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PubMed2025

Generating three-dimensional genome structures with a variational quantum algorithm.

Chromosome conformation capture experiments have revealed the underlying spatial interactions that govern three-dimensional (3D) genome organization and topology. Detecting 3D contacts between genomic loci considerably enhances our understanding of fundamental regulatory processes. Modeling 3D structures from experimental contact matrices can further contextualize the relationship between 3D genome organization and regulation. While classical algorithms have been successful in reconstructing genomic conformations, we investigate the prospect of quantum computation to aid in modeling the conformational space. In this context, we propose a novel variational quantum algorithm (VQA) to model the distribution of 3D genomic structures from experimental contact data. Through rigorous evaluations, we demonstrate the capability of our algorithm to sample ensembles of viable 3D conformations that agree well with experimental and simulated contact data. Furthermore, we extend our methodology to model the conformational space of a single cell or a population of cells. In the advent of sufficient quantum utility, the insights gained from this study can serve as a foundation for investigating high-resolution, large-scale ensembles of genomic conformations through generative VQAs.

Algorithms↗
PubMed2025

Q RadFusion: Hybrid Quantum Classical Radiogenomic Framework for Breast Cancer Diagnosis.

BACKGROUND AND PURPOSE: Breast cancer remains the most common cancer in women worldwide, with early and accurate diagnosis critical for patient survival. Radiogenomics integrates imaging phenotypes with genomic profiles, offering a pathway to precision diagnostics. However, existing classical machine learning models often struggle with the high dimensionality and heterogeneity of multimodal data, leading to issues in calibration and reproducibility. This study presents Q RadFusion, a hybrid quantum-classical framework designed to enhance breast cancer diagnosis by fusing mammography and genomics data. METHODS: Q RadFusion was implemented on two publicly available datasets: CBIS-DDSM (2,600 curated mammography cases, TCIA) and TCGA-BRCA (1,000 genomic profiles, GDC). Imaging preprocessing included bias-field correction, segmentation, and harmonization, while genomic data underwent normalization and imputation. Feature selection was performed using the Quantum Approximate Optimization Algorithm (QAOA), and features were mapped into a quantum Hilbert space using Variational Quantum Circuits (VQC). For multimodal fusion, ResNet encoded mammography features, and a Transformer encoded genomic features. Patient-level and site-held-out splits were used for evaluation. RESULTS: Q RadFusion achieved an AUC of 0.96 and accuracy of 94%, outperforming baselines including CNN-LSTM, ResNet + XGBoost, and multimodal Transformers. Ablation studies confirmed the contribution of quantum components, with optimal performance observed at circuit depth, qubits, and QAOA layers. The model also demonstrated improved calibration and ~ 80% fewer parameters compared to deep fusion networks. CONCLUSION: Q RadFusion demonstrates that hybrid quantum-classical radiogenomic integration can deliver accurate, reproducible, and clinically meaningful diagnostic support for breast cancer, with strong potential for future clinical translation.

Breast Cancer↗
PubMed1991

Long-term enhancement of CA1 synaptic transmission is due to increased quantal size, not quantal content.

Quantal components of Schaffer collateral synaptic transmission recorded intracellularly from CA1 pyramidal cells were examined using 2 methods: simultaneous recordings of CA3-CA1 cell-pairs, and minimal electrical stimulation in stratum radiatum. Quantal parameters estimated by the method of failures and by a computer algorithm that optimized parameter estimates using deconvolution of background noise were highly correlated. EPSP-amplitude histograms of CA3-CA1 cell pairs (N = 10) and minimal electrical stimulation (N = 33) could be adequately described either by Poisson or binomial statistics, or by both, and exhibited similar estimates of unit quantal size (q) and mean quantal content (m). Paired-pulse stimulation with 50 msec between stimuli resulted in an expected facilitation in the EPSP amplitude and increase in m during the second response, as estimated by noise deconvolution, by the decrease in apparent failures, and by a decrease in the coefficient of variation of the EPSP. Tetanization of the Schaffer collaterals that induced long-term enhancement (LTE/LTP) of the population response was associated with an average increase in q for minimal-stimulation responses, with no significant change in any estimate of m. Taken together, these data indicate that, under the present experimental conditions, LTE is expressed as an increase in quantal size, rather than an increase in the number of quanta released per presynaptic impulse. Although this is not definitive evidence for a postsynaptic mechanism, these findings do further restrict the classes of possible presynaptic mechanisms that may be proposed to account for LTE expression.

Action Potentials↗