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Gate-defined quantum dots are a promising candidate system for realizing scalable, coupled qubit systems and serving as a fundamental building block for quantum computers. However, present-day quantum dot devices suffer from imperfections that must be accounted for, which hinders the characterization, tuning, and operation process. Moreover, with an increasing number of quantum dot qubits, the relevant parameter space grows sufficiently to make heuristic control infeasible. Thus, it is imperative that reliable and scalable autonomous tuning approaches are developed. This meeting report outlines current challenges in automating quantum dot device tuning and operation with a particular focus on datasets, benchmarking, and standardization. We also present insights and ideas put forward by the quantum dot community on how to overcome them. We aim to provide guidance and inspiration to researchers invested in automation efforts., Comment: A meeting report from a workshop held at the National Institute of Standards and Technology, Gaithersburg, MD

Qubits encoded in a decoherence-free subsystem and realized in exchange-coupled silicon quantum dots are promising candidates for fault-tolerant quantum computing. Benefits of this approach include excellent coherence, low control crosstalk, and configurable insensitivity to certain error sources. Key difficulties are that encoded entangling gates require a large number of control pulses and high-yielding quantum dot arrays. Here we show a device made using the single-layer etch-defined gate electrode architecture that achieves both the required functional yield needed for full control and the coherence necessary for thousands of calibrated exchange pulses to be applied. We measure an average two-qubit Clifford fidelity of $97.1 \pm 0.2\%$ with randomized benchmarking. We also use interleaved randomized benchmarking to demonstrate the controlled-NOT gate with $96.3 \pm 0.7\%$ fidelity, SWAP with $99.3 \pm 0.5\%$ fidelity, and a specialized entangling gate that limits spreading of leakage with $93.8 \pm 0.7\%$ fidelity.

We demonstrate rapid, high-fidelity state preparation and measurement in exchange-only Si/SiGe triple-quantum-dot qubits. Fast measurement integration ($980$ ns) and initialization ($\approx 300$ ns) operations are performed with all-electrical, baseband control. We emphasize a leakage-sensitive joint initialization and measurement metric, developed in the context of exchange-only qubits but applicable more broadly, and report an infidelity of $2.5\pm0.5\times 10^{-3}$. This result is enabled by a high-valley-splitting heterostructure, initialization at the 2-to-3 electron charge boundary, and careful assessment and mitigation of $T_1$ during spin-to-charge conversion. The ultimate fidelity is limited by a number of comparably-important factors, and we identify clear paths towards further improved fidelity and speed. Along with an observed single-qubit randomized benchmarking error rate of $1.7\times 10^{-3}$, this work demonstrates initialization, control, and measurement of Si/SiGe triple-dot qubits at fidelities and durations which are promising for scalable quantum information processing.

An increasing number of machine learning tasks deal with learning representations from set-structured data. Solutions to these problems involve the composition of permutation-equivariant modules (e.g., self-attention, or individual processing via feed-forward neural networks) and permutation-invariant modules (e.g., global average pooling, or pooling by multi-head attention). In this paper, we propose a geometrically-interpretable framework for learning representations from set-structured data, which is rooted in the optimal mass transportation problem. In particular, we treat elements of a set as samples from a probability measure and propose an exact Euclidean embedding for Generalized Sliced Wasserstein (GSW) distances to learn from set-structured data effectively. We evaluate our proposed framework on multiple supervised and unsupervised set learning tasks and demonstrate its superiority over state-of-the-art set representation learning approaches.

Through simultaneous but unequal electromechanical amplification and cooling processes, we create a method for nearly noiseless pulsed measurement of mechanical motion. We use transient electromechanical amplification (TEA) to monitor a single motional quadrature with a total added noise $-8.5\pm2.0$ dB relative to the zero-point motion of the oscillator, or equivalently the quantum limit for simultaneous measurement of both mechanical quadratures. We demonstrate that TEA can be used to resolve fine structure in the phase-space of a mechanical oscillator by tomographically reconstructing the density matrix of a squeezed state of motion. Without any inference or subtraction of noise, we directly observe a squeezed variance $2.8\pm 0.3$ dB below the oscillator's zero-point motion., Comment: 6 page main text, 13 page supplement, accepted version

We demonstrate a fully cryogenic microwave feedback network composed of modular superconducting devices connected by transmission lines and designed to control a mechanical oscillator coupled to one of the devices. The network features an electromechanical device and a tunable controller that coherently receives, processes and feeds back continuous microwave signals that modify the dynamics and readout of the mechanical state. While previous electromechanical systems represent some compromise between efficient control and efficient readout of the mechanical state, as set by the electromagnetic decay rate, the tunable controller produces a closed-loop network that can be dynamically and continuously tuned between both extremes much faster than the mechanical response time. We demonstrate that the microwave decay rate may be modulated by at least a factor of 10 at a rate greater than $10^4$ times the mechanical response rate. The system is easy to build and suggests that some useful functions may arise most naturally at the network-level of modular, quantum electromagnetic devices., Comment: 11 pages, 6 figures, final published version