Classical algorithms for lung cancer exist based on immunotherapy but are quite prone to error. Non-small cell is a major type of lung cancer, frequently caused by smoking. A classical algorithm to treat non-small-cell lung cancer is compared to a quantum algorithm created. Using superposition of qubits, Born’s rule, and the general qubit rule, an algorithm simulates the pathway of certain driver genes, by finding the angle required of the qubit to be able to result in the probability needed to simulate the pathway, tested in IBM Quantum Composer.
Classical algorithms for lung cancer exist based on immunotherapy but are quite prone to error. Non-small cell is a major type of lung cancer, frequently caused by smoking. A classical algorithm to treat non-small-cell lung cancer is compared to a quantum algorithm created. Using superposition of qubits, Born’s rule, and the general qubit rule, an algorithm simulates the pathway of certain driver genes, by finding the angle required of the qubit to be able to result in the probability needed to simulate the pathway, tested in IBM Quantum Composer.
The ability to accurately classify disease subtypes is of vital importance, especially in oncology where this capability could have a life saving impact. Here we report a classification between two subtypes of non-small cell lung cancer, namely Adeno- carcinoma vs Squamous cell carcinoma. The data consists of approximately 20,000 gene expression values for each of 104 patients. The data was curated from [1] [2]. We used an amalgamation of classical and and quantum machine learning models to successfully classify these patients. We utilized feature selection methods based on univariate statistics in addition to XGBoost [3]. A novel and proprietary data representation method developed by one of the authors called QCrush was also used as it was designed to incorporate a maximal amount of information under the size constraints of the D-Wave quantum annealing computer. The machine learning was performed by a Quantum Boltzmann Machine. This paper will report our results, the various classical methods, and the quantum machine learning approach we utilized.
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.
Leukemia is the most common cancer for children, impacting around one out of three patients (American Cancer Society). Various treatment pipelines exist to decrease the cancer spread, including intensive induction chemotherapy. Molecular markers have been a point of focus, since their DNA make-up can indicate a presence of leukemia, helping in early diagnosis and treatment of acute myeloid leukemia. Quantum computing, which disobeys the rules of classical physics, is a booming field with versatile applications. In healthcare, quantum computing is being applied to increase computing power and provide more accurate diagnoses of diseases. In this study, molecular markers in acute myeloid leukemia are simulated using core principles of quantum computing, including placing qubits in superposition. By utilizing qubits to represent molecular markers, IBM Quantum Composer is used to create a circuit that accurately reflects the proper genetic frequencies and pathways in the dataset used.
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.
This work explores the potential of PennyLane and variational quantum-classical algorithms (VQCA) to forecast lung cancer using a structured dataset. The VQCA model performs exceptionally well, with flawless training, validation, and test accuracies of 1.0, demonstrating its capacity to identify patterns in the dataset and provide reliable predictions successfully. Contrarily, the accuracy of the quantum neural network (QNN) and classical neural network (NN) models is lower, demonstrating the benefits of utilizing quantum computing methods for enhanced predictive modeling. We provide a complete examination of the data, stressing the better performance of the VQCA model and its promise in correctly predicting lung cancer. The results highlight the importance of quantum-classical algorithms and help us understand the benefits and drawbacks of various strategies for predicting lung cancer. The study highlights the potential applications of quantum computing techniques in advancing the field of healthcare analytics. It shows the capability of the VQCA model to predict lung cancer using a tabular dataset accurately. Further research in this area is needed to explore scalability and practical implementation aspects. In summary, this study showcases the potential of VQCA and PennyLane in predicting lung cancer and underscores the benefits of quantum computing techniques in healthcare analytics.
This study presents an effective analysis of a hybrid transfer learning algorithm in medical image processing, specifically focusing on histopathological cancer detection. The research involves a two-part approach. First, a hybrid model was created, combining classical and quantum units for classifying images into cancerous and non-cancerous cells. Second, the performance of this hybrid model was compared with traditional models, quantum simulators like Pennylane, and IBM's real quantum computers (Lima, Quito, Belem, and Jakarta), considering factors such as the number of qubits, Variational Quantum Classifier (VQC), and number of images. The objective was to aid users in selecting the optimal combination of classical and quantum units for accurate classification. The study found that the hybrid transfer learning model, when used with the Pennylane quantum simulator, yielded slightly higher accuracy than hybrid models implemented with IBM's quantum computers.