Quantum Phase Estimation (QPE) is a cornerstone algorithm in quantum computing, with applications ranging from integer factorization to quantum chemistry simulations. However, the resource demands of standard QPE, which require a large number of coherent qubits and deep circuits, pose significant challenges for current Noisy Intermediate Scale Quantum (NISQ) devices. In this work, we introduce the Adaptive Windowed Quantum Phase Estimation (AWQPE) algorithm, a novel method designed to address the limitations of standard QPE. AWQPE utilizes small, independent blocks of m> 1 control qubits to estimate multiple phase bits simultaneously within a "window," thereby significantly reducing the number of coherent qubits required to achieve a desired precision. These independent blocks are amenable to parallelization and, when combined with a robust least-significant-bit (LSB) to most-significant-bit (MSB) ambiguity resolution mechanism, enhance the algorithm's accuracy while mitigating the risk of error propagation. Our numerical simulations demonstrate AWQPE's accuracy and improved robustness relative to standard QPE under representative noise models, showcasing a favorable balance between resource efficiency and computational speed. This makes AWQPE particularly well-suited for near-term quantum platforms.
Drawing on multi-sited ethnographic research on a school-based cadet program in Kerala, India, this article complicates prevailing understandings of religious accommodation. It examines unofficial and tacit sartorial adjustments negotiated by cadets, families, and teachers to enable Muslim girls' participation in the cadet program. These practices demonstrate that religious accommodation is not a unilateral act of a secular state, but a reciprocal, pragmatic process shaped by everyday negotiations in specific socio-political contexts.
In this study, we investigate electromagnetic and Dirac test field perturbations of a charged regular black hole arising from quantum gravity effects, commonly referred to as the Frolov black hole, a regular (nonsingular) black hole solution. We derive the master wave equations for massless electromagnetic and Dirac perturbations and solve them using the standard Wentzel-Kramers-Brillouin (WKB) method along with Pad´e Averaging. From these solutions, we extract the dominant and overtone quasinormal mode (QNM) frequencies along with the associated grey-body factors, highlighting the deviations introduced by quantum gravity corrections compared to the classical case of Reissner–Nordstr¨om black hole. Furthermore, we analyze the Unruh-Verlinde temperature of this spacetime, providing quantitative estimates of how quantumgravity effects influence both quasinormal ringing and particle emission in nonsingular black hole models.
Foodborne pathogens such as Escherichia coli (E. Coli), Salmonella, and Listeria monocytogenes continue to pose a major potential threat to global public health and therefore rapid, accurate, and field-deployable detection methods are still extremely desirable. This review describes cutting-edge examples of advanced biosensing platforms for the strategy of detecting these priority pathogens, focusing on clinical detection and highlighting electrochemical, optical, and microfluidic sensing modalities. This has been enabled by recent advances in functional nanomaterials, molecular recognition elements (including aptamers and nanozymes), and surface engineering strategies rendering sensors much ‘smarter’/improved in terms of sensitivity, specificity, and behaviour towards complex food matrices. However blending these biosensors with artificial intelligence (AI) and Machine Learning (ML) enabled intelligent pattern recognition, real-time analytics, and multiplexing at high-speed, turning traditional detection systems into smart diagnostic devices. We critically review recent case studies in light of biosensor design, signal transduction mechanisms, models of AI, performance validation, and applicability in different food environments. The principal challenges are identified which include matrix interference, instability of biorecognition elements, limitations in scalability, and the need for regulatory standardization. We discuss these with associated mitigation strategies that are technically sound, including ratiometric sensing, microfluidic pre-treatment techniques, explainable AI, and printable electronics. Forward-looking, we discuss biosensors enabled by being self-powered, biosensor hubs with modular pathogen panels, blockchain incorporation, and standardized validation pipelines. This review offers a prospective view toward enabling intelligent, robust, and regulation-ready biosensing platforms for next-generation food safety monitoring through the bridging of technological innovations with practical implementation. Foodborne illnesses caused by E. coli, Salmonella, and Listeria monocytogenes remain a global public health concern, driving the demand for rapid, accurate, and field-deployable detection strategies. This review comprehensively explores advanced biosensing platforms tailored for detecting these priority pathogens, highlighting progress in electrochemical, optical, and microfluidic sensing mechanisms. Integrating functional nanomaterials, molecular recognition elements such as aptamers and nanozymes, and surface engineering techniques has significantly enhanced sensor sensitivity, specificity, and adaptability to complex food matrices. Moreover, the convergence of biosensors with AI and ML has enabled intelligent pattern recognition, real-time analytics, and high-throughput multiplexing. Key challenges, including matrix interference, bioreceptor instability, manufacturing scalability, and regulatory standardization, are discussed.
We present a large-scale experimental study of quantum-computing-based molecular simulation carried out on IQM's Sirius 24-qubit superconducting processor, utilizing up to 16 operational qubits. The work employs Sample-based Quantum Diagonalization (SQD) together with the Local Unitary Cluster Jastrow (LUCJ) ansatz to estimate ground-state energies for a set of benchmark molecules, including H_2, LiH, BeH_2, H_2O, and NH_3. In addition, we introduce a Linear-CNOT variant of the Unitary Coupled-Cluster Singles and Doubles (LCNot-UCCSD) ansatz within the SQD workflow, trading higher circuit depth for reduced classical preprocessing. A comparison between these ansätze is provided, clarifying their respective strengths, limitations, and suitability for near-term quantum hardware. We further explore potential energy landscapes through 1D scans for H_2 and HeH^+ using both STO-3G and 6-31G basis sets, and for LiH and BeH_2 in STO-3G. Extending beyond this, we demonstrate the experimental construction of a full 2D potential energy surface for the water molecule on quantum hardware, mapped over a 32 × 32 grid in bond length and bond angle. To move beyond small benchmark systems, we combine SQD(LUCJ) with Density Matrix Embedding Theory (DMET) to compute active-space energies for a set of ligand-like molecules, as well as the pharmacologically relevant amantadine system. Across all studies, the majority of quantum-computed energies agree with reference FCI results, as well as with DMET-CASCI energies for embedded systems, to within chemical accuracy for the chosen basis sets. These results demonstrate the reliability of sample-based diagonalization approaches and underscore the potential of hybrid embedding strategies for extending quantum simulations to increasingly complex molecular systems, while also highlighting their practicality on current IQM quantum hardware.
