nature of dispersive measurements. Our results demonstrate an industrial grade platform for two-qubit operations, alongside integration with dispersive sensing techniques.

The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a beginner but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an Active Learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL based on Gaussian Process (GP) and Random Forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e. ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials.