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Measurement-based quantum computing offers a promising route towards scalable, universal photonic quantum computation. This approach relies on the deterministic and efficient generation of photonic graph states in which many photons are mutually entangled with various topologies. Recently, deterministic sources of graph states have been demonstrated with quantum emitters in both the optical and microwave domains. In this work, we demonstrate deterministic and reconfigurable graph state generation with optical solid-state integrated quantum emitters. Specifically, we use a single semiconductor quantum dot in a cavity to generate caterpillar graph states, the most general type of graph state that can be produced with a single emitter. By using fast detuned optical pulses, we achieve full control over the spin state, enabling us to vary the entanglement topology at will. We perform quantum state tomography of two successive photons, measuring Bell state fidelities up to 0.80$\pm$0.04 and concurrences up to 0.69$\pm$0.09, while maintaining high photon indistinguishability. This simple optical scheme, compatible with commercially available quantum dot-based single photon sources, brings us a step closer to fault-tolerant quantum computing with spins and photons.

This paper introduces a novel mobile phone control architecture, termed ``app agents", for efficient interactions and controls across various Android apps. The proposed Lightweight Multi-modal App Control (LiMAC) takes as input a textual goal and a sequence of past mobile observations, such as screenshots and corresponding UI trees, to generate precise actions. To address the computational constraints inherent to smartphones, within LiMAC, we introduce a small Action Transformer (AcT) integrated with a fine-tuned vision-language model (VLM) for real-time decision-making and task execution. We evaluate LiMAC on two open-source mobile control datasets, demonstrating the superior performance of our small-form-factor approach against fine-tuned versions of open-source VLMs, such as Florence2 and Qwen2-VL. It also significantly outperforms prompt engineering baselines utilising closed-source foundation models like GPT-4o. More specifically, LiMAC increases the overall action accuracy by up to 19% compared to fine-tuned VLMs, and up to 42% compared to prompt-engineering baselines.

Coherent quantum emitters are a central resource for advanced quantum technologies. Hexagonal boron nitride (hBN) hosts a range of quantum emitters that can be engineered using techniques such as high-temperature annealing, optical doping, and irradiation with electrons or ions. Here, we demonstrate that such processes can degrade the coherence, and hence the functionality, of quantum emitters in hBN. Specifically, we show that hBN annealing and doping methods that are used routinely in hBN nanofabrication protocols give rise to decoherence of B-center quantum emitters. The decoherence is characterized in detail, and attributed to defects that act as charge traps which fluctuate electrostatically during SPE excitation and induce spectral diffusion. The decoherence is minimal when the emitters are engineered by electron beam irradiation of as-grown, pristine flakes of hBN, where B-center linewidths approach the lifetime limit needed for quantum applications involving interference and entanglement. Our work highlights the critical importance of crystal lattice quality to achieving coherent quantum emitters in hBN, despite the common perception that the hBN lattice and hBN SPEs are highly-stable and resilient against chemical and thermal degradation. It underscores the need for nanofabrication techniques that are minimally invasive and avoid crystal damage when engineering hBN SPEs and devices for quantum-coherent technologies.

This study describes and evaluates i-GRIP, a novel movement intention estimator designed to facilitate the control of assistive devices for grasping tasks in individuals with upper limb impairments. Operating within a collaborative grasping control paradigm, the users naturally move their hand towards an object they wish to grasp and i-GRIP identifies the target of the movement and selects an appropriate grip for the assistive device to perform. In an experimental study involving 11 healthy participants, i-GRIP exhibited promising estimation performances and responsiveness. The proposed approach paves the way towards more intuitive control of grasping assistive device for individuals with upper limb impairments., Comment: International Conference on Humanoid Robots, IEEE-RAS, Nov 2024, Nancy, France

The frequency or color of photons is an attractive degree of freedom to encode and distribute the quantum information over long distances. However, the generation of frequency-encoded photonic qubits has so far relied on probabilistic non-linear single-photon sources and inefficient gates. Here, we demonstrate the deterministic generation of photonic qubits hyper-encoded in frequency and polarization based on a semiconductor quantum dot in a cavity. We exploit the double dipole structure of a neutral exciton and demonstrate the generation of any quantum superposition in amplitude and phase, controlled by the polarization of the pump laser pulse. The source generates frequency-polarization single-photon qubits at a rate of 4 MHz corresponding to a generation probability at the first lens of 28 $\pm$ 2%, with a photon number purity > 98%. The photons show an indistinguishability > 91% for each dipole and 88% for a balanced quantum superposition of both. The density matrix of the hyper-encoded photonic state is measured by time-resolved polarization tomography, evidencing a fidelity to the target state of 94 $\pm$ 8% and concurrence of 77 $\pm$ 2%, here limited by frequency overlap in our device. Our approach brings the advantages of quantum dot sources to the field of quantum information processing based on frequency encoding.

This study introduces i-GRIP, an innovative movement goal estimator designed to facilitate the control of assistive devices for grasping tasks in individuals with upperlimb impairments. The algorithm operates within a collaborative control paradigm, eliminating the need for specific user actions apart from naturally moving their hand toward a desired object. i-GRIP analyzes the hand's movement in an object-populated scene to determine its target and select an appropriate grip. In an experimental study involving 11 healthy participants, i-GRIP showed promising goal estimation performances and responsiveness and the potential to facilitate the daily use of assistive devices for individuals with upper-limb impairments in the future.

B-centres in hexagonal boron nitride (hBN) are gaining significant research interest for quantum photonics applications due to precise emitter positioning and highly reproducible emission wavelengths. Here, we leverage the layered nature of hBN to directly measure the quantum efficiency (QE) of single B-centres. The defects were engineered in a 35 nm flake of hBN using electron beam irradiation, and the local dielectric environment was altered by transferring a 250 nm hBN flake on top of the one containing the emitters. By analysing the resulting change in measured lifetimes, we determined the QE of B-centres in the thin flake of hBN, as well as after the transfer. Our results indicate that B-centres located in thin flakes can exhibit QEs higher than 40%. Near-unity QEs are achievable under reasonable Purcell enhancement for emitters embedded in thick flakes of hBN, highlighting their promise for quantum photonics applications.

The integration of van der Waals (vdW) materials into photonic devices has laid out a foundation for many new quantum and optoelectronic applications. Despite tremendous progress in the nanofabrication of photonic building blocks from vdW crystals, there are still limitations, specifically with large-area devices and masking. Here, we focus on hexagonal boron nitride (hBN) as a vdW material and present a double etch method that overcomes problems associated with methods that employ metallic films and resist-based films for masking. Efficacy of the developed protocol is demonstrated by designing and fabricating a set of functional photonic components including waveguides, ring resonators and photonic crystal cavities. The functionality of the fabricated structures is demonstrated through optical characterization over several key spectral ranges. These include the near-infrared and blue ranges, where the hBN boron vacancy (VB-) spin defects and the coherent B center quantum emitters emit, respectively. The double etch method enables fabrication of high-quality factor optical cavities and constitutes a promising pathway toward on-chip integration of vdW materials., Comment: 10 pages, 4 figures

Electrostrictive materials exhibit a strain that is proportional to the square of the induced polarization. In linear dielectrics where the permittivity is constant, this electromechanical strain is also proportional to the square of the electric field. However, under increasing amplitudes of the driving field, the electromechanical strain sometimes saturates; the electrostrictive coefficients therefore appear to depend on the amplitude of the electric field used to measure them. Here, we present a methodology showing that this apparent field dependence is a consequence of neglecting higher-order electromechanical phenomena. When these are taken into account, not only do the electrostrictive coefficients remain constant but the signs of the high-order coefficients enable the prediction of the saturation behavior from a single measurement. We illustrate this approach on both classical and non-classical (so-called ``giant'') electrostrictors.

To ensure that large language model (LLM) responses are helpful and non-toxic, a reward model trained on human preference data is usually used. LLM responses with high rewards are then selected through best-of-$n$ (BoN) sampling or the LLM is further optimized to produce responses with high rewards through reinforcement learning from human feedback (RLHF). However, these processes are susceptible to reward overoptimization or `hacking', where responses receive high rewards due to imperfections in the reward model rather than true preference, particularly as prompts or responses deviate from the training data. To address these challenges, we propose to train a Bayesian reward model, which signals higher uncertainty further from the training data distribution. We trained Bayesian reward models using Laplace approximation on LoRA weights, and found that the resulting uncertainty estimates can effectively mitigate reward overoptimization in BoN sampling.

A key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL). However, constructing a standalone RL policy that maps perception to action directly encounters severe problems, chief among them being its lack of generality across multiple tasks and the need for a large amount of training data. The leading cause is that it cannot effectively integrate prior information into the perception-action cycle when devising the policy. Large language models (LLMs) emerged as a fundamental way to incorporate cross-domain knowledge into AI agents but lack crucial learning and adaptation toward specific decision problems. This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies. Our methodology is motivated by the modularity found in the human brain. The framework utilises the construction of intrinsic and extrinsic functions to add previous understandings of reasoning structures. It also provides the adaptive ability to learn models inside every module or function, consistent with the modular structure of cognitive processes. We describe the framework in-depth and compare it with other AI pipelines and existing frameworks. The paper explores practical applications, covering experiments that show the effectiveness of our method. Our results indicate that AI agents perform and adapt far better when organised reasoning and prior knowledge are embedded. This opens the door to more resilient and general AI agent systems., Comment: paper and appendix, 27 pages

Semantic segmentation is a crucial task in medical image processing, essential for segmenting organs or lesions such as tumors. In this study we aim to improve automated segmentation in CBCTs through multi-task learning. To evaluate effects on different volume qualities, a CBCT dataset is synthesised from the CT Liver Tumor Segmentation Benchmark (LiTS) dataset. To improve segmentation, two approaches are investigated. First, we perform multi-task learning to add morphology based regularization through a volume reconstruction task. Second, we use this reconstruction task to reconstruct the best quality CBCT (most similar to the original CT), facilitating denoising effects. We explore both holistic and patch-based approaches. Our findings reveal that, especially using a patch-based approach, multi-task learning improves segmentation in most cases and that these results can further be improved by our denoising approach., Comment: Accepted at German Conference on Medical Image Computing (BVM) 2024

The expected hitting time from vertex $a$ to vertex $b$, $H(a,b)$, is the expected value of the time it takes a random walk starting at $a$ to reach $b$. In this paper, we give estimates for $H(a,b)$ when the distance between $a$ and $b$ is comparable to the diameter of the graph, and the graph satisfies a Harnack condition. We show that, in such cases, $H(a,b)$ can be estimated in terms of the volumes of balls around $b$. Using our results, we estimate $H(a,b)$ on various graphs, such as rectangular tori, some convex traces in $\mathbb{Z}^d$, and fractal graphs. Our proofs use heat kernel estimates., Comment: 24 pages, 5 figures

The widespread deployment of inverter-based resources (IBRs) renders distribution systems susceptible to transmission-level faults. This paper presents a comprehensive analysis of the impact of transmission-level faults on 3-phase and 1-phase distribution IBR operation. To evaluate distributed IBR tripping across various phases and locations on a distribution feeder, we conduct simulations of both symmetrical and unsymmetrical transmission faults at progressively greater electrical distances on a real-time transmission and distribution (T&D) co-simulation platform. The IBR power-to-load ratios (PLRs) at 50%, 100%, and 300% are considered to emulate low, medium, and high IBR conditions. Our results indicate that, while 1-phase and 2-phase faults typically trigger fewer IBR trips when compared to 3-phase faults, a significant power imbalance arises from the tripping of 1-phase IBRs on the affected phases. The imbalance can result in significant power quality problems and unintended equipment tripping. It may be necessary to design fault-ride-through mechanisms specifically tailored to 1-phase IBRs to help mitigate the power imbalances caused by unbalanced faults.

We study the probability that a random walk started inside a subgraph of a larger graph exits that subgraph (or, equivalently, hits the exterior boundary of the subgraph). Considering the chance a random walk started in the subgraph never leaves the subgraph leads to a notion we call "survival" transience, or $S$-transience. In the case where the heat kernel of the larger graph satisfies two-sided Gaussian estimates, we prove an upper bound on the probability of hitting the boundary of the subgraph. Under the additional hypothesis that the subgraph is inner uniform, we prove a two-sided estimate for this probability. The estimate depends upon a harmonic function in the subgraph. We also provide two-sided estimates for related probabilities, such as the harmonic measure (the chance the walk exits the subgraph at a particular point on its boundary)., Comment: 30 pages, 3 figures

1.8 micrometer-thick magnesium hydride films were synthesized in a single-step process by reactive plasma-assisted sputtering. The MgH2 thin films, which were deposited on two types of flexible surfaces (namely graphite and polyimide foils) were found to adhere on both substrates. In all cases, XRD analysis revealed an as-deposited thin film consisting of alpha-MgH2, a tetragonal, rutile-type crystal structure (space group \#136). The hydrogen sorption capacities of the uncapped films were studied over successive desorption/absorption cycles performed at 350 {\textdegree}C. The first desorption always shows a slow kinetics that can be explained by a superficial oxidation of the films. However, once the passivating layer is removed, the following dehydrogenations occur faster. Multiple cycling of the film deposited on polyimide resulted in delamination of the film and its conversion into loose powder. As for MgH2 deposited on the flexible graphite substrate, a fully reversible capacity was observed over 28 cycles with no delamination of the film. Upon cycling, the microstructure of the film has evolved from homogeneous fibrous to an untextured morphology with a higher degree of crystallinity.

Reinforcement learning from human feedback (RLHF) is a standard approach for fine-tuning large language models to follow instructions. As part of this process, learned reward models are used to approximately model human preferences. However, as imperfect representations of the "true" reward, these learned reward models are susceptible to overoptimization. Gao et al. (2023) studied this phenomenon in a synthetic human feedback setup with a significantly larger "gold" reward model acting as the true reward (instead of humans) and showed that overoptimization remains a persistent problem regardless of the size of the proxy reward model and training data used. Using a similar setup, we conduct a systematic study to evaluate the efficacy of using ensemble-based conservative optimization objectives, specifically worst-case optimization (WCO) and uncertainty-weighted optimization (UWO), for mitigating reward model overoptimization when using two optimization methods: (a) best-of-n sampling (BoN) (b) proximal policy optimization (PPO). We additionally extend the setup of Gao et al. (2023) to include 25% label noise to better mirror real-world conditions. Both with and without label noise, we find that conservative optimization practically eliminates overoptimization and improves performance by up to 70% for BoN sampling. For PPO, ensemble-based conservative optimization always reduces overoptimization and outperforms single reward model optimization. Moreover, combining it with a small KL penalty successfully prevents overoptimization at no performance cost. Overall, our results demonstrate that ensemble-based conservative optimization can effectively counter overoptimization., Comment: Accepted at ICLR 2024

Assistive devices for indivuals with upper-limb movement often lack controllability and intuitiveness, in particular for grasping function. In this work, we introduce a novel user interface for grasping movement control in which the user delegates the grasping task decisions to the device, only moving their (potentially prosthetic) hand toward the targeted object.

We analyse the numbers of closed paths of length $k\in\mathbb{N}$ on two important regular lattices: the hexagonal lattice (also called $\textit{graphene}$ in chemistry) and its dual triangular lattice. These numbers form a moment sequence of specific random variables connected to the distance of a position of a planar random flight (in three steps) from the origin. Here, we refer to such a random variable as a $\textit{random eigenvalue}$ of the underlying lattice. Explicit formulas for the probability density and characteristic functions of these random eigenvalues are given for both the hexagonal and the triangular lattice. Furthermore, it is proven that both probability distributions can be approximated by a functional of the random variable uniformly distributed on increasing intervals $[0,b]$ as $b\to\infty$. This yields a straightforward method to simulate these random eigenvalues without generating graphene and triangular lattice graphs. To demonstrate this approximation, we first prove a key integral identity for a specific series containing the third powers of the modified Bessel functions $I_n$ of $n$th order, $n\in\mathbb{Z}$. Such series play a crucial role in various contexts, in particular, in analysis, combinatorics, and theoretical physics.

Energy-based models (EBMs) are generative models inspired by statistical physics with a wide range of applications in unsupervised learning. Their performance is best measured by the cross-entropy (CE) of the model distribution relative to the data distribution. Using the CE as the objective for training is however challenging because the computation of its gradient with respect to the model parameters requires sampling the model distribution. Here we show how results for nonequilibrium thermodynamics based on Jarzynski equality together with tools from sequential Monte-Carlo sampling can be used to perform this computation efficiently and avoid the uncontrolled approximations made using the standard contrastive divergence algorithm. Specifically, we introduce a modification of the unadjusted Langevin algorithm (ULA) in which each walker acquires a weight that enables the estimation of the gradient of the cross-entropy at any step during GD, thereby bypassing sampling biases induced by slow mixing of ULA. We illustrate these results with numerical experiments on Gaussian mixture distributions as well as the MNIST dataset. We show that the proposed approach outperforms methods based on the contrastive divergence algorithm in all the considered situations.