arXiv subjects

Sabre Kais

Publications and source records attributed to Sabre Kais.

At least 37 records · Page 2Linked to original sources

arXiv2026

Chiral Discrimination on Gate-Based Quantum Computers

We present a novel approach to chiral discrimination using gate-based quantum processors, addressing a key challenge in adapting conventional control techniques using modern quantum computing. Schemes such as stimulated rapid adiabatic passage (STIRAP) and shortcuts to adiabaticity (STAP) have shown strong potential for enantiomer discrimination; their reliance on analog and continuous-time control makes them incompatible with digital gate-based quantum computing architectures. Here, we adapt these protocols for quantum computers by discretizing their Gaussian-shaped pulses through Trotterization. We simulate the chiral molecule 1,2-propanediol and experimentally validate this gate-based implementation on IBM quantum hardware. Our results demonstrate that this approach is a viable foundation for advancing chiral discrimination protocols, preparing the way for quantum-level manipulation of molecular chirality on accessible quantum architectures.

quant-ph↗

arXiv2025

PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealing

PearSAN is a machine learning-assisted optimization algorithm applicable to inverse design problems with large design spaces, where traditional optimizers struggle. The algorithm leverages the latent space of a generative model for rapid sampling and employs a Pearson correlated surrogate model to predict the figure of merit of the true design metric. As a showcase example, PearSAN is applied to thermophotovoltaic (TPV) metasurface design by matching the working bands between a thermal radiator and a photovoltaic cell. PearSAN can work with any pretrained generative model with a discretized latent space, making it easy to integrate with VQ-VAEs and binary autoencoders. Its novel Pearson correlational loss can be used as both a latent regularization method, similar to batch and layer normalization, and as a surrogate training loss. We compare both to previous energy matching losses, which are shown to enforce poor regularization and performance, even with upgraded affine parameters. PearSAN achieves a state-of-the-art maximum design efficiency of 97%, and is at least an order of magnitude faster than previous methods, with an improved maximum figure-of-merit gain.

cs.LG↗

arXiv2025

Out-of-time-order correlators bridge classical transport and quantum dynamics

The out-of-time-order correlator (OTOC) has emerged as a central tool for quantifying decoherence across wide-ranging physical platforms. Here we demonstrate its direct measurement in a classical ensemble using nuclear magnetic resonance (NMR) with a modulated gradient spin echo (MGSE) sequence and extend the method into a multidimensional correlation to track exchange phenomena. Position is encoded through magnetic field gradients and momentum through the velocity autocorrelation function, enabling experimental access to OTOCs for proton motion confined within the self-similar lattice of the metal-organic framework MOF-808. Here, water confined to specified geometries within the MOF pores gives rise to spatially distinct diffusive eigenmodes with characteristic relative entropies. We demonstrate that periodic radiofrequency (rf) driving combined with gradient modulation yields entropy evolution through the selection of distinct diffusion modes. Frequency-resolved diffusion spectra connect these entropy dynamics to classical heat-exchange laws, revealing how operational features of quantum systems are mirrored in confined, macroscopic spin ensembles.

quant-ph↗

arXiv2025

Strong Local Passivity in Unconventional Scenarios: A New Protocol for Amplified Quantum Energy Teleportation

Quantum energy teleportation (QET) has been proposed to overcome the restrictions of strong local passivity (SLP) and to facilitate energy transfer in quantum systems. Traditionally, QET has only been considered under strict constraints, including the requirements that the initial state be the ground state of an interacting Hamiltonian, that Alice's measurement commute with the interaction terms, and that entanglement be present. These constraints have significantly limited the broader applicability of QET protocols. In this work, we demonstrate that SLP can arise beyond these conventional constraints, establishing the necessity of QET in a wider range of scenarios for local energy extraction. This leads to a more flexible and generalized framework for QET. Furthermore, we introduce the concept of a ``local effective Hamiltonian,'' which eliminates the need for optimization techniques in determining Bob's optimal energy extraction in QET protocols. As an additional advantage, the amount of energy that can be extracted using our new protocol is amplified to be 7.2 times higher than that of the original protocol. These advancements enhance our understanding of QET and extend its broader applications to quantum technologies. To support our findings, we implement the protocol on quantum hardware, confirming its theoretical validity and experimental feasibility.

quant-ph↗

arXiv2025

Superdiffusion resilience in Heisenberg Chains with 2D interactions on a quantum processor

Observing superdiffusive scaling in the spin transport of the integrable 1D Heisenberg model is one of the key discoveries in non-equilibrium quantum many-body physics. Despite this remarkable theoretical development and the subsequent experimental observation of the phenomena in KCuF$_3$, real materials are often imperfect and contain integrability breaking interactions. Understanding the effect of such terms on the superdiffusion is crucial in identifying connections to such materials. Current quantum hardware has already ascertained its utility in studying such non-equilibrium phenomena by simulating the superdiffusion of the 1D Heisenberg model. In this work, we perform a quantum simulation of the superdiffusion breakdown by generalizing the superdiffusive Floquet-type 1D Heisenberg model to a general 2D model. We comprehensively study the effect of different 2D interactions on the superdiffusion breakdown by tuning up their strength from zero, corresponding to the 1D Heisenberg chain, to finite nonzero values. We observe that certain 2D interactions are more resilient against superdiffusion breakdown than others and that the $SU(2)$ preserving 2D interaction has the highest resilience among all the 2D interactions we study. Importantly, this observed resilience has direct implications for sustaining superdiffusive spin transport in two-dimensional lattices. We reason out the relative resilience against the superdiffusion breakdown through an analysis of the scattering coefficients off the 2D interaction in otherwise 1D chains. The relative resilience of different interaction types against superdiffusion breakdown was also captured in quantum hardware with remarkable accuracy, further establishing the current quantum hardware's applicability in simulating interesting non-equilibrium quantum many-body phenomena.

quant-ph↗

arXiv2025

A deep dive into the interplay of structured quantum peaked circuits and infinite temperature correlation functions

Random quantum circuits have been extensively explored for quantum supremacy demonstrations. However, verifying their output distributions remains challenging. Here, we propose the infinite-temperature correlation function (ITCF) as a physically meaningful observable for noisy intermediate-scale quantum (NISQ) devices one that can be extracted using engineered circuits rather than relying on fully random constructions. This is realized by leveraging peaked quantum states whose probability distributions are sharply peaked at specific outcomes due to constructive interference thus offering more efficient verifiability and stronger signal observability. Rather than using Haar-random states, which often yield vanishing signals through destructive interference, we construct purposefully biased quantum states using either Grover-based amplitude amplification or shallow structured circuits. These engineered states amplify contributions from relevant operator subspaces, enabling robust detection of non-zero ITCF values that would otherwise be suppressed under random-state sampling. Our results highlight a problem-specific state preparation framework that mitigates signal loss from random averaging and facilitates the detection of physically meaningful observables in NISQ devices. We also discuss future extensions to multi-qubit observables, scrambling diagnostics, and variational circuit optimization, underscoring the broader potential of Peaked States for quantum simulation and verification.

quant-ph↗

arXiv2025

Multimodal Quantum Vision Transformer for Enzyme Commission Classification from Biochemical Representations

Accurately predicting enzyme functionality remains one of the major challenges in computational biology, particularly for enzymes with limited structural annotations or sequence homology. We present a novel multimodal Quantum Machine Learning (QML) framework that enhances Enzyme Commission (EC) classification by integrating four complementary biochemical modalities: protein sequence embeddings, quantum-derived electronic descriptors, molecular graph structures, and 2D molecular image representations. Quantum Vision Transformer (QVT) backbone equipped with modality-specific encoders and a unified cross-attention fusion module. By integrating graph features and spatial patterns, our method captures key stereoelectronic interactions behind enzyme function. Experimental results demonstrate that our multimodal QVT model achieves a top-1 accuracy of 85.1%, outperforming sequence-only baselines by a substantial margin and achieving better performance results compared to other QML models.

cs.LG↗

arXiv2025

Roadblocks and Opportunities in Quantum Algorithms -- Insights from the National Quantum Initiative Joint Algorithms Workshop, May 20--22, 2024

The National Quantum Initiative Joint Algorithms Workshop brought together researchers across academia, national laboratories, and industry to assess the current landscape of quantum algorithms and discuss roadblocks to progress. The workshop featured discussions on emerging algorithmic techniques, resource constraints in near-term hardware, and opportunities for co-design across software and systems. Presented here are seven topics from the workshop, each highlighting a critical challenge or promising opportunity discussed during the event. Together, they offer a snapshot of the field's evolving priorities and a shared vision for what is needed to advance quantum computational capabilities.

quant-ph↗

arXiv2025

Enhancing Quantum Federated Learning with Fisher Information-Based Optimization

Federated Learning (FL) has become increasingly popular across different sectors, offering a way for clients to work together to train a global model without sharing sensitive data. It involves multiple rounds of communication between the global model and participating clients, which introduces several challenges like high communication costs, heterogeneous client data, prolonged processing times, and increased vulnerability to privacy threats. In recent years, the convergence of federated learning and parameterized quantum circuits has sparked significant research interest, with promising implications for fields such as healthcare and finance. By enabling decentralized training of quantum models, it allows clients or institutions to collaboratively enhance model performance and outcomes while preserving data privacy. Recognizing that Fisher information can quantify the amount of information that a quantum state carries under parameter changes, thereby providing insight into its geometric and statistical properties. We intend to leverage this property to address the aforementioned challenges. In this work, we propose a Quantum Federated Learning (QFL) algorithm that makes use of the Fisher information computed on local client models, with data distributed across heterogeneous partitions. This approach identifies the critical parameters that significantly influence the quantum model's performance, ensuring they are preserved during the aggregation process. Our research assessed the effectiveness and feasibility of QFL by comparing its performance against other variants, and exploring the benefits of incorporating Fisher information in QFL settings. Experimental results on ADNI and MNIST datasets demonstrate the effectiveness of our approach in achieving better performance and robustness against the quantum federated averaging method.

cs.LG↗

arXiv2025

Spectral Bounds on Entropy and Ergotropy via Statistical Effective Temperature in Classical Polarization and Quantum Thermal States

We formulate a unified definition of the statistical effective temperature (SET) for finite-dimensional classical and quantum systems using dimension-dependent indices of purity derived from the eigenvalue spectrum. This spectral approach bypasses the need for Hamiltonians or energy gaps and remains applicable to both quantum density matrices and classical polarization coherency matrices. The SET framework naturally describes the divergence of inverse temperature near pure, non-degenerate states, consistent with the third law. Using entropy-SET diagrams, we explore spectral bounds in two-, three-, and four-level systems, which reveal physically realizable entropy regions, rank-dependent constraints, and cusp-like features. A Hamiltonian-free parametric spectrum ansatz provides a universal reference curve within these bounds. Furthermore, we derive spectral bounds on ergotropy as a function of entropy and SET, which quantify the maximum extractable work under passive constraints and introduce the notion of structured-states, engineered spectral configurations that saturate these bounds. Our analysis shows that SET serves as a thermodynamically meaningful and operationally relevant quantity for bounding entropy and ergotropy in both classical polarization systems and quantum thermal states.

quant-ph↗

arXiv2025

Quantum smell: tunneling mechanisms in olfaction

The mechanism by which odorants are recognized by olfactory receptors remains primarily unresolved. While charge transport is believed to play a significant role, its precise nature is still unclear. Here, we present a novel perspective by exploring the interplay between the intrinsic energy scales of odorant molecules and the gap states that facilitate intermolecular charge transport. We find that odorants act as weak tunneling conductors mainly because of the limited magnitude of electronic coupling. This behavior is further connected to electron-phonon interactions and reorganization energy, suggesting that physically meaningful values for these parameters emerge only in the deep off-resonant tunneling regime. These findings complement the swipe card model of olfaction, in which an odorant needs both the right shape to bind to a receptor and the correct vibrational frequency to trigger signal transduction. Moreover, they reveal that the underlying mechanisms are much more complex than previously assumed.

physics.chem-ph↗

arXiv2025

A Classical Analogue of Entanglement for a Kicked Top

It is widely believed that quantum mechanics cannot exhibit chaos, since unitarity of time evolution ensures that distances between quantum states are preserved. However, a parallel argument can be constructed in classical mechanics that would seem to deny the existence of classical chaos too. The argument works by describing classical states as probability distributions in phase space and showing that the inner product between distributions on phase space is preserved under Liouvillian dynamics. Thus, the more faithful classical analogy of a quantum state is not a single phase space trajectory but is instead a phase space distribution, and chaos in such states must be identified by some statistical signatures instead of exponential separation of nearby states. The search for these signatures is the primary goal in quantum chaos research. However, this perspective also naturally motivates the search for classical analogues of these signatures, to reveal the inner machinery of chaos in quantum systems. One widely recognized signature of chaos in quantum systems is the dynamical generation of entanglement. Chaos in the classical system is correlated with a greater entanglement production in the corresponding quantum system. One of the most well-studied examples of this is the kicked top model. In this paper, we construct a classical analogue of bipartite entanglement in terms of the mutual information between phase space distributions of subsystems and find completely analogous signatures of chaos as those found in entanglement for the kicked top Hamiltonian.

quant-ph↗

arXiv2025

Maximal Entropy Formalism and the Restricted Boltzmann Machine

The connection between the Maximum Entropy (MaxEnt) formalism and Restricted Boltzmann Machines (RBMs) is natural, as both give rise to a Boltzmann-like distribution with constraints enforced by Lagrange multipliers, which corresponds to RBM parameters. We integrate RBMs into quantum state tomography (QST) by using them as probabilistic models to approximate quantum states while satisfying MaxEnt constraints. Additionally, we employ polynomially efficient quantum sampling techniques to enhance RBM training, enabling scalable and high-fidelity quantum state reconstruction. This approach provides a computationally efficient framework for applying RBMs to MaxEnt-based quantum tomography. Furthermore, our method applies to the general and previously unaddressed case of reconstructing arbitrary mixed quantum states from incomplete and potentially non-commuting sets of expectations of observables while still ensuring maximal entropy.

quant-ph↗

arXiv2025

Entropy-Assisted Quality Pattern Identification in Finance

Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge. In this paper, we propose an entropy-assisted framework for identifying high-quality, non-overlapping patterns that exhibit consistent behavior over time. We ground our approach in the premise that historical patterns, when accurately clustered and pruned, can yield substantial predictive power for short-term price movements. To achieve this, we incorporate an entropy-based measure as a proxy for information gain. Patterns that lead to high one-sided movements in historical data, yet retain low local entropy, are more informative in signaling future market direction. Compared to conventional clustering techniques such as K-means and Gaussian Mixture Models (GMM), which often yield biased or unbalanced groupings, our approach emphasizes balance over a forced visual boundary, ensuring that quality patterns are not lost due to over-segmentation. By emphasizing both predictive purity (low local entropy) and historical profitability, our method achieves a balanced representation of Buy and Sell patterns, making it better suited for short-term algorithmic trading strategies.

q-fin.TR↗

arXiv2025

Refined Phase Diagram for a Spin-1 System Exhibiting a Haldane Phase

We provide the phase diagram of a 2-parameter spin-1 chain that has a symmetry-protected topological (SPT) Haldane phase using computational algorithms along with tensor-network tools. We improve previous results, showing the existence of a new phase and new triple points. New striking features are the triple end of the Haldane phase and the complexity of phases bordering the Haldane phase in proximity allowing moving to nearby non-SPT phases via small perturbations. These characteristics make the system, which appears in Rydberg excitons, e.g. in Cu$_2$O, a prime candidate for applications.

cond-mat.str-el↗

arXiv2024

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications

Neural-network quantum states (NQS) offer a versatile and expressive alternative to traditional variational ansätze for simulating physical systems. Energy-based frameworks, like Hopfield networks and Restricted Boltzmann Machines, leverage statistical physics to map quantum states onto an energy landscape, functioning as memory descriptors. Here, we show that such models can be efficiently trained using Monte Carlo techniques enhanced by quantum devices. Our algorithm scales linearly with circuit width and depth, requires constant measurements, avoids mid-circuit measurements, and is polynomial in storage, ensuring optimal efficiency. It applies to both phase and amplitude fields, significantly expanding the trial space compared to prior methods. Quantum-assisted sampling accelerates Markov Chain convergence and improves sample fidelity, offering advantages over classical approaches. We validate our method by accurately learning ground states of local spin models and non-local electronic structure Hamiltonians, even in distorted molecular geometries with strong multi-reference correlations. Benchmark comparisons show robust agreement with traditional methods. This work highlights the potential of combining machine learning protocols with near-term quantum devices for quantum state learning, with promising applications in theoretical chemistry and condensed matter physics.

quant-ph↗

arXiv2024

Multi-Omic and Quantum Machine Learning Integration for Lung Subtypes Classification

Quantum Machine Learning (QML) is a red-hot field that brings novel discoveries and exciting opportunities to resolve, speed up, or refine the analysis of a wide range of computational problems. In the realm of biomedical research and personalized medicine, the significance of multi-omics integration lies in its ability to provide a thorough and holistic comprehension of complex biological systems. This technology links fundamental research to clinical practice. The insights gained from integrated omics data can be translated into clinical tools for diagnosis, prognosis, and treatment planning. The fusion of quantum computing and machine learning holds promise for unraveling complex patterns within multi-omics datasets, providing unprecedented insights into the molecular landscape of lung cancer. Due to the heterogeneity, complexity, and high dimensionality of multi-omic cancer data, characterized by the vast number of features (such as gene expression, micro-RNA, and DNA methylation) relative to the limited number of lung cancer patient samples, our prime motivation for this paper is the integration of multi-omic data, unique feature selection, and diagnostic classification of lung subtypes: lung squamous cell carcinoma (LUSC-I) and lung adenocarcinoma (LUAD-II) using quantum machine learning. We developed a method for finding the best differentiating features between LUAD and LUSC datasets, which has the potential for biomarker discovery.

cs.LG↗

arXiv2024

Extracting and Storing Energy From a Quasi-Vacuum on a Quantum Computer

We explore recent advancements in the understanding and manipulation of vacuum energy in quantum physics, with a focus on the quantum energy teleportation (QET) protocol. Traditional QET protocols extract energy from what we refer to as a ``quasi-vacuum'' state, but the extracted quantum energy is dissipated into classical devices, limiting its practical utility. To address this limitation, we propose an enhanced QET protocol that incorporates an additional qubit, enabling the stored energy to be stored within a quantum register for future use. We experimentally validated this enhanced protocol using IBM superconducting quantum computers, demonstrating its feasibility and potential for future applications in quantum energy manipulation.

quant-ph↗