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Computational design of protein-binding proteins is a fundamental capability with broad utility in biomedical research and biotechnology. Recent methods have made strides against some target proteins, but on-demand creation of high-affinity binders without multiple rounds of experimental testing remains an unsolved challenge. This technical report introduces AlphaProteo, a family of machine learning models for protein design, and details its performance on the de novo binder design problem. With AlphaProteo, we achieve 3- to 300-fold better binding affinities and higher experimental success rates than the best existing methods on seven target proteins. Our results suggest that AlphaProteo can generate binders "ready-to-use" for many research applications using only one round of medium-throughput screening and no further optimization., Comment: 45 pages, 17 figures

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

We report relaxation oscillations in a one-dimensional array of Josephson junctions. The oscillations are circuit-dual to those ordinarily observed in single junctions. The dual circuit quantitatively accounts for temporal dynamics of the array, including the dependence on biasing conditions. Injection locking the oscillations results in well-developed current plateaux. A thermal model explains the relaxation step of the oscillations., Comment: 7 pages, 8 figures

Electrostatically tunable Josephson field-effect transistors (JoFETs) are one of the most desired building blocks of quantum electronics. JoFET applications range from parametric amplifiers and superconducting qubits to a variety of integrated superconducting circuits. Here, we report on graphene JoFET devices fabricated with wafer-scale complementary metal-oxide-semiconductor (CMOS) compatible processing based on wet transfer of chemical vapour deposited graphene, atomic-layer-deposited Al$_{2}$O$_{3}$ gate oxide, and evaporated superconducting Ti/Al source, drain, and gate contacts. By optimizing the contact resistance down to $\sim$ 170 $\Omega \mu m$, we observe proximity-induced superconductivity in the JoFET channels with different gate lengths of 150 - 350 nm. The Josephson junction devices show reproducible critical current $I_{\text{C}}$ tunablity with the local top gate. Our JoFETs are in short diffusive limit with the $I_{\text{C}}$ reaching up to $\sim\,$3 $\mu A$ for a 50 $\mu m$ channel width. Overall, our demonstration of CMOS-compatible 2D-material-based JoFET fabrication process is an important step toward graphene-based integrated quantum circuits., Comment: revised manuscript; 30 pages, 4 figures, 1 table, 12 supplementary figures

We establish that the asymptotic mean action and the asymptotic linking number of irrational pseudo-rotations remain well-defined everywhere and constant for every $C^{1}$ irrational pseudo-rotation that behaves as a rotation on the boundary. As a consequence, we demonstrate that the isotopy of irrational pseudo-rotations with a positive rotation number is a right-handed isotopy in the sense of Ghys., Comment: 20 pages, comments welcome

Quantum error-correction is a prerequisite for reliable quantum computation. Towards this goal, we present a recurrent, transformer-based neural network which learns to decode the surface code, the leading quantum error-correction code. Our decoder outperforms state-of-the-art algorithmic decoders on real-world data from Google's Sycamore quantum processor for distance 3 and 5 surface codes. On distances up to 11, the decoder maintains its advantage on simulated data with realistic noise including cross-talk, leakage, and analog readout signals, and sustains its accuracy far beyond the 25 cycles it was trained on. Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers.

Micro-vascular flow in the myocardium is of significant importance clinically but remains poorly understood. Up to 25% of patients with symptoms of coronary heart diseases have no obstructive coronary arteries and have suspected microvascular diseases. However, such microvasculature is difficult to image in vivo with existing modalities due to the lack of resolution and sensitivity. Here, we demonstrate the feasibility of transthoracic super-resolution ultrasound localisation microscopy (SRUS/ULM) of myocardial microvasculature and hemodynamics in a large animal model and in patients, using a cardiac phased array probe with a customised data acquisition and processing pipeline. A multi-level motion correction strategy was proposed. A tracking framework incorporating multiple features and automatic parameter initialisations was developed to reconstruct microcirculation. In two patients with impaired myocardial function, we have generated SRUS images of myocardial vascular structure and flow with a resolution that is beyond the wave-diffraction limit (half a wavelength), using data acquired within a breath hold. Myocardial SRUS/ULM has potential to improve the understanding of myocardial microcirculation and the management of patients with cardiac microvascular diseases., Comment: 22 pages, 10 figures

We demonstrate ion irradiation by argon or gallium as a wafer-scale post-processing method to increase disorder in superconducting thin films. We study several widely used superconductors, both single-elements and compounds. We show that ion irradiation increases normal-state resistivity in all our films, which is expected to enable tuning their superconducting properties, for example, toward higher kinetic inductance. We observe an increase of superconducting transition temperature for Al and MoSi, and a decrease for Nb, NbN, and TiN. In MoSi, ion irradiation also improves the mixing of the two materials. We demonstrate fabrication of an amorphous and homogeneous film of MoSi with uniform thickness, which is promising, e.g., for superconducting nanowire single-photon detectors.

2D image understanding is a complex problem within computer vision, but it holds the key to providing human-level scene comprehension. It goes further than identifying the objects in an image, and instead, it attempts to understand the scene. Solutions to this problem form the underpinning of a range of tasks, including image captioning, visual question answering (VQA), and image retrieval. Graphs provide a natural way to represent the relational arrangement between objects in an image, and thus, in recent years graph neural networks (GNNs) have become a standard component of many 2D image understanding pipelines, becoming a core architectural component, especially in the VQA group of tasks. In this survey, we review this rapidly evolving field and we provide a taxonomy of graph types used in 2D image understanding approaches, a comprehensive list of the GNN models used in this domain, and a roadmap of future potential developments. To the best of our knowledge, this is the first comprehensive survey that covers image captioning, visual question answering, and image retrieval techniques that focus on using GNNs as the main part of their architecture., Comment: 20 pages, 5 figures, 5 tables