Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrability and chaos in the quantum brachistochrone problem

The quantum brachistochrone problem addresses the fundamental challenge of achieving the quantum speed limit in applications aiming to realize a given unitary operation in a quantum system. Specifically, it looks into optimization of the transformation of quantum states through controlled Hamiltonians, which form a small subset in the space of the system's observables. Here we introduce a broad family of completely integrable brachistochrone protocols, which arise from a judicious choice of the control Hamiltonian subset. Furthermore, we demonstrate how the inherent stability of the completely integrable protocols makes them numerically tractable and therefore practicable as opposed to their non-integrable counterparts.
Single- and multi-layer micro-scale diffractive lens fabrication for fiber imaging probes with versatile depth-of-field

Hair-thin optical fiber endoscopes have opened up new paradigms for advanced imaging applications in vivo. In certain applications, such as optical coherence tomography (OCT), light-shaping structures may be required on fiber facets to generate needle-like Bessel beams with large depth-of-field, while in others shorter depths of field with high lateral resolutions are preferable. In this paper, we demonstrate a novel method to fabricate light-shaping structures on optical fibres, achieved via bonding encapsulated planar diffractive lenses onto fiber facets. Diffractive metallic structures have the advantages of being simple to design, fabricate and transfer, and our encapsulation approach is scalable to multi-layer stacks. As a demonstration, we design and transfer a Fresnel zone plate and a diffractive axicon onto fiber facets, and show that the latter device generates a needle-like Bessel beam with 350 mu m focal depth. We also evaluate the imaging performance of both devices and show that the axicon fiber is able to maintain focussed images of a USAF resolution target over a 150 mu m distance. Finally, we fabricate a two-layer stack of Fresnel zone plates on a fiber and characterise the modified beam profile and demonstrate good imaging performance. We anticipate our fabrication approach could enable multi-functional complex optical structures (e.g. using plasmonics, polarization control) to be integrated onto fibers for ultra-thin advanced imaging and sensing.
Protocol for certifying entanglement in surface spin systems using a scanning tunneling microscope

Certifying quantum entanglement is a critical step towards realizing quantum-coherent applications of surface spin systems. In this work, we show that entanglement can be unambiguously shown in a scanning tunneling microscope (STM) with electron spin resonance by exploiting the fact that entangled states undergo a free time evolution with a distinct characteristic time constant that clearly distinguishes it from any other time evolution in the system. By implementing a suitable phase control scheme, the phase of this time evolution can be mapped back onto the population of one entangled spin in a pair, which can then be read out reliably using a weakly coupled sensor spin in the junction of the scanning tunneling microscope. We demonstrate through open quantum system simulations with realistic spin systems, which are currently available with spin coherence times of $T_2\approx$ 300 ns, that a signal directly correlated with the degree of entanglement can be measured at a temperature range of 100$-$400 mK accessible in sub-Kelvin cryogenic STM systems.
An Algorithm for Streaming Differentially Private Data

Much of the research in differential privacy has focused on offline applications with the assumption that all data is available at once. When these algorithms are applied in practice to streams where data is collected over time, this either violates the privacy guarantees or results in poor utility. We derive an algorithm for differentially private synthetic streaming data generation, especially curated towards spatial datasets. Furthermore, we provide a general framework for online selective counting among a collection of queries which forms a basis for many tasks such as query answering and synthetic data generation. The utility of our algorithm is verified on both real-world and simulated datasets.
End-To-End Set-Based Training for Neural Network Verification

Neural networks are vulnerable to adversarial attacks, i.e., small input perturbations can result in substantially different outputs of a neural network. Safety-critical environments require neural networks that are robust against input perturbations. However, training and formally verifying robust neural networks is challenging. We address this challenge by employing, for the first time, a end-to-end set-based training procedure that trains robust neural networks for formal verification. Our training procedure drastically simplifies the subsequent formal robustness verification of the trained neural network. While previous research has predominantly focused on augmenting neural network training with adversarial attacks, our approach leverages set-based computing to train neural networks with entire sets of perturbed inputs. Moreover, we demonstrate that our set-based training procedure effectively trains robust neural networks, which are easier to verify. In many cases, set-based trained neural networks outperform neural networks trained with state-of-the-art adversarial attacks.
On Repairing Quantum Programs Using ChatGPT

Automated Program Repair (APR) is a vital area in software engineering aimed at generating automatic patches for vulnerable programs. While numerous techniques have been proposed for repairing classical programs, the realm of quantum programming lacks a comparable automated repair technique. In this initial exploration, we investigate the use of ChatGPT for quantum program repair and evaluate its performance on Bugs4Q, a benchmark suite of quantum program bugs. Our findings demonstrate the feasibility of employing ChatGPT for quantum program repair. Specifically, we assess ChatGPT's ability to address bugs within the Bugs4Q benchmark, revealing its success in repairing 29 out of 38 bugs. This research represents a promising step towards automating the repair process for quantum programs.
STEMFold: Stochastic Temporal Manifold for Multi-Agent Interactions in the Presence of Hidden Agents

Learning accurate, data-driven predictive models for multiple interacting agents following unknown dynamics is crucial in many real-world physical and social systems. In many scenarios, dynamics prediction must be performed under incomplete observations, i.e., only a subset of agents are known and observable from a larger topological system while the behaviors of the unobserved agents and their interactions with the observed agents are not known. When only incomplete observations of a dynamical system are available, so that some states remain hidden, it is generally not possible to learn a closed-form model in these variables using either analytic or data-driven techniques. In this work, we propose STEMFold, a spatiotemporal attention-based generative model, to learn a stochastic manifold to predict the underlying unmeasured dynamics of the multi-agent system from observations of only visible agents. Our analytical results motivate STEMFold design using a spatiotemporal graph with time anchors to effectively map the observations of visible agents to a stochastic manifold with no prior information about interaction graph topology. We empirically evaluated our method on two simulations and two real-world datasets, where it outperformed existing networks in predicting complex multiagent interactions, even with many unobserved agents.
Semantic Huffman Coding using Synonymous Mapping

Semantic communication stands out as a highly promising avenue for future developments in communications. Theoretically, source compression coding based on semantics can achieve lower rates than Shannon entropy. This paper introduces a semantic Huffman coding built upon semantic information theory. By incorporating synonymous mapping and synonymous sets, semantic Huffman coding can achieve shorter average code lengths. Furthermore, we demonstrate that semantic Huffman coding theoretically have the capability to approximate semantic entropy. Experimental results indicate that, under the condition of semantic lossless, semantic Huffman coding exhibits clear advantages in compression efficiency over classical Huffman coding.
PepGB: Facilitating peptide drug discovery via graph neural networks

Peptides offer great biomedical potential and serve as promising drug candidates. Currently, the majority of approved peptide drugs are directly derived from well-explored natural human peptides. It is quite necessary to utilize advanced deep learning techniques to identify novel peptide drugs in the vast, unexplored biochemical space. Despite various in silico methods having been developed to accelerate peptide early drug discovery, existing models face challenges of overfitting and lacking generalizability due to the limited size, imbalanced distribution and inconsistent quality of experimental data. In this study, we propose PepGB, a deep learning framework to facilitate peptide early drug discovery by predicting peptide-protein interactions (PepPIs). Employing graph neural networks, PepGB incorporates a fine-grained perturbation module and a dual-view objective with contrastive learning-based peptide pre-trained representation to predict PepPIs. Through rigorous evaluations, we demonstrated that PepGB greatly outperforms baselines and can accurately identify PepPIs for novel targets and peptide hits, thereby contributing to the target identification and hit discovery processes. Next, we derive an extended version, diPepGB, to tackle the bottleneck of modeling highly imbalanced data prevalent in lead generation and optimization processes. Utilizing directed edges to represent relative binding strength between two peptide nodes, diPepGB achieves superior performance in real-world assays. In summary, our proposed frameworks can serve as potent tools to facilitate peptide early drug discovery.
SSR: SAM is a Strong Regularizer for domain adaptive semantic segmentation

We introduced SSR, which utilizes SAM (segment-anything) as a strong regularizer during training, to greatly enhance the robustness of the image encoder for handling various domains. Specifically, given the fact that SAM is pre-trained with a large number of images over the internet, which cover a diverse variety of domains, the feature encoding extracted by the SAM is obviously less dependent on specific domains when compared to the traditional ImageNet pre-trained image encoder. Meanwhile, the ImageNet pre-trained image encoder is still a mature choice of backbone for the semantic segmentation task, especially when the SAM is category-irrelevant. As a result, our SSR provides a simple yet highly effective design. It uses the ImageNet pre-trained image encoder as the backbone, and the intermediate feature of each stage (ie there are 4 stages in MiT-B5) is regularized by SAM during training. After extensive experimentation on GTA5$\rightarrow$Cityscapes, our SSR significantly improved performance over the baseline without introducing any extra inference overhead.
Large Language Model Adaptation for Financial Sentiment Analysis

Natural language processing (NLP) has recently gained relevance within financial institutions by providing highly valuable insights into companies and markets' financial documents. However, the landscape of the financial domain presents extra challenges for NLP, due to the complexity of the texts and the use of specific terminology. Generalist language models tend to fall short in tasks specifically tailored for finance, even when using large language models (LLMs) with great natural language understanding and generative capabilities. This paper presents a study on LLM adaptation methods targeted at the financial domain and with high emphasis on financial sentiment analysis. To this purpose, two foundation models with less than 1.5B parameters have been adapted using a wide range of strategies. We show that through careful fine-tuning on both financial documents and instructions, these foundation models can be adapted to the target domain. Moreover, we observe that small LLMs have comparable performance to larger scale models, while being more efficient in terms of parameters and data. In addition to the models, we show how to generate artificial instructions through LLMs to augment the number of samples of the instruction dataset.
An approach to Julia Robinson numbers through the lattice of subfields

By fully describing the lattice of subfields of some towers of number fields built by iterating square roots, we obtain infinitely many fields, each of them either contradicts Julia Robinson's problem (obtaining a JR-number $4$ which is not a minimum) or gives a Julia Robinson number strictly between four and infinity. This improves a previous result by M. Castillo and the same authors.
On Log-Concave-Tailed Chaos and the Restricted Isometry Property

We develop a novel decoupling inequality for nonhomogeneous order $2$ non-Gaussian chaoses. Having this decoupling inequality, we show two types of the $p$-th moment bounds for the suprema of a log-concave-tailed chaos process based on some chaining techniques and the generalized majorizing measure theorems for canonical processes. As applications, we show the restricted isometry property of partial random circulant matrices and time-frequency structured random matrices induced by standard $\alpha$-subexponential vectors, $1\le \alpha\le 2$, which extends the previously known results for the sub-Gaussian case.
Expert with Clustering: Hierarchical Online Preference Learning Framework

Emerging mobility systems are increasingly capable of recommending options to mobility users, to guide them towards personalized yet sustainable system outcomes. Even more so than the typical recommendation system, it is crucial to minimize regret, because 1) the mobility options directly affect the lives of the users, and 2) the system sustainability relies on sufficient user participation. In this study, we consider accelerating user preference learning by exploiting a low-dimensional latent space that captures the mobility preferences of users. We introduce a hierarchical contextual bandit framework named Expert with Clustering (EWC), which integrates clustering techniques and prediction with expert advice. EWC efficiently utilizes hierarchical user information and incorporates a novel Loss-guided Distance metric. This metric is instrumental in generating more representative cluster centroids. In a recommendation scenario with $N$ users, $T$ rounds per user, and $K$ options, our algorithm achieves a regret bound of $O(N\sqrt{T\log K} + NT)$. This bound consists of two parts: the first term is the regret from the Hedge algorithm, and the second term depends on the average loss from clustering. The algorithm performs with low regret, especially when a latent hierarchical structure exists among users. This regret bound underscores the theoretical and experimental efficacy of EWC, particularly in scenarios that demand rapid learning and adaptation. Experimental results highlight that EWC can substantially reduce regret by 27.57% compared to the LinUCB baseline. Our work offers a data-efficient approach to capturing both individual and collective behaviors, making it highly applicable to contexts with hierarchical structures. We expect the algorithm to be applicable to other settings with layered nuances of user preferences and information.
Discrete Approximation of Optimal Transport on Compact Spaces

We investigate the approximation of Monge--Kantorovich problems on general compact metric spaces, showing that optimal values, plans and maps can be effectively approximated via a fully discrete method. First we approximate optimal values and plans by solving finite dimensional discretizations of the corresponding Kantorovich problem. Then we approximate optimal maps by means of the usual barycentric projection or by an analogous procedure available in general spaces without a linear structure. We prove the convergence of all these approximants in full generality and show that our convergence results are sharp.
Many-excitation removal of a transmon qubit using a single-junction quantum-circuit refrigerator and a two-tone microwave drive

Achieving fast and precise initialization of qubits is a critical requirement for the successful operation of quantum computers. The combination of engineered environments with all-microwave techniques has recently emerged as a promising approach for the reset of superconducting quantum devices. In this work, we experimentally demonstrate the utilization of a single-junction quantum-circuit refrigerator (QCR) for an expeditious removal of several excitations from a transmon qubit. The QCR is indirectly coupled to the transmon through a resonator in the dispersive regime, constituting a carefully engineered environmental spectrum for the transmon. Using single-shot readout, we observe excitation stabilization times down to roughly $500$ ns, a $20$-fold speedup with QCR and a simultaneous two-tone drive addressing the $e$-$f$ and $f0$-$g1$ transitions of the system. Our results are obtained at a $48$-mK fridge temperature and without postselection, fully capturing the advantage of the protocol for the short-time dynamics and the drive-induced detrimental asymptotic behavior in the presence of relatively hot other baths of the transmon. We validate our results with a detailed Liouvillian model truncated up to the three-excitation subspace, from which we estimate the performance of the protocol in optimized scenarios, such as cold transmon baths and fine-tuned driving frequencies. These results pave the way for optimized reset of quantum-electric devices using engineered environments and for dissipation-engineered state preparation.
Extension of Recurrent Kernels to different Reservoir Computing topologies

Reservoir Computing (RC) has become popular in recent years due to its fast and efficient computational capabilities. Standard RC has been shown to be equivalent in the asymptotic limit to Recurrent Kernels, which helps in analyzing its expressive power. However, many well-established RC paradigms, such as Leaky RC, Sparse RC, and Deep RC, are yet to be analyzed in such a way. This study aims to fill this gap by providing an empirical analysis of the equivalence of specific RC architectures with their corresponding Recurrent Kernel formulation. We conduct a convergence study by varying the activation function implemented in each architecture. Our study also sheds light on the role of sparse connections in RC architectures and propose an optimal sparsity level that depends on the reservoir size. Furthermore, our systematic analysis shows that in Deep RC models, convergence is better achieved with successive reservoirs of decreasing sizes.
Heating of the Atmospheres of Short-orbit Exoplanets by Their Rapid Orbital Motion through an Extreme Space Environment

Exoplanets with short orbit periods reside very close to their host stars. They transition very rapidly between different sectors of the circumstellar space environment along their orbit, leading to large variations of the magnetic field in the vicinity of the planet on short timescales. This rapid change of the magnetic flux through the conducting and resistive layer of the planetary upper atmosphere may drive currents that dissipate in the form of Joule heating (JH). Here, we estimate the amount of JH dissipation in the upper atmosphere of Trappist-1e, and two hypothetical planets orbiting the Sun in close-in orbits. We find that the rapid orbital motion could drive a significant amount of atmospheric heating and could significantly affect the planetary atmosphere escape rate. Thus, the process should be accounted for when studying the long-term evolution of exoplanetary atmospheres.
The geodesic dispersion phenomenon in random fields dynamics

Random fields are ubiquitous mathematical structures in physics, with applications ranging from thermodynamics and statistical physics to quantum field theory and cosmology. Recent works on information geometry of Gaussian random fields proposed mathematical expressions for the components of the metric tensor of the underlying parametric space, allowing the computation of the curvature in each point of the manifold. In this study, our hypothesis is that time irreversibility in Gaussian random fields dynamics is a direct consequence of geometric properties (curvature) of their parametric space. In order to validate this hypothesis, we compute the components of the metric tensor and derive the twenty seven Christoffel symbols of the metric to define the Euler-Lagrange equations, a system of partial differential equations that are used to build geodesic curves in Riemannian manifolds. After that, by the application of the fourth-order Runge-Kutta method and Markov Chain Monte Carlo simulation, we numerically build geodesic curves starting from an arbitrary initial point in the manifold. The obtained results show that, when the system undergoes phase transitions, the geodesic curve obtained by time reversing the computational simulation diverges from the original curve, showing a strange effect that we called the geodesic dispersion phenomenon, which suggests that time irreversibility in random fields is related to the intrinsic geometry of their parametric space.
A Nonparametric Bayes Approach to Online Activity Prediction

Accurately predicting the onset of specific activities within defined timeframes holds significant importance in several applied contexts. In particular, accurate prediction of the number of future users that will be exposed to an intervention is an important piece of information for experimenters running online experiments (A/B tests). In this work, we propose a novel approach to predict the number of users that will be active in a given time period, as well as the temporal trajectory needed to attain a desired user participation threshold. We model user activity using a Bayesian nonparametric approach which allows us to capture the underlying heterogeneity in user engagement. We derive closed-form expressions for the number of new users expected in a given period, and a simple Monte Carlo algorithm targeting the posterior distribution of the number of days needed to attain a desired number of users; the latter is important for experimental planning. We illustrate the performance of our approach via several experiments on synthetic and real world data, in which we show that our novel method outperforms existing competitors.
Dimensional gain in sensing through higher-dimensional quantum spin chain

Recent breakthroughs in quantum technology pave the way for extensive utilization of higher-dimensional quantum systems, which outperform their qubit counterparts in terms of capabilities and versatility. We present a framework for accurately predicting weak external magnetic fields using a higher-dimensional many-body quantum probe. We demonstrate that dimension serves as a valuable resource for quantum sensing when a transverse spin-s Ising chain interacts locally with a magnetic field whose strength has to be determined. We observe the distinct performance of sensors for spin chains with half-integer and integer spins. Furthermore, we highlight that the time duration appropriate for quantum-enhanced sensing increases with the increase of dimension. Additionally, we observe that, in addition to nearest-neighbor interactions, incorporating interactions between the next nearest-neighbor sites increases sensing precision, particularly for spin chains with integer spins. We also prove the dimensional-dependence of the bound on quantum Fisher information which provides the limit on the precision in estimating parameters.