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The spatial distribution of spurious dots in SiMOS single-electron transistors (SETs), fabricated on an industrial 300 mm process line, has been statistically analyzed. To have a deeper understanding of the origin of these spurious dots, we analyzed SETs with three different oxide thicknesses: 8 nm, 12 nm and 20 nm. By combining spurious dot triangulation cryo-measurement with simulations of strain, gate bias, and location of the electron wave function, we demonstrate that most spurious dots are formed through the combined effects of strain and gate bias, leading to variations in the conduction band energy. Despite the similar thermal expansion coefficients of polycrystalline silicon gates and single-crystalline silicon substrates, strain remains a crucial factor in spurious dots formation. This learning can be use to optimize the device design and the oxide thickness, to reduce the density of spurious dot while keeping quantum dot tunability., Comment: 10 pages, 9 figures

Fabrication of quantum processors in advanced 300 mm wafer-scale complementary metal-oxide-semiconductor (CMOS) foundries provides a unique scaling pathway towards commercially viable quantum computing with potentially millions of qubits on a single chip. Here, we show precise qubit operation of a silicon two-qubit device made in a 300 mm semiconductor processing line. The key metrics including single- and two-qubit control fidelities exceed 99% and state preparation and measurement fidelity exceeds 99.9%, as evidenced by gate set tomography (GST). We report coherence and lifetimes up to $T_\mathrm{2}^{\mathrm{*}} = 30.4$ $\mu$s, $T_\mathrm{2}^{\mathrm{Hahn}} = 803$ $\mu$s, and $T_1 = 6.3$ s. Crucially, the dominant operational errors originate from residual nuclear spin carrying isotopes, solvable with further isotopic purification, rather than charge noise arising from the dielectric environment. Our results answer the longstanding question whether the favourable properties including high-fidelity operation and long coherence times can be preserved when transitioning from a tailored academic to an industrial semiconductor fabrication technology., Comment: 10 pages, 4 figures, 4 extended data figures

Spin-based semiconductor qubits hold promise for scalable quantum computing, yet they require reliable autonomous calibration procedures. This study presents an experimental demonstration of online single-dot charge autotuning using a convolutional neural network integrated into a closed-loop calibration system. The autotuning algorithm explores the gates' voltage space to localize charge transition lines, thereby isolating the one-electron regime without human intervention. In 20 experimental runs on a device cooled to 25mK, the method achieved a success rate of 95% in locating the target electron regime, highlighting the robustness of this method against noise and distribution shifts from the offline training set. Each tuning run lasted an average of 2 hours and 9 minutes, primarily due to the limited speed of the current measurement. This work validates the feasibility of machine learning-driven real-time charge autotuning for quantum dot devices, advancing the development toward the control of large qubit arrays., Comment: 6 pages (main) + 5 pages (supplementary)

The realisation of an universal quantum computer will require the operation of thousands to millions of qubits. The possibility of using existing industrial semiconductor fabrication techniques and infrastructure for up-scaling and reproducibility makes silicon based spin qubits one of the most promising platforms to achieve this goal. The implementation of the up to now largest semiconductor based quantum processor was realized in a silicon/silicon-germanium heterostructure known for its low charge noise, long qubit coherence times and fast driving speeds, but the high structural complexity creates challenges for industrial implementations. Here we demonstrate quantum dots hosted in a natural Si/SiGe heterostructure fully fabricated by an industrial 300$\,$mm semiconductor wafer process line from heterostructure growth to Co micromagnet monolithic integration. We report charge noise values below 2$\,\mathrm{\mu eV/\sqrt{Hz}}$, spin relaxation times of over 1$\,$s and coherence times $T_2^*$ and $T_2^H$ of 1$\,\mathrm{\mu s}$ and 50$\,\mathrm{\mu s}$ respectively, for quantum wells grown using natural silicon. Further, we achieve Rabi frequencies up to 5$\,$MHz and single qubit gate fidelities above 99$\,\%$. In addition to scalability, the high reproducibility of the 300$\,$mm processes enables the deterministic study of qubit metric dependencies on process parameters, which is essential for optimising qubit quality.

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.

We establish equivalence between the boundedness of specific supremum operators and the optimality of function spaces in Sobolev embeddings acting on domains in ambient Euclidean space with a prescribed isoperimetric behavior. Our approach is based on exploiting known relations between higher-order Sobolev embeddings and isoperimetric inequalities. We provide an explicit way to compute both the optimal domain norm and the optimal target norm in a Sobolev embedding. Finally, we apply our results to higher-order Sobolev embeddings on John domains and on domains from the Maz'ya classes. Furthermore, our results are partially applicable to embeddings involving product probability spaces.

This study presents a machine-learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This method exploits artificial neural networks to identify noisy transition lines in stability diagrams, guiding a robust exploration strategy leveraging neural networks' uncertainty estimations. Tested across three distinct offline experimental datasets representing different single quantum dot technologies, the approach achieves over 99% tuning success rate in optimal cases, where more than 10% of the success is directly attributable to uncertainty exploitation. The challenging constraints of small training sets containing high diagram-to-diagram variability allowed us to evaluate the capabilities and limits of the proposed procedure., Comment: 12 pages (main) + 14 pages (supplementary)

Retrieval Augmented Generation (RAG) enhances the abilities of Large Language Models (LLMs) by enabling the retrieval of documents into the LLM context to provide more accurate and relevant responses. Existing RAG solutions do not focus on queries that may require fetching multiple documents with substantially different contents. Such queries occur frequently, but are challenging because the embeddings of these documents may be distant in the embedding space, making it hard to retrieve them all. This paper introduces Multi-Head RAG (MRAG), a novel scheme designed to address this gap with a simple yet powerful idea: leveraging activations of Transformer's multi-head attention layer, instead of the decoder layer, as keys for fetching multi-aspect documents. The driving motivation is that different attention heads can learn to capture different data aspects. Harnessing the corresponding activations results in embeddings that represent various facets of data items and queries, improving the retrieval accuracy for complex queries. We provide an evaluation methodology and metrics, synthetic datasets, and real-world use cases to demonstrate MRAG's effectiveness, showing improvements of up to 20% in relevance over standard RAG baselines. MRAG can be seamlessly integrated with existing RAG frameworks and benchmarking tools like RAGAS as well as different classes of data stores.

Large Language Models (LLMs) are revolutionizing various domains, yet verifying their answers remains a significant challenge, especially for intricate open-ended tasks such as consolidation, summarization, and extraction of knowledge. In this work, we propose CheckEmbed: an accurate, scalable, and simple LLM verification approach. CheckEmbed is driven by a straightforward yet powerful idea: in order to compare LLM solutions to one another or to the ground-truth, compare their corresponding answer-level embeddings obtained with a model such as GPT Text Embedding Large. This reduces a complex textual answer to a single embedding, facilitating straightforward, fast, and meaningful verification. We develop a comprehensive verification pipeline implementing the CheckEmbed methodology. The CheckEmbed pipeline also comes with metrics for assessing the truthfulness of the LLM answers, such as embedding heatmaps and their summaries. We show how to use these metrics for deploying practical engines that decide whether an LLM answer is satisfactory or not. We apply the pipeline to real-world document analysis tasks, including term extraction and document summarization, showcasing significant improvements in accuracy, cost-effectiveness, and runtime performance compared to existing token-, sentence-, and fact-level schemes such as BERTScore or SelfCheckGPT.

Star-product graphs are a natural extension of the Cartesian product, but have not been well-studied. We show that many important established and emerging network topologies, including HyperX, SlimFly, BundleFly, PolarStar, mesh, and torus, are in fact star-product graphs. While this connection was known for BundleFly and PolarStar, it was not for the others listed. We extend a method of constructing maximal and near-maximal sets of edge-disjoint spanning trees on Cartesian products to the star product, thus obtain maximal or near-maximal sets of edge-disjoint spanning trees on new networks of importance, where such sets can improve bandwidth of collective operations and therefore accelerate many important workloads in high-performance computing.

We very much welcome the EDPB's Guidelines. Please find hereunder our feedback to the Guidelines 2/2023 on Technical Scope of Art. 5(3) of ePrivacy Directive. Our comments are presented after a quotation from the proposed text by the EDPB in a box.

The field of natural language processing (NLP) has witnessed significant progress in recent years, with a notable focus on improving large language models' (LLM) performance through innovative prompting techniques. Among these, prompt engineering coupled with structures has emerged as a promising paradigm, with designs such as Chain-of-Thought, Tree of Thoughts, or Graph of Thoughts, in which the overall LLM reasoning is guided by a structure such as a graph. As illustrated with numerous examples, this paradigm significantly enhances the LLM's capability to solve numerous tasks, ranging from logical or mathematical reasoning to planning or creative writing. To facilitate the understanding of this growing field and pave the way for future developments, we devise a general blueprint for effective and efficient LLM reasoning schemes. For this, we conduct an in-depth analysis of the prompt execution pipeline, clarifying and clearly defining different concepts. We then build the first taxonomy of structure-enhanced LLM reasoning schemes. We focus on identifying fundamental classes of harnessed structures, and we analyze the representations of these structures, algorithms executed with these structures, and many others. We refer to these structures as reasoning topologies, because their representation becomes to a degree spatial, as they are contained within the LLM context. Our study compares existing prompting schemes using the proposed taxonomy, discussing how certain design choices lead to different patterns in performance and cost. We also outline theoretical underpinnings, relationships between prompting and other parts of the LLM ecosystem such as knowledge bases, and the associated research challenges. Our work will help to advance future prompt engineering techniques.

Search in test time is often used to improve the performance of reinforcement learning algorithms. Performing theoretically sound search in fully adversarial two-player games with imperfect information is notoriously difficult and requires a complicated training process. We present a method for adding test-time search to an arbitrary policy-gradient algorithm that learns from sampled trajectories. Besides the policy network, the algorithm trains an additional critic network, which estimates the expected values of players following various transformations of the policies given by the policy network. These values are then used for depth-limited search. We show how the values from this critic can create a value function for imperfect information games. Moreover, they can be used to compute the summary statistics necessary to start the search from an arbitrary decision point in the game. The presented algorithm is scalable to very large games since it does not require any search during train time. We evaluate the algorithm's performance when trained along Regularized Nash Dynamics, and we evaluate the benefit of using the search in the standard benchmark game of Leduc hold'em, multiple variants of imperfect information Goofspiel, and Battleships.

Novel low-diameter network topologies such as Slim Fly (SF) offer significant cost and power advantages over the established Fat Tree, Clos, or Dragonfly. To spearhead the adoption of low-diameter networks, we design, implement, deploy, and evaluate the first real-world SF installation. We focus on deployment, management, and operational aspects of our test cluster with 200 servers and carefully analyze performance. We demonstrate techniques for simple cabling and cabling validation as well as a novel high-performance routing architecture for InfiniBand-based low-diameter topologies. Our real-world benchmarks show SF's strong performance for many modern workloads such as deep neural network training, graph analytics, or linear algebra kernels. SF outperforms non-blocking Fat Trees in scalability while offering comparable or better performance and lower cost for large network sizes. Our work can facilitate deploying SF while the associated (open-source) routing architecture is fully portable and applicable to accelerate any low-diameter interconnect.

Due to its extremely short timescale, the non-equilibrium melting of metals is exceptionally difficult to probe experimentally. The knowledge of melting mechanisms is thus based mainly on the results of theoretical predictions. This work reports on the investigation of ultrafast melting of thin polycrystalline Pd films studied by optical laser pump - X-ray free-electron laser probe experiments and molecular-dynamics simulations. By acquiring X-ray diffraction snapshots with sub-picosecond resolution, we capture the sample's atomic structure during its transition from the crystalline to the liquid state. Bridging the timescales of experiments and simulations allows us to formulate a realistic microscopic picture of melting. We demonstrate that the existing models of strongly non-equilibrium melting, developed for systems with relatively weak electron-phonon coupling, remain valid even for ultrafast heating rates achieved in femtosecond laser-excited Pd. Furthermore, we highlight the role of pre-existing and transiently generated crystal defects in the transition to the liquid state., Comment: main manuscript 33 pages, 9 figures; supplemental material 19 pages, 13 figures - all in one file

We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea and primary advantage of GoT is the ability to model the information generated by an LLM as an arbitrary graph, where units of information ("LLM thoughts") are vertices, and edges correspond to dependencies between these vertices. This approach enables combining arbitrary LLM thoughts into synergistic outcomes, distilling the essence of whole networks of thoughts, or enhancing thoughts using feedback loops. We illustrate that GoT offers advantages over state of the art on different tasks, for example increasing the quality of sorting by 62% over ToT, while simultaneously reducing costs by >31%. We ensure that GoT is extensible with new thought transformations and thus can be used to spearhead new prompting schemes. This work brings the LLM reasoning closer to human thinking or brain mechanisms such as recurrence, both of which form complex networks.

In this article, we present TornadoQSim, an open-source quantum circuit simulation framework implemented in Java. The proposed framework has been designed to be modular and easily expandable for accommodating different user-defined simulation backends, such as the unitary matrix simulation technique. Furthermore, TornadoQSim features the ability to interchange simulation backends that can simulate arbitrary quantum circuits. Another novel aspect of TornadoQSim over other quantum simulators is the transparent hardware acceleration of the simulation backends on heterogeneous devices. TornadoQSim employs TornadoVM to automatically compile parts of the simulation backends onto heterogeneous hardware, thereby addressing the fragmentation in development due to the low-level heterogeneous programming models. The evaluation of TornadoQSim has shown that the transparent utilization of GPU hardware can result in up to 506.5$x$ performance speedup when compared to the vanilla Java code for a fully entangled quantum circuit of 11 qubits. Other evaluated quantum algorithms have been the Deutsch-Jozsa algorithm (493.10$x$ speedup for a 11-qubit circuit) and the quantum Fourier transform algorithm (518.12$x$ speedup for a 11-qubit circuit). Finally, the best TornadoQSim implementation of unitary matrix has been evaluated against a semantically equivalent simulation via Qiskit. The comparative evaluation has shown that the simulation with TornadoQSim is faster for small circuits, while for large circuits Qiskit outperforms TornadoQSim by an order of magnitude., Comment: 29 pages

Recently proposed systems aim at achieving privacy using locality-sensitive hashing. We show how these approaches fail by presenting attacks against two such systems: Google's FLoC proposal for privacy-preserving targeted advertising and the MinHash Hierarchy, a system for processing mobile users' traffic behavior in a privacy-preserving way. Our attacks refute the pre-image resistance, anonymity, and privacy guarantees claimed for these systems. In the case of FLoC, we show how to deanonymize users using Sybil attacks and to reconstruct 10% or more of the browsing history for 30% of its users using Generative Adversarial Networks. We achieve this only analyzing the hashes used by FLoC. For MinHash, we precisely identify the movement of a subset of individuals and, on average, we can limit users' movement to just 10% of the possible geographic area, again using just the hashes. In addition, we refute their differential privacy claims., Comment: 14 pages, 9 figures submitted to PETS 2023

Effective photoinduced charge transfer makes molecular bimetallic assemblies attractive for applications as active light induced proton reduction systems. For a more sustainable future, development of competitive base metal dyads is mandatory. However, the electron transfer mechanisms from the photosensitizer to the proton reduction catalyst in base metal dyads remain so far unexplored. We study a Fe-Co dyad that exhibits photocatalytic H2 production activity using femtosecond X-ray emission spectroscopy, complemented by ultrafast optical spectroscopy and theoretical time-dependent DFT calculations, to understand the electronic and structural dynamics after photoexcitation and during the subsequent charge transfer process from the FeII photosensitizer to the cobaloxime catalyst. Using this novel approach, the simultaneous measurement of the transient Kalpha X-ray emission at the iron and cobalt K-edges in a two-colour experiment is enabled making it possible to correlate the excited state dynamics to the electron transfer processes. The methodology, therefore, provides a clear and direct spectroscopic evidence of the Fe->Co electron transfer responsible for the proton reduction activity.

Increasing the separation between semiconductor quantum dots offers scaling advantages by fa- cilitating gate routing and the integration of sensors and charge reservoirs. Elongated quantum dots have been utilized for this purpose in GaAs heterostructures to extend the range of spin-spin interactions. Here, we study a metal-oxide-semiconductor (MOS) device where two quantum dot arrays are separated by an elongated quantum dot (340 nm long, 50 nm wide). We monitor charge transitions of the elongated quantum dot by measuring radiofrequency single-electron currents to a reservoir to which we connect a lumped-element resonator. We operate the dot as a single electron box to achieve charge sensing of remote quantum dots in each array, separated by a distance of 510 nm. Simultaneous charge detection on both ends of the elongated dot demonstrates that the charge is well distributed across its nominal length, supported by the simulated quantum-mechanical electron density. Our results illustrate how single-electron boxes can be realised with versatile foot- prints that may enable novel and compact quantum processor layouts, offering distributed charge sensing in addition to the possibility of mediated coupling., Comment: 14 pages, 9 figures

Silicon spin qubits are among the most promising candidates for large scale quantum computers, due to their excellent coherence and compatibility with CMOS technology for upscaling. Advanced industrial CMOS process flows allow wafer-scale uniformity and high device yield, but off the shelf transistor processes cannot be directly transferred to qubit structures due to the different designs and operation conditions. To therefore leverage the know-how of the micro-electronics industry, we customize a 300mm wafer fabrication line for silicon MOS qubit integration. With careful optimization and engineering of the MOS gate stack, we report stable and uniform quantum dot operation at the Si/SiOx interface at milli-Kelvin temperature. We extract the charge noise in different devices and under various operation conditions, demonstrating a record-low average noise level of 0.61 ${\mu}$eV/${\sqrt{Hz}}$ at 1 Hz and even below 0.1 ${\mu}$eV/${\sqrt{Hz}}$ for some devices and operating conditions. By statistical analysis of the charge noise with different operation and device parameters, we show that the noise source can indeed be well described by a two-level fluctuator model. This reproducible low noise level, in combination with uniform operation of our quantum dots, marks CMOS manufactured MOS spin qubits as a mature and highly scalable platform for high fidelity qubits., Comment: 22 pages, 13 figures

This paper presents a conceptual overview of the EASIER project and its scope. EASIER focuses on supporting emergency forces in disaster response scenarios with a semi-autonomous mobile manipulator. Specifically, we examine the operator's trust in the system and his/her cognitive load generated by its use. We plan to address different research topics, exploring how shared autonomy, interaction design, and transparency relate to trust and cognitive load. Another goal is to develop non-invasive methods to continuously measure trust and cognitive load in the context of disaster response using a multilevel approach. This project is conducted by multiple academic partners specializing in artificial intelligence, interaction design, and psychology, as well as an industrial partner for disaster response equipment and end-users for framing the project and the experiments in real use-cases., Comment: IEEE 2022 ICRA full day workshop - Shared Autonomy in Physical Human-Robot Interaction: Adaptability and Trust

In zero-sum games, the optimal strategy is well-defined by the Nash equilibrium. However, it is overly conservative when playing against suboptimal opponents and it can not exploit their weaknesses. Limited look-ahead game solving in imperfect-information games allows defeating human experts in massive real-world games such as Poker, Liar's Dice, and Scotland Yard. However, since they approximate Nash equilibrium, they tend to only win slightly against weak opponents. We propose methods combining limited look-ahead solving with an opponent model in order to 1) approximate a best response in large games or 2) compute a robust response with control over the robustness of the response. Both methods can compute the response in real time to previously unseen strategies. We present theoretical guarantees of our methods. We show that existing robust response methods do not work combined with limited look-ahead solving of the shelf, and we propose a novel solution for the issue. Our algorithm performs significantly better than multiple baselines in smaller games and outperforms state-of-the-art methods against SlumBot., Comment: 16 pages, 15 figures

Larger arrays of electron spin qubits require radical improvements in fabrication and device uniformity. Here we demonstrate excellent qubit device uniformity and tunability from 300K down to mK temperatures. This is achieved, for the first time, by integrating an overlapping polycrystalline silicon-based gate stack in an 'all-Silicon' and lithographically flexible 300mm flow. Low-disorder Si/SiO$_2$ is proved by a 10K Hall mobility of $1.5 \cdot 10^4$ $cm^2$/Vs. Well-controlled sensors with low charge noise (3.6 $\mu$eV/$\sqrt{\mathrm{Hz}}$ at 1 Hz) are used for charge sensing down to the last electron. We demonstrate excellent and reproducible interdot coupling control over nearly 2 decades (2-100 GHz). We show spin manipulation and single-shot spin readout, extracting a valley splitting energy of around 150 $\mu$eV. These low-disorder, uniform qubit devices and 300mm fab integration pave the way for fast scale-up to large quantum processors., Comment: 2021 Symposium on VLSI Circuits. IEEE, 2021. Copyright 2021 by The Japan Society of Applied Physics. All rights reserved

The quality of the semiconductor-barrier interface plays a pivotal role in the demonstration of high quality reproducible quantum dots for quantum information processing. In this work, we have measured SiMOSFET Hall bars on undoped Si substrates in order to investigate the quality of the devices fabricated in a full CMOS process. We report a record mobility of 17'500 cm2/Vs with a sub-10 nm oxide thickness indicating a high quality interface, suitable for future qubit applications. We also study the influence of gate materials on the mobilities and discuss the underlying mechanisms, giving insight into further material optimization for large scale quantum processors., Comment: 3 pages plus appendix 1 page with 7 figures

We report on a flexible 300 mm process that optimally combines optical and electron beam lithography to fabricate silicon spin qubits. It enables on-the-fly layout design modifications while allowing devices with either n- or p-type ohmic implants, a pitch smaller than 100 nm, and uniform critical dimensions down to 30 nm with a standard deviation ~ 1.6 nm. Various n- and p-type qubits are characterized in a dilution refrigerator at temperatures ~ 10 mK. Electrical measurements demonstrate well-defined quantum dots, tunable tunnel couplings, and coherent spin control, which are essential requirements for the implementation of a large-scale quantum processor., Comment: 2020 IEEE International Electron Devices Meeting (IEDM), December 12-18, 2020