arXiv subjects

Kahn Rhrissorrakrai

Publications and source records attributed to Kahn Rhrissorrakrai.

9 recordsLinked to original sources

arXiv2026

Hybrid quantum-classical attention for histopathology-based molecular profiling in data-limited cancers

Molecular profiling from routine histopathology could expand access to precision oncology when sequencing is unavailable, tissue is limited, or training cohorts are small. We developed a hybrid quantum-classical strategy that replaces softmax attention in a transformer for histopathology-based gene expression prediction with a quantum-derived doubly stochastic matrix (QDSM). Across 29 cancer cohorts from The Cancer Genome Atlas and an independent pancreatic cancer cohort from the Clinical Proteomic Tumor Analysis Consortium, QDSM attention produced selective gains, with the largest relative improvements in smaller, data-limited cohorts, including adrenocortical carcinoma and uveal melanoma. Rather than improving transcriptome-wide performance uniformly, QDSM redistributed predictive accuracy across genes and pathways, improving biologically relevant targets in some tumor contexts while worsening others. In adrenocortical carcinoma, preferentially improved genes were enriched for adverse overall-survival associations, linking enhanced molecular inference to prognostically relevant biology. In pancreatic cancer transfer experiments, QDSM improved selected metabolic and lineage-associated genes but did not consistently improve performance under cross-cohort shift. Leave-one-cancer-out mixed-effects analysis showed that baseline molecular features predicted part of the gene-level benefit, while residuals identified cancer-specific programs that improved more or less than expected. Separate experiments on IBM quantum processors recovered the doubly stochastic matrix primitive underlying the attention mechanism. These findings position QDSM attention as a context- and target-dependent strategy for image-based molecular profiling and molecular triage when direct testing is unavailable, incomplete, or impractical.

quant-ph↗

arXiv2026

A Quantum Reservoir for Neurodynamical Forecasting

Forecasting neural activity from short recordings remains a fundamental challenge. Reservoir computing may offer an efficient paradigm for temporal prediction, however classical reservoirs typically underperform in small-data regimes. Here we investigate whether quantum reservoir computing (QRC) can help overcome this limitation. Building on recent advances, we introduce a quantum reservoir based on a transverse-field Ising model, combined with heterogeneous quantum measurements and polynomial ridge regression. On a standard benchmark task, results show that the quantum reservoir outperforms a classical counterpart overall, with prediction accuracy strongly dependent on reservoir parameters. We further demonstrate feasibility by running the same task on quantum hardware. To assess performance on biological signals, we evaluate QRC on simulated human electroencephalography (EEG) data with a parallel reservoir architecture. On this challenging task, the tested quantum reservoir did not match the performance of the classical one, but it produced stable, convergent predictions. This is a meaningful first step toward forecasting of biologically realistic neural data using a quantum reservoir. Overall, our findings indicate that although current quantum hardware and parallel reservoir architectures do not yet surpass classical methods on complex neural signals, QRC can be executed on near-term devices and does converge with realistic EEG-like data. This work establishes a practical baseline for future algorithmic and hardware developments aimed at clinical time-series forecasting with quantum systems.

quant-ph↗

arXiv2026

Data complexity signature predicts quantum projected learning benefit for antibiotic resistance

This study presents the first large-scale empirical evaluation of quantum machine learning for predicting antibiotic resistance in clinical urine cultures. Antibiotic resistance is amongst the top threats to humanity, and inappropriate antibiotic use is a main driver of resistance. We developed a Quantum Projective Learning (QPL) approach and executed 60 qubit experiments on IBM Eagle and Heron quantum processing units. While QPL did not consistently outperform classical baselines, potentially reflecting current quantum hardware limitations, it did achieve parity or superiority in specific scenarios, notably for the antibiotic nitrofurantoin and selected data splits, revealing that quantum advantage may be data-dependent. Analysis of data complexity measures uncovered a multivariate signature, which comprised Shannon entropy, Fisher Discriminant Ratio, standard deviation of kurtosis, number of low-variance features, and total correlations. The multivariate model accurately (AUC = 0.88, $p$-value = 0.03) distinguished cases wherein QPL executed on quantum hardware would outperform classical models. This signature suggests that quantum kernels excel in feature spaces with high entropy and structural complexity. These findings point to complexity-driven adaptive model selection as a promising strategy for optimizing hybrid quantum-classical workflows in healthcare. Overall, this investigation marks the first application of quantum machine learning in urology, and in antibiotic resistance prediction. Further, this work highlights conditional quantum utility and introduces a principled approach for leveraging data complexity signatures to guide quantum machine learning deployment in biomedical applications.

q-bio.OT↗

arXiv2025

Quantum Doubly Stochastic Transformers

At the core of the Transformer, the softmax normalizes the attention matrix to be right stochastic. Previous research has shown that this often de-stabilizes training and that enforcing the attention matrix to be doubly stochastic (through Sinkhorn's algorithm) consistently improves performance across different tasks, domains and Transformer flavors. However, Sinkhorn's algorithm is iterative, approximative, non-parametric and thus inflexible w.r.t. the obtained doubly stochastic matrix (DSM). Recently, it has been proven that DSMs can be obtained with a parametric quantum circuit, yielding a novel quantum inductive bias for DSMs with no known classical analogue. Motivated by this, we demonstrate the feasibility of a hybrid classical-quantum doubly stochastic Transformer (QDSFormer) that replaces the softmax in the self-attention layer with a variational quantum circuit. We study the expressive power of the circuit and find that it yields more diverse DSMs that better preserve information than classical operators. Across multiple small-scale object recognition tasks, we find that our QDSFormer consistently surpasses both a standard ViT and other doubly stochastic Transformers. Beyond the Sinkformer, this comparison includes a novel quantum-inspired doubly stochastic Transformer (based on QR decomposition) that can be of independent interest. Our QDSFormer also shows improved training stability and lower performance variation suggesting that it may mitigate the notoriously unstable training of ViTs on small-scale data.

cs.LG↗

arXiv2025

Enhanced Prediction of CAR T-Cell Cytotoxicity with Quantum-Kernel Methods

Chimeric antigen receptor (CAR) T-cells are T-cells engineered to recognize and kill specific tumor cells. Through their extracellular domains, CAR T-cells bind tumor cell antigens which triggers CAR T activation and proliferation. These processes are regulated by co-stimulatory domains present in the intracellular region of the CAR T-cell. Through integrating novel signaling components into the co-stimulatory domains, it is possible to modify CAR T-cell phenotype. Identifying and experimentally testing new CAR constructs based on libraries of co-stimulatory domains is nontrivial given the vast combinatorial space defined by such libraries. This leads to a highly data constrained, poorly explored combinatorial problem, where the experiments undersample all possible combinations. We propose a quantum approach using a Projected Quantum Kernel (PQK) to address this challenge. PQK operates by embedding classical data into a high dimensional Hilbert space and employs a kernel method to measure sample similarity. Using 61 qubits on a gate-based quantum computer, we demonstrate the largest PQK application to date and an enhancement in the classification performance over purely classical machine learning methods for CAR T cytotoxicity prediction. Importantly, we show improved learning for specific signaling domains and domain positions, particularly where there was lower information highlighting the potential for quantum computing in data-constrained problems.

cs.LG↗

arXiv2025

Quantum Ensembling Methods for Healthcare and Life Science

Learning on small data is a challenge frequently encountered in many real-world applications. In this work we study how effective quantum ensemble models are when trained on small data problems in healthcare and life sciences. We constructed multiple types of quantum ensembles for binary classification using up to 26 qubits in simulation and 56 qubits on quantum hardware. Our ensemble designs use minimal trainable parameters but require long-range connections between qubits. We tested these quantum ensembles on synthetic datasets and gene expression data from renal cell carcinoma patients with the task of predicting patient response to immunotherapy. From the performance observed in simulation and initial hardware experiments, we demonstrate how quantum embedding structure affects performance and discuss how to extract informative features and build models that can learn and generalize effectively. We present these exploratory results in order to assist other researchers in the design of effective learning on small data using ensembles. Incorporating quantum computing in these data constrained problems offers hope for a wide range of studies in healthcare and life sciences where biological samples are relatively scarce given the feature space to be explored.

cs.LG↗

arXiv2024

Discovering Multi-omic Biomarkers for Prostate Cancer Severity Using Machine Learning

Prostate cancer is the second most common form of cancer, though most patients have a positive prognosis with many experiencing long-term survival with current treatment options. Yet, each treatment carries varying levels of intensity and side effects, therefore determining the severity of prostate cancer is an important criteria in selecting the most appropriate treatment. The Gleason score is the most common grading system used to judge the severity of prostate cancer, but much of the grading process can be affected by human error or subjectivity. Finding biomarkers for prostate cancer Gleason scores in a quantitative, machine-driven approach could enable pathologists to validate their assessment of a patient cancer sample by examining such biomarkers. In our study, we identified biomarkers from multi-omics data using machine learning, statistical tools, and deep learning to train models against the Gleason score and capture the most important features that could potentially serve as biomarkers for the Gleason score. Through this process, multiple genes, such as COL1A1 and SFRP4, and cell cycle pathways, such as G2M checkpoint, E2F targets, and the PLK1 pathways, were found to be important predictive features for particular Gleason scores. The combination of these analytical methods shows potential for more accurate grading of prostate cancer, and greater understanding of biological processes behind prostate cancer severity that could provide additional therapeutic targets.

q-bio.QM↗

arXiv2024

WGTDA: A Topological Perspective to Biomarker Discovery in Gene Expression Data

Advancing the discovery of prognostic cancer biomarkers is crucial for comprehending disease mechanisms, refining treatment plans, and improving patient outcomes. This study introduces Weighted Gene Topological Data Analysis (WGTDA), an innovative framework utilizing topological principles to identify gene interactions and distinctive biomarker features. WGTDA undergoes evaluation against Weighted Gene Co-expression Network Analysis (WGCNA), underscoring that topology-based biomarkers offer more reliable predictors of survival probability than WGCNA's hub genes. Furthermore, WGTDA identifies gene signatures that are significant to survival probability, irrespective of whether the expression is above or below the median. WGTDA provides a new perspective on biomarker discovery, uncovering intricate gene-to-gene relationships often overlooked by conventional correlation-based analyses, emphasizing the potential advantage of leveraging topological concepts to extract crucial information about gene-gene interactions.

q-bio.QM↗

arXiv2023

Towards quantum-enabled cell-centric therapeutics

In recent years, there has been tremendous progress in the development of quantum computing hardware, algorithms and services leading to the expectation that in the near future quantum computers will be capable of performing simulations for natural science applications, operations research, and machine learning at scales mostly inaccessible to classical computers. Whereas the impact of quantum computing has already started to be recognized in fields such as cryptanalysis, natural science simulations, and optimization among others, very little is known about the full potential of quantum computing simulations and machine learning in the realm of healthcare and life science (HCLS). Herein, we discuss the transformational changes we expect from the use of quantum computation for HCLS research, more specifically in the field of cell-centric therapeutics. Moreover, we identify and elaborate open problems in cell engineering, tissue modeling, perturbation modeling, and bio-topology while discussing candidate quantum algorithms for research on these topics and their potential advantages over classical computational approaches.

quant-ph↗