Abstract

Deep thermalization refers to the emergence of Haar-like randomness from quantum systems upon partial measurements. As a generalization of quantum thermalization, it is often associated with high complexity and entanglement. Here, we introduce computational deep thermalization and construct the fastest possible dynamics exhibiting it at infinite effective temperature. Our circuit dynamics produce quantum states with low entanglement in polylogarithmic depth that are indistinguishable from Haar random states to any computationally bounded observer. Importantly, the observer is allowed to request many copies of the same residual state obtained from partial projective measurements on the state—this condition is beyond the standard settings of quantum pseudorandomness but natural for deep thermalization. In cryptographic terms, these states are pseudorandom and pseudoentangled, and crucially, they retain these properties under local measurements. Our results demonstrate a new form of computational thermalization in which thermal-like behavior arises from structured quantum states endowed with cryptographic properties instead of from highly unstructured ensembles. The low resource complexity of preparing these states suggests scalable simulations of deep thermalization using quantum computers. Our Letter also motivates the study of computational quantum pseudorandomness beyond BQP observers.

Abstract

Certain types of quantum computing platforms, such as those realized using Rydberg atoms or Kerr-cat qubits, are natively more susceptible to Pauli-Z noise than Pauli-X noise, or vice versa. On such hardware, it is useful to ensure that computations use only gates that maintain the Z-bias (or X-bias) in the noise. This is so that quantum error-correcting codes tailored for biased-noise models can be used to provide fault-tolerance on these platforms. In this paper, we follow up on the recent work of Fellous-Asiani et al. (npj Quantum Inf., 2025) in studying the structure and properties of bias-preserving gates. Our main contributions are threefold: (1) We give a novel characterization of Z-bias-preserving gates based on their decomposition as a linear combination of Pauli operators. (2) We show that any Z-bias-preserving gate can be approximated arbitrarily well using only gates from the set {X,R_z(theta),CNOT,CCNOT}, where theta is any irrational multiple of 2pi. (3) We prove, by drawing a connection with coherence resource theory, that any Z-bias-preserving logical operator acting on the logical qubits of a Calderbank-Shor-Steane (CSS) code can be realized by applying Z-bias-preserving gates on the physical qubits. Along the way, we also demonstrate that Z-bias-preserving gates are far from being universal for quantum computation.

Abstract

Air pollution represents one of the most critical environmental and public health challenges globally, with traditional sensor-based monitoring systems facing significant scalability and economic constraints. Image-based air quality estimation has emerged as a promising alternative, leveraging the visual characteristics of atmospheric pollutants in traffic scenes. However, existing methods suffer from limited cross-city generalization and inadequate exploitation of multi-view perspectives. We present AQIFormer, a novel transformer-based ensemble architecture that addresses these fundamental limitations through innovative dual-view integration, weather-aware attention mechanisms, and comprehensive multi-task learning. Our approach uniquely combines front and rear traffic imagery with meteorological parameters to achieve robust air quality classification across diverse urban environments. Extensive evaluation on a dataset of 26,678 synchronized front–rear image pairs demonstrates 89.96% accuracy, representing a 14.96% improvement over state-of-the-art methods. Most importantly, our model maintains exceptional cross-city generalization capabilities, achieving 81.67% accuracy on an independent dataset collected in Nagpur, India, with only 8.29% performance degradation using few-shot adaptation and minimal training samples.

Abstract

Traditional solar forecasting models are based on several years of site-specific historical irradiance data, often spanning five or more years, which are unavailable for newer photovoltaic farms. As renewable energy is highly intermittent, building accurate solar irradiance forecasting systems that are data-efficient is essential for efficient grid management and enabling the ongoing proliferation of solar energy, which is crucial to achieve the United Nations' net zero goals. In this work, we propose SPIRIT, a novel framework leveraging foundation models for solar irradiance forecasting, making it applicable to newer solar installations. Our approach outperforms state-of-the-art models in zero-shot transfer learning by upto 70%, enabling effective performance at new locations without relying on any past data. Further improvements in performance are achieved through fine-tuning, as more location-specific data becomes available. These findings are supported by statistical significance, further validating our approach. By dramatically reducing the forecasting setup timeline, SPIRIT accelerates solar farm deployment in all potential global sites, most of which lack historical data, thereby democratizing access to clean energy and enabling participation in the renewable energy transition.

Abstract

In recent times, denoising diffusion probabilistic models (DPMs) have proven to show significant success in medical image generation and denoising, while also serving as powerful representation learners for downstream tasks such as segmentation. However, their effectiveness in segmentation is limited by the need for detailed pixel-wise annotations, which are expensive, time-consuming, and require expert knowledge—a significant bottleneck in real-world clinical applications. In order to mitigate this limitation of label-efficiency, we propose a fast and efficient model named FastTextDiff, a diffusion-based segmentation model that integrates medical text annotations to enhance semantic representations. Our approach leverages ModernBERT, a transformer-based language model capable of processing long medical text sequences, to establish a strong connection between textual annotations and semantic meaning in medical imaging. ModernBERT can efficiently encode clinical knowledge for directing segmentation tasks since it has been trained on both MIMIC-III and MIMIC-IV. Label-efficient segmentation with enhanced performance is made possible by cross-modal attention processes, which enable smooth interaction between visual and textual modalities. This study validates ModernBERT as a quick and scalable substitute for Clinical BioBERT in diffusion-based segmentation pipelines and demonstrates the promise of multi-modal techniques for medical image analysis. By replacing Clinical BioBERT with ModernBERT, our model benefits from Flash Attention 2 for memory-efficient training, an alternating attention mechanism for computational efficiency, and a training corpus of 2 trillion tokens, significantly improving text-based medical image segmentation. Our experiments demonstrate that FastTextDiff achieves better segmentation performance and faster training compared to traditional diffusion-based models. We release this trained model on Hugging Face and GitHub.

Abstract

Natural fibre composites (NFCs) have gained prominence as sustainable, cost-effective alternatives to synthetic fibre reinforcements in masonry structural retrofitting. This paper reviews the extraction, treatment, and fabrication of natural fibres such as jute, flax, sisal, hemp, and basalt into Fibre Reinforced Polymer (FRP) and Textile Reinforced Mortar (TRM) systems suited for masonry appli-cations. Experimental and field studies demonstrate that natural FRP composites improve tensile strength, ductility, and seismic resilience of unreinforced masonry walls, while natural TRM composites offer enhanced environmental compatibil-ity, fire resistance, and vapour permeability, which are essential for heritage ma-sonry preservation. The review assesses mechanical performance, durability chal-lenges related to moisture and ultraviolet (UV) exposure, and sustainability bene-fits, including reduced embodied energy and biodegradability. Practical case stud-ies confirm the feasibility of locally sourced natural fibre retrofits in seismic zones. Challenges such as variability in fibre properties and the need for stand-ardized design codes are discussed alongside future research directions focusing on hybrid composites, bio-based matrices, and sensor integration for structural health monitoring. This review consolidates current advances and prospects, positioning NFCs in form of FRP and TRM composites as viable, eco-friendly retrofit technologies for resilient and sustainable masonry infrastructure.

Abstract

Bharatanatyam is one of the most celebrated classical dance forms in India. It is performed and enjoyed by art enthusiasts all over the world. It comprises expressions, hand gestures, footwork, rhythmic patterns, eye, neck and head movements, lyrics, and music. These basic components are integrated and woven into a beautifully crafted visual story. However, beneath this creative visual craft lies a structured compilation of technical, mathematical, computational, and modular aspects. In this paper, we provide a database schema to store the artistic components in a structured relational database model. By providing a database schema, we provide a layered approach to store Bharatanatyam choreographies. This layered approach helps in building choreographies by working individually on different modules of the art form like the foot work, hand movement, etc, and consolidating them to form and store sequences. Bharatanatyam choreographies are a collection of sequences. In this paper, we discuss how complete choreographies are modelled through the logical sequencing of these components. Our paper lays the foundation for bridging traditional knowledge of the performing arts with technologies to help in digital archiving, cultural analytics, and choreography suggestions.

Abstract

Non-Structural Elements (NSEs) such as building contents, appendages to buildings, utilities and services are highly vulnerable during earthquakes and can pose significant risks if not properly secured to the supporting structure. Consequences of unsecured NSEs in the aftermath of earthquakes are significant losses to human life, property, and functionality of buildings. This is because, NSEs are susceptible to sliding, rocking, toppling, overturning, stretching, shortening, and shearing. These movements are compounded by: (a) NSE-structure anchorage conditions, (b) geometry, weight, and friction with the supporting surface and NSE, and (c) NSE types. Recent earthquakes in Turkey and Myanmar also witnessed damage in NSEs, reiterating the criticality. In general, earthquake resistance of NSEs can be improved by 3 broad mitigation approaches recommended in literature. They are: (i) non-engineered (based on judgement) (ii) pre-engineered (based on calculations, experiments, and experience from past earthquakes), and (iii) engineered (based on detailed design recommended in design standards). These approaches typically employ anchor bolts, braces, slack chains, snubbers, flexible connections, and shock absorbers tailored to the specific type and performance requirements of NSE. This paper presents a detailed discussion of commonly adopted methods that help improve earthquake resistance of NSEs and enhance overall safety of buildings in urban areas.

Abstract

Accurate segmentation of medical images is challenging due to unclear lesion boundaries and mask variability. We introduce Segmentation Schödinger Bridge (SSB), the first application of Schödinger Bridge for ambiguous medical image segmentation, modelling joint image-mask dynamics to enhance performance. SSB preserves structural integrity, delineates unclear boundaries without additional guidance, and maintains diversity using a novel loss function. We further propose the Diversity Divergence Index () to quantify inter-rater variability, capturing both diversity and consensus. SSB achieves state-of-the-art performance on LIDC-IDRI, COCA, and RACER (in-house) datasets.

Abstract

Extracting work from a physical system is one of the cornerstones of quantum thermodynamics. The extractable work, as quantified by ergotropy, necessitates a complete description of the quantum system. This is significantly more challenging when the state of the underlying system is unknown, as quantum tomography is extremely inefficient. In this article, we analyze the number of samples of the unknown state required to extract work. With only a single copy of an unknown state, we prove that ergotropy approaches zero in the asymptotic limit, rendering work extraction nearly impossible. In contrast, when multiple copies are available, we quantify the sample complexity required to estimate extractable work, establishing a scaling relationship that balances the desired accuracy with success probability. Our work develops a sample-efficient protocol to assess the utility of unknown states as quantum batteries and opens avenues for estimating thermodynamic quantities using near-term quantum computers.