A lung cancer record can contain very different kinds of data. A tissue slide shows cells and tissue shape. A blood test gives numbers such as white blood cell count, haemoglobin, albumin, glucose, and creatinine. A urine test gives numbers such as urine albumin and urine creatinine. Many computer models use only one of these data types. We propose Q-MedDict, a quantum-inspired dictionary model for image and laboratory data. The model stores information as key–value pairs. The key is a bit string. The value is an amplitude. Gate-like rules move or mix the amplitudes. The method follows the idea of dictionary-based quantum image encoding, but it adds a second branch for blood and urine data. The image branch was tested on two public lung adenocarcinoma H&E images. The laboratory branch used public NHANES release statistics for blood and urine variables. We did not pair CMB-LCA patients with NHANES people because they are different cohorts. The two branches were compared by the same encoding measures instead. With only 25% of block coefficients, the two lung images kept 99.80% and 99.35% of transform energy. Their SSIM values were 0.919 and 0.910. A 50% dictionary state for blood kept 98.64% of transform energy with cosine similarity 0.993. A 50% urine state kept 98.53% with cosine similarity 0.993. These results show one common pattern: image, blood, and urine information can be placed in the same dictionary form and reduced while most of the encoded signal stays present. The result is a small proof-of concept. It is not a lung cancer diagnostic model. A matched cohort with slides and laboratory tests from the same people is needed for clinical prediction.
A lung cancer record can contain very different kinds of data. A tissue slide shows cells and tissue shape. A blood test gives numbers such as white blood cell count, haemoglobin, albumin, glucose, and creatinine. A urine test gives numbers such as urine albumin and urine creatinine. Many computer models use only one of these data types. We propose Q-MedDict, a quantum-inspired dictionary model for image and laboratory data. The model stores information as key–value pairs. The key is a bit string. The value is an amplitude. Gate-like rules move or mix the amplitudes. The method follows the idea of dictionary-based quantum image encoding, but it adds a second branch for blood and urine data. The image branch was tested on two public lung adenocarcinoma H&E images. The laboratory branch used public NHANES release statistics for blood and urine variables. We did not pair CMB-LCA patients with NHANES people because they are different cohorts. The two branches were compared by the same encoding measures instead. With only 25% of block coefficients, the two lung images kept 99.80% and 99.35% of transform energy. Their SSIM values were 0.919 and 0.910. A 50% dictionary state for blood kept 98.64% of transform energy with cosine similarity 0.993. A 50% urine state kept 98.53% with cosine similarity 0.993. These results show one common pattern: image, blood, and urine information can be placed in the same dictionary form and reduced while most of the encoded signal stays present. The result is a small proof-of concept. It is not a lung cancer diagnostic model. A matched cohort with slides and laboratory tests from the same people is needed for clinical prediction.
Medical image encoding has several challenges. File sizes are large. Processing is slow. Fine details like edges, textures, and soft tissue patterns can be lost. Traditional methods use matrix operations that are powerful but often hard to scale and interpret. These limitations highlight opportunities to improve image encoding with better interpretability. To explore new opportunities for scalable image encoding, we introduce a quantum inspired model built on a dictionary structure. In this model, quantum states are stored as key value pairs for ensuring interpretability and scalability. We do not directly implement quantum gates. we provide dictionary based alternatives that emulate gate behavior using key value logic. We simulate the effect of quantum gates on these structures. For example, a Hadamard like rule may help capture sharp or high contrast areas. Phase like rules (such asSandT) may respond to smooth or soft changes. Control rules, likeCXandCCX, can connect different parts of the image using logic. It may reduce the number of steps needed for image operations. This makes it useful for small quantum image datasets. It also makes the steps more explainable. The process is also traceable through the dictionary logic. We tested this method on grayscale MRI image patches. It performs well in tasks such as image compression. It also supports pattern recognition and secure image sharing. These results suggest its suitability for medical image applications. The model balances technical performance and explainability. This makes it helpful for developers building quantum tools. It also supports researchers and students in medical image analysis.
Diagnosis of cancer is one of the hardest problems faced in modern medicine and involves integrating different data sources such as medical images, genomic profiles and clinical records. Traditional machine learning methods have difficulty handling the high-dimensional and complex correlation properties of multimodal medical data. In view of this, we propose a new Quantum-Enhanced Multimodal Fusion Network (QEMFN) framework to break through traditional image–text matching based on quantum computing principles for CT imaging with genomic sequencing data and EHR information. Our approach utilizes variational quantum circuits for feature encoding, quantum kernel methods for crossmodal attention, and hybrid quantum–classical architectures for final classification. We realize the framework using Google Cirq quantum computing library and validate it on publicly available datasets including TCIA (The Cancer Imaging Archive), TCGA (The Cancer Genome Atlas), and MIMIC-III clinical database. The matched multimodal cohort comprises 847 lung cancer patients, 623 colorectal cancer patients, and 401 liver cancer patients with complete imaging, genomic, and clinical records, assembled via de-identified patient ID linkage across the three archives. The experiment takes steps toward the realization of quantum-enhanced diagnostic systems and offers a path for subsequent experimental confirmation. We theoretically analyze the potential quantum advantage, present detailed implementation details using Cirq, and describe a roadmap to clinical translation for quantum-enhanced diagnostic tools.
Quantum machine learning (QML) presents novel opportunities for resolving, accelerating, and refining computational biology challenges. In biomedical research and personalized medicine, multi-omics integration offers a comprehensive understanding of complex biological systems, bridging the gap between basic research and clinical applications. Recent advancements in quantum computing could enhance multi-omics integration by leveraging unique quantum principles, potentially revealing intricate patterns in multi-omics datasets and offering new insights into the molecular landscape of lung cancer. This study focuses on the integration, feature selection, diagnostic classification, and biomarker identification of lung cancer types-LUAD primary tumor and solid tissue normal-using QML. Given the heterogeneity and high dimensionality of the data, we aim to assess the performance of QML in feature selection, reduction, encoding, and diagnostic classification in integrated multi-omics cancer datasets. We demonstrate the efficacy of Quantum Neural Networks (QNNs) for classifying multi-omics lung cancer data from The Cancer Genome Atlas, comparing our framework with 16 and 64 dimensions of feature using QML. Our results show that the Multi-omic Quantum Machine Learning (MQML) framework achieves superior classification performance with smaller training datasets, as determined by significant and non-significant p-values. This study provides empirical evidence for the potential application of unconventional computing approaches in biomed-ical sciences, particularly in oncology research, including cancer subtype diagnostic, survival, and stage classification. Key results include the identification of sixteen genes and thirty-two genes with the hybrid quantum-classical neural net-work model, with notable genes such as MIR632, CYP19A1, RP11-108K3.1, C14orf39, THUMPD1, ACSM3, GSC, MIR92a-2, DYRK1B, DNAJA1, IRX1, FMR1, POU2F2, MIR9-2, ZNF274, MIR196a-1, MIR3653, MIR33b, MIR301a, MIR182, MIR520a, PYGO2, EMX2OS, EMX2, RP11-550F7.1, RPS11, MIR20a, EIF4G2, IGLV1-47, RP11-180M15.7, SCYL3, AC107081.5, C2orf47, FST, MIR7-1, SLC22A18, CLDN5, and AC007879.7 identified as having significant and non-significant p-values asso-ciated with LUAD patient predictions.
The combination of quantum computing with machine learning which forms quantum machine learning (QML) presents promising advances for healthcare purposes with specific applications in predicting cancer outcomes. The research investigates how QML can work together with the quantitative imaging analysis techniques in radiomics to improve prediction methods for lung cancer results. The proposed quantum neural network (QNN) model attempts to predict lung cancer patient 5-year survival status of lung cancer patient by radiomic features extracted from CT scan images. Quantum artificial intelligence showed promise in lung cancer prognosis because its evaluation included a balanced accuracy score of 0.71 and ROC-AUC performance metric of 0.71. The current approach has three major challenges due to data biases, restricted quantum hardware capabilities and insufficient evidence to demonstrate the quantum speedup benefits. In this study QML's clinical potential was noted in this research with operational and developmental pathways moving forward.
This record contains the reproducibility materials for the Quantum Biomarker Discovery in Multi-Omics (QuBID-Omics) study of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) classification. It includes scripts for training-only RNA and DNA biomarker discovery, RNA–DNA feature integration, quantum neural network (QNN) and classical-baseline modeling, IBM Fez quantum-processing-unit inference, and CPTAC external validation. The release also provides ordered gene–CpG feature lists, CPTAC feature mappings, and prepared TCGA train/validation/test and CPTAC external-validation matrices for Samples 1–3. See the README for the workflow, data organization, label coding, and execution requirements.
Quantum machine learning offers a promising new paradigm for computational biology by leveraging quantum mechanical principles to enhance cancer classification, biomarker discovery, and bioinformatics diagnostics. In this study, we apply QML to identify subtype specific biomarkers for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two predominant forms of non-small cell lung cancer. Our methodology involves a two-phase process: in Phase 1, differential expression analysis and methylation analysis between tumor and normal samples allows us to identify LUAD-specific and LUSC-specific genes, revealing potential prognostic biomarkers for cancer subtypes. Phase 2 focuses on developing a quantum classifier capable of distinguishing between LUAD and LUSC tumors, as well as between tumor and normal samples. This classifier not only enhances diagnostic precision but also demonstrates the quantum advantage in processing large-scale multiomic datasets. Our results consistently demonstrated that Sample3, representing the combined gene set, achieved the highest overall predictive performance in all metrics. These results demonstrate that QML provides an effective and scalable approach for biomarker discovery and subtype specific cancer classification. GO enrichment analysis highlighted the significant involvement of genes in synaptic signaling, ion channel regulation, and neuronal development. In the quantum phase, KEGG analysis further identified enrichment in cancer-associated pathways, including neurotrophin, MAPK, Ras, and PI3KAkt signaling, with key genes such as NGFR, NTRK2, and NTF3 suggesting a central role in neurotrophinmediated oncogenic processes. Our findings highlight the growing potential of quantum computing to advance precision oncology and next-generation biomedical analytics.