Papers

The study of real-time dynamics of fermions remains one of the last frontiers beyond the reach of classical simulations and is key to our understanding of quantum behavior in chemistry and materials, with implications for quantum technology. Here we introduce a Heisenberg-picture algorithm that propagates observables expressed in a Majorana-string basis using a truncation scheme that preserves Trotter accuracy and aims at maintaining computational efficiency. The framework is exact for quadratic Hamiltonians – remaining restricted to a fixed low-weight sector determined by the physical observable – admits variational initial states, and can be extended to interacting regimes via systematically controlled truncations. We benchmark our approach on one- and two-dimensional Fermi-Hubbard quenches, comparing against tensor network methods (MPS and fPEPS) and recent experimental data. The method achieves high accuracy on timescales comparable to state-of-the-art variational techniques and experiments, demonstrating that controlled Majorana-string truncation is a practical tool for simulating two-dimensional fermionic dynamics.

The question of thermalization in quantum many-body systems has long been studied through the properties of matrix elements of operators corresponding to local observables. More recently, the focus has shifted to the dynamics of operators, which lead to seminal works proposing universal bounds on the rate of operator growth. In this work, we unify these two approaches: we show that exponential operator growth in Hilbert space, as measured by Krylov complexity, is governed by an exponential off-diagonal decay of the operator matrix elements in the system eigenbasis. When this decay is algebraic or slower, the growth rate saturates the universal bound, thereby establishing a microscopic origin of operator growth which is independent of chaos, dimensionality or the presence of many-body interactions.

We theoretically investigate coherent orbital-angular-momentum (OAM) transfer and dispersion-controlled light propagation in a ladder-type Er YAG three-level system. Using the density-matrix formalism and coupled Maxwell-Bloch equations, we derive analytical expressions for the probe and generated beams that explicitly incorporate Er ion concentration. We show that an incident vortex-carrying probe beam transfers its OAM to a generated signal beam through a concentration-dependent sum-frequency nonlinear process, with complete phase and topological-charge preservation. By analyzing conversion efficiency, spatial phase, and intensity distributions, we identify an optimal Er concentration (3 percent) that maximizes vortex-transfer efficiency. Furthermore, the absorption and dispersion spectra of the probe and generated beams reveal the mechanism underpinning the vortex transfer and demonstrate tunable transitions between fast and slow regimes. These results establish Er YAG as a viable solid-state platform for the coherent manipulation of structured light, enabling efficient vortex-beam frequency conversion and dispersion engineering for applications in high-dimensional quantum communication, wavelength-compatible OAM interfaces, and slow-light photonic signal processing.

Federated learning (FL) is a widely used method for training machine learning (ML) models in a scalable way while preserving privacy (i.e., without centralizing raw data). Prior research shows that the risk of exposing sensitive data increases cumulatively as the number of iterations where a client's updates are included in the aggregated model increase. Attackers can launch membership inference attacks (MIA; deciding whether a sample or client participated), property inference attacks (PIA; inferring attributes of a client's data), and model inversion attacks (MI; reconstructing inputs), thereby inferring client-specific attributes and, in some cases, reconstructing inputs. In this paper, we mitigate risk by substantially reducing per client exposure using a quantum computing-inspired quadratic unconstrained binary optimization (QUBO) formulation that selects a small subset of client updates most relevant for each training round. In this work, we focus on two threat vectors: (i) information leakage by clients during training and (ii) adversaries who can query or obtain the global model. We assume a trusted central server and do not model server compromise. This method also assumes that the server has access to a validation/test set with global data distribution. Experiments on the MNIST dataset with 300 clients in 20 rounds showed a 95.2% per-round and 49% cumulative privacy exposure reduction, with 147 clients' updates never being used during training while maintaining in general the full-aggregation accuracy or even better. The method proved to be efficient at lower scale and more complex model as well. A CINIC-10 dataset-based experiment with 30 clients resulted in 82% per-round privacy improvement and 33% cumulative privacy.

We find a process satisfying the axioms of hyper-decoherence which produces standard quantum theory from the theory of quantum boxes (higher-order quantum theory with the non-signalling tensor product). This hyper-decoherence map evades the no-go theorem of Lee and Selby by relaxing constraints on signalling to the past and the uniqueness of purifications. We discuss some natural opposing conclusions: that the existence of this map might be evidence of a genuine hyper-decoherence process producing causal quantum theory from its causally-indefinite higher-order theory; or that it serves as an indication that the axioms of hyper-decoherence might need careful re-consideration, especially regarding the subtle albeit central role that purity plays.

Quantum algorithms offer a compelling new avenue for addressing difficult NP-complete optimization problems, such as the Generalized Assignment Problem (GAP). Given the operational constraints of contemporary Noisy Intermediate-Scale Quantum (NISQ) devices, hybrid quantum-classical approaches, specifically Variational Quantum Algorithms (VQAs) like the Variational Quantum Eigensolver (VQE), promises to be effective approaches to solve real-world optimization problems. This paper proposes an approach, named VQGAP, designed to efficiently solve the GAP by optimizing quantum resources and reducing the required parametrized quantum circuit width with respect to standard VQE. The main idea driving our proposal is to decouple the qubits of ansatz circuits from the binary variables of the General Assignment Problem, by providing encoding/decoding functions transforming the solutions generated by ansatze in the limited quantum space in feasible solutions in the problem variables space, by exploiting the constraints of the problem. Preliminary results, obtained through both noiseless and noisy simulations, indicate that VQGAP exhibits performance and behavior very similar to VQE, while effectively reducing the number of qubits and circuit depth.

Quantum emitters coupled to nanophotonic structures are an excellent platform for controllable single-photon scattering. The tunable light-matter interaction enables the construction of a single-photon switch – a device that can route a single photon from an input port to a selected output port. Such single-photon switching devices can be integrated into reconfigurable photonic circuits to actively control the photon propagation direction in a quantum network. Ideally, a single-photon switch should operate with high speed, efficiency, and fidelity, preserving the state of the input photon in the routing process. This review brings together key input-output methods from quantum optics, theoretical proposals of emitter-based single-photon routing mechanisms, and experimental demonstrations of single-photon switching devices across different physical platforms, including semiconductor quantum dots, neutral atoms, superconducting qubits, and color centers. We highlight the need for reporting the key figures of merit (speed/efficiency/fidelity) in future single-photon switch demonstrations to support further developments in the field.

The travelling salesman problem is a well-known example of computationally-hard combinatorial problem for classical machines. Here, we propose a novel variational quantum algorithm to solve it. The method is based on the preparation of two maximally entangled quantum registers whose correlations are assigned to different paths between pairs of cities. For $N$ cities, this encoding requires $2 \lceil\log_2 N\rceil$ qubits and the solution to the problem is directly found in the correlation matrix of the two registers composing the overall trial state. As a proof-of-concept experiment, we implement this algorithm for generic problems with four cities on a reconfigurable room-temperature silicon photonic circuit with integrated photon-pair sources, used to initialize maximally entangled path-encoded single-photon states.

Gravity-induced entanglement (GIE) experiments have been proposed as a theory-independent test of the quantum nature of gravity, on the strength of several no-go theorems concluding that GIE detection implies the gravitational interaction is either nonlocal or nonclassical. This is in apparent tension with a growing number of papers claiming that theories with a classical gravitational field do predict GIE. This article resolves the tension by reviewing the assumptions of the no-go theorems and highlighting the information-theoretic, rather than spatiotemporal, nature of the locality assumption. This makes violating locality not particularly problematic. Indeed, quantum electrodynamics and perturbative quantum gravity are nonlocal in the sense of the theorems. Thus, simple detection GIE is not sufficient to rule out a classical gravitational field and we will need quantitative comparison of theoretical predictions in the low-energy quantum gravity regime to test whether the gravitational field is quantum or classical.

Superfluorescence, the cooperative burst of spontaneous emission from an ensemble of dipoles, arises when microscopic oscillators spontaneously synchronize their phases. Here we show that this process can be reversed in time within quantum materials. Coherent multidimensional spectroscopy of halide perovskite quantum dots reveals a delayed cooperative absorption burst, the mirror image of superfluorescent emission, driven by transient polaron fields that phase-lock unit-cell dipoles within 100 fs. The effect scales systematically with quantum-dot size and halide composition, reaching near-unity coherence fidelity even at 300 K. A microscopic exciton-polaron model reproduces the buildup and decay of the coherent state, identifying lattice polarons as the mediators of synchronization. These results demonstrate that many-body temporal coherence can self-organize and persist at room temperature, opening routes toward engineered collective optical states and superabsorbing quantum devices.

Fermionic many-body systems provide an unrivaled arena to investigate how interactions drive the emergence of collective quantum behavior, such as macroscopic coherence and superfluidity. Central to these phenomena is the formation of Cooper pairs, correlated states of two fermions that behave as composite bosons and condense below a critical temperature. However, unlike elementary bosons, these pairs retain their internal structure set by underlying fermionic correlations, essential for understanding superfluid properties throughout the so-called Bose-Einstein condensate (BEC) to Bardeen-Cooper-Schrieffer (BCS) crossover – a cornerstone of strongly correlated fermionic matter. Here, we harness a sonic analog of the optical Sagnac effect to disclose the composite nature of fermionic condensates across the BEC-BCS crossover. We realize an in-situ loop interferometer by coherently exciting two counter-propagating long-wavelength phonons of an annular fermionic superfluid with tuneable interparticle interactions. The frequency degeneracy between clock- and anticlock-wise sound modes is lifted upon controllably injecting a quantized supercurrent in the superfluid ring, resulting in a measurable Doppler shift that enables us to probe the elementary quantum of circulation and the angular momentum carried by each particle in the fermionic fluid. Our observations directly reveal that the superflow circulation is quantized in terms of $h/2m$, where $m$ is the mass of the constituents, in striking contrast to bosonic condensates where $h/m$ is the relevant circulation quantum. Further, by operating our interferometer at tunable temperature, we measure the thermal depletion of the superfluid in the unitary Fermi gas, demonstrating phonon interferometry as a powerful technique for probing fundamental properties of strongly-correlated quantum systems.

The study introduces the Adaptive Quantum Ising Agents (AQIA) framework, a Hamiltonian-based methodology that extends programmable quantum matter into an adaptive domain. Each agent operates as a finite transverse-field Ising subsystem, maintaining internal quantum coherence while interacting through state-dependent feedback channels characterised by reduced observables such as spin polarisation, bond correlation, and internal energy. These informational couplings enable the transformation of a static lattice into a feedback-reconfigurable medium. The effective Hamiltonian generated, which remains Hermitian at each iteration, is resolved self-consistently using a mean-field approximation, where the feedback fields are iteratively adjusted to minimise the total energy. Numerical investigations identify three distinct regimes: domain formation near the feedback–fluctuation critical point, glass-like frustration due to competing feedback channels, and modular polarisation sustained by structured interactions. These phenomena occur independently of geometric embedding, illustrating that informational similarity alone can induce coherent organisation. The AQIA framework is adaptable to implementation on superconducting, trapped-ion, or Rydberg platforms, offering a minimalistic model for exploring self-organisation and learning in adaptive programmable quantum matter.

We study the quantum relaxation dynamics for a lattice version of the one-dimensional (1D) $N$-flavor Gross-Neveu (GN) model after a Hamiltonian parameter quench. Allowing for a system-reservoir coupling $\gamma$, we numerically describe the system dynamics through a time-dependent self-consistent Lindblad master equation. For a closed ($\gamma=0$) finite-size system subjected to an interaction parameter quench, the order parameter dynamics exhibits oscillations and revivals. In the thermodynamic limit, our results imply that the order parameter reaches its post-quench stationary value in accordance with the eigenstate thermalization hypothesis (ETH). However, time-dependent finite-momentum correlation matrix elements equilibrate only if $\gamma>0$. Our findings are consistent with the system being described by a pertinent Generalized Gibbs Ensemble (GGE) and, accordingly, highlight subtle yet important aspects of the post-quench relaxation dynamics of quantum many-body systems.

Suspended carbon nanotubes hosting electrostatically defined quantum dots allow for exceptionally strong and tunable electromechanical coupling as well as mechanical modes that can reach the quantum ground state of motion simply by cryogenic cooling. This makes them a unique platform for quantum simulation of electron-phonon coupling. Here, we propose an experimentally realisable setup with two such carbon nanotubes in parallel, each hosting four quantum dots. Our system not only exhibits phonon-mediated electron-electron attraction, but also supports a robust, maximally entangled Bell phase at mesoscopic scales shared across the subsystems. These features highlight its potential as a simulator of strongly correlated quantum systems.

Quantum machine learning (QML) is a promising paradigm for tackling computational problems that challenge classical AI. Yet, the inherent probabilistic behavior of quantum mechanics, device noise in NISQ hardware, and hybrid quantum-classical execution pipelines introduce new risks that prevent reliable deployment of QML in real-world, safety-critical settings. This research offers a broad roadmap for Trustworthy Quantum Machine Learning (TQML), integrating three foundational pillars of reliability: (i) uncertainty quantification for calibrated and risk-aware decision making, (ii) adversarial robustness against classical and quantum-native threat models, and (iii) privacy preservation in distributed and delegated quantum learning scenarios. We formalize quantum-specific trust metrics grounded in quantum information theory, including a variance-based decomposition of predictive uncertainty, trace-distance-bounded robustness, and differential privacy for hybrid learning channels. To demonstrate feasibility on current NISQ devices, we validate a unified trust assessment pipeline on parameterized quantum classifiers, uncovering correlations between uncertainty and prediction risk, an asymmetry in attack vulnerability between classical and quantum state perturbations, and privacy-utility trade-offs driven by shot noise and quantum channel noise. This roadmap seeks to define trustworthiness as a first-class design objective for quantum AI.

Quantum batteries have emerged as promising platforms for exploring energy storage and transfer processes governed by quantum mechanical laws. In this work, we study three models of two-qubit open quantum systems. The first model comprises two central spins immersed in spin baths, and both central spins are collectively considered as quantum batteries. The impact of inter-qubit interactions on the performance of the quantum battery is investigated. In the second model, a two-qubit model interacting with a squeezed thermal bath serves as a collective quantum battery, where the impact of inter-atomic distance and the bath temperature on the battery's performance is explored. Furthermore, a two-qubit model is used, where one qubit is modeled as a battery and the other as a charger. The charger in this model interacts with an anisotropic spin-chain bath, which is conducive to quantum criticality. It is demonstrated that this criticality has a substantial impact on the quantum battery's storage capacity.

Entanglement-based quantum links are the backbone of future quantum internet networks, enabling secure communication between distant cities. Realizing such networks requires addressing multiple practical challenges in long-distance quantum key distribution : time synchronisation, interferometer stabilisation and automation. Here, we report several advances. First, we maintained an operational QKD link continuously for 325hours over 50km between two remotes locations, demonstrating the feasibility of long-duration key generation. We further extended secure key distribution up to a 100km operational link connecting the University of Nice to a ground-based optical station, a setup compatible with future quantum satellite connections. Finally, by employing wavelength demultiplexing to separate photons of entangled pairs, we performed QKD across multiple ITU channels, achieving secure key exchange via the BBM92 protocol and time-energy observables.

Cavity optomagnonic platforms offer a promising route for exploring quantum phenomena, particularly quantum correlations, which are vital resources for modern quantum technologies. Here, we propose a theoretical scheme for achieving nonreciprocal quantum correlations such as entanglement, and quantum discord via Barnett effect in a molecular-optomagnonical system, where a yttrium iron garnet sphere is placed in a microwave cavity that is hosting molecules. We show optimal parameter regimes for achieving nonreciprocal quantum correlations through Barnett effect. The generated entanglements are robust against thermal fluctuations, persisting even at high temperatures. Our scheme suggests a new tool for engineering noise-tolerant quantum correlations, and paves a way toward realizing novel nonreciprocal quantum devices by integrating magnons with molecular ensembles.

We attempt to modify the time-convolutionless master equation (TCL-ME) to be more resistant to breakdown. We remove the standard assumption that a portion of the generator is invertible by instead taking the Moore-Penrose inverse. We rederive the perturbative expansion using Israel and Charnes' result, and test the equation up to sixth and fifth orders on the Jaynes-Cummings and Ising models, respectively. We find that in both cases, the modified equation fails to capture the dynamics of the exact solution compared to the standard TCL due to the terms of the modified equation scaling exponentially with the dimension of the bath, and connect this failure to a loss of convergence of the perturbative expansion.

Accurate estimation of observables in quantum systems is a central challenge in quantum information science, yet practical implementations are fundamentally constrained by the limited number of measurement shots. In this work we explore a variation of the classical shadows protocol in which the measurements are kept local while allowing the resulting classical shadows themselves to be correlated. By constructing locally optimal shadows, we obtain unbiased estimators that are competitive with state-of-the-art methods in terms of measurement overhead, while requiring only single-qubit measurements and allowing for the estimation of any observable in pure post-processing, reducing estimation errors by orders of magnitude over standard classical shadows. We validate our approach through numerical experiments on molecular Hamiltonians with up to 40 qubits consistently observing significant reductions in estimation errors, including for estimations of multiple chemically relevant observables simultaneously.

We propose and study a generalized quantum statistical framework, referred to as alpha statistics, that continuously interpolates between Bose–Einstein and Fermi–Dirac statistics and naturally extends into the hyperbosonic regime for $\alpha < 0$. Inspired by Haldane's exclusion statistics, this formulation introduces a modified occupation weight function that encodes effective statistical interactions via the parameter $\alpha$. Using thermodynamic geometry, we analyze the sign and singular behavior of the thermodynamic curvature as a diagnostic of underlying interactions and phase structures. A crossover temperature $T^{*}$, at which the curvature changes sign, marks the transition between effectively attractive (Bose-like) and repulsive (Fermi-like) statistical regimes. When expressed relative to the Bose–Einstein condensation temperature $T_{c}$, the ratio $T^{*}/T_{c}$ depends universally on $\alpha$. For negative $\alpha$, corresponding to hyperbosonic statistics, we find curvature singularities at specific fugacities, indicating modified condensation phenomena distinct from conventional Bose condensation. These results highlight the geometric and thermodynamic consequences of alpha statistics and establish a link between fractional exclusion principles and curvature-induced interaction signatures in statistical thermodynamics.

We experimentally demonstrate quantum data compression exploiting hidden subgroup symmetries using a photonic quantum processor. Classical databases containing generalized periodicities-symmetries that are in the worst cases inefficient for known classical algorithms to be detect-can efficiently compressed by quantum hidden subgroup algorithms. We implement a variational quantum autoencoder that autonomously learns both the symmetry type (e.g., $\mathbb{Z}_2 \times \mathbb{Z}_2$ vs. $\mathbb{Z}_4$) and the generalized period from structured data. The system uses single photons encoded in path, polarization, and time-bin degrees of freedom, with electronically controlled waveplates enabling tunable quantum gates. Training via gradient descent successfully identifies the hidden symmetry structure, achieving compression by eliminating redundant database entries. We demonstrate two circuit ansatzes: a parametrized generalized Fourier transform and a less-restricted architecture for Simon's symmetry. Both converge successfully, with the cost function approaching zero as training proceeds. These results provide experimental proof-of-principle that photonic quantum computers can compress classical databases by learning symmetries inaccessible to known efficient classical methods, opening pathways for quantum-enhanced information processing.

Security in machine learning is fragile when data are exfiltrated or perturbed, yet existing frameworks rarely connect the definition and analysis of the security to learnability. In this work, we develop a theory of secure learning grounded in the probably-approximately-correct (PAC) viewpoint and develop an operational framework that links data-path behavior to finite-sample budgets. In our formulation, an accuracy-confidence target is evaluated via a run-based sequential test that halts after a prescribed number of consecutive validations, and a closed-form budget bound guarantees the learning success if the data-path channel is admissible; the acceptance must also exceed a primitive random-search baseline. We elevate and complete our secure-learning construction in the context of quantum information – establishing quantum-secure PAC learning: for prepare-and-measure scenarios, the data-path admissibility is set to be threshold fixed by Holevo information, not a learner-tunable tolerance. Thus, a certified information advantage for the learner directly becomes the learning security – an effect with no classical analogue. The channel-determined confidence follows naturally and basis sifting is incorporated for practical deployments. This is the first complete framework that simultaneously embeds a security notion and an operational sample-budget law within the PAC learning and anchors the security in quantum information. The resulting blueprint points toward standardized guarantees for the learning security, with clear avenues for PAC-Bayes extensions and for integration with advanced quantum machine learning front ends.

We propose and analyze a hybrid quantum repeater architecture that combines two leading hardware platforms: quantum dots (QDs) as bright, deterministic sources of entangled photon pairs and group-IV-vacancy centers in diamond as efficient, heralded quantum memories. This combination leverages high-rate entanglement generation together with long-lived storage, enabling scalable entanglement distribution over long distances. A key challenge is the large bandwidth mismatch between QD photons and the narrow optical transitions of the memories. We combine a comprehensive model of the spin-photon interface, including full spin-photon coupling dynamics, and explore mitigation strategies such as frequency filtering and optimized magnetic-field orientation. Our results show that with optimized designs, photon-to-memory transfer can be achieved with high efficiency and fidelity, supporting the feasibility of such hybrid systems. Finally, we analyze a full repeater chain using experimentally achievable parameters and find that a network with thousands of memories across several repeater nodes could achieve a secret-key rate of 500 bit/s over 1,000 km, demonstrating the strong potential of this approach for next-generation quantum networks.

Molecular conformation in n-alk-1-ynes (CnA) is conventionally simplified to an all-planar structure. We report a comprehensive quantum chemical analysis revealing two near-isoenergetic rotamers at the acetylenic terminus: planar (C$_s$) and skewed (C$_1$). The high, symmetric rotational energy barrier ($\approx 150$\,meV) arises from unique steric relief near the $\mathrm{sp}$ center coupled with electronic stabilization of C$_1$. This creates a unique kinetic profile: a Quantum “Trigger Finger” ($\alpha$ rotation) that enforces an $\approx 50\%:\,50\%$ $\mathrm{C}_s/\mathrm{C}_1$ ensemble, sharply contrasting with the thermodynamically biased “Stiff Joint” ($\delta$ rotation) of the alkyl chain. This structural degeneracy necessitates ensemble averaging for spectroscopic data interpretation, while the slow interconversion permits kinetic trapping and intentional conformer enrichment during synthesis and molecular junction fabrication. Our work redefines the alkyne anchor, providing a blueprint for accurate interpretation of spectroscopic data and achieving conformational control in molecular electronics.

Among the variety of quantum emitters in hexagonal boron nitride (hBN), blue-emitting color centers, or B centers, have gathered a particular interest owing to their excellent quantum optical properties. Moreover, the fact that they can be locally activated by an electron beam makes them suitable for top-down integration in photonic devices. However, in the absence of a real-time monitoring technique sensitive to individual emitters, the activation process is stochastic in the number of emitters, and its mechanism is under debate. Here, we implement an in-situ cathodoluminescence monitoring setup capable of detecting individual quantum emitters in the blue and ultraviolet (UV) range. We demonstrate that the activation of individual B centers is spatially and temporally correlated with the deactivation of individual UV centers emitting at 4.1 eV, which are ubiquitous in hBN. We then make use of the ability to detect individual B center activation events to demonstrate the controlled creation of an array with only one emitter per irradiation site. Additionally, we demonstrate a symmetric technique for heralded selective deactivation of individual emitters. Our results provide insights into the microscopic structure and activation mechanism of B centers, as well as versatile techniques for their deterministic integration.

This paper focuses on quantum algorithms for three key matrix operations: Hadamard (Schur) product, Kronecker (tensor) product, and elementary column transformations each. By designing specific unitary transformations and auxiliary quantum measurement, efficient quantum schemes with circuit diagrams are proposed. Their computational depths are: O(1) for Kronecker product; O(max(m,n)) for Hadamard product (linked to matrix dimensions); and O(m) for elementary column transformations of (2^n X 2^m) matrices (dependent only on column count).Notably, compared to traditional column transformation via matrix transposition and row transformations, this scheme reduces computation steps and quantum gate usage, lowering quantum computing energy costs.

We solve Dicke superradiance with two or more competing collective decay channels of tunable rates using a symbolic quantum-trajectory construction. The method yields closed time-domain populations and observables as finite combinations of exponential terms for arbitrary numbers of emitters and arbitrary decay rates. For two channels, the behavior of the stationary ground-state distribution resembles a first-order phase transition at the point where the channel-rate ratio is equal to unity. For balanced $d$-channel decay, we obtain scaling laws for the superradiant peak time and intensity. These results unify and extend single-channel Dicke dynamics to multilevel emitters and provide a compact tool for cavity and waveguide experiments, where permutation-symmetric reservoirs engineer multiple collective decay paths.

Nitrogen vacancy centers in diamond are among the leading solid state quantum platforms, offering exceptional spatial resolution and sensitivity for applications such as magnetic field sensing, thermometry, and bioimaging. However, in high background environments,such as those encountered in in vitro diagnostics, the performance of NV based sensors can be compromised by strong background fluorescence, particularly from substrates such as nitrocellulose. In this work, we analytically and experimentally investigate the use of pulsed laser excitation combined with time gating techniques to suppress background fluorescence and enhance the signal to noise ratio in NV based quantum sensing, with an emphasis on spin enhanced biosensing. Through experimental studies using mixed ensembles of silicon vacancy and NV centers in bulk diamond, as well as fluorescent nanodiamonds on NC substrates, we demonstrate significant improvements in NV spin resonance visibility, demonstrated by an increase of the SNR by up to 4x, and a resulting measurement time reduction by 16x. The presented technique and results here can help significantly increase the readout efficiency and speed in future applications of NV centers in high background environments, such as in IVD, where the NV centers are used as a fluorescent label for biomolecules.

This paper presents a novel framework for enhancing the quantum resistance of NTRUEncrypt using Markov Chain Monte Carlo (MCMC) methods. We establish formal bounds on sampling efficiency and provide security reductions to lattice problems, bridging theoretical guarantees with practical implementations. Key contributions include: a new methodology for exploring private key vulnerabilities while maintaining quantum resistance, provable mixing time bounds for high-dimensional lattices, and concrete metrics linking MCMC parameters to lattice hardness assumptions. Numerical experiments validate our approach, demonstrating improved security guarantees and computational efficiency. These findings advance the theoretical understanding and practical adoption of NTRU- Encrypt in the post-quantum era.