The rapid explosion of medical image data poses a significant challenge to reliable disease Classification with high-dimensional feature spaces and intricate inter-feature correlations. In this paper, a new quantum annealing-inspired framework for optimal feature subset selection, specifically tailored to improve medical image Classification. In contrast to traditional filter, wrapper, or embedded techniques that tend to ignore redundancy and feature interactions, our solution poses feature selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solves it effectively using D-Wave quantum annealer hardware. The presented approach makes three primary contributions: (i) a QUBO formulation that captures feature importance, redundancy suppression, and interaction synergy simultaneously, (ii) a hybrid quantum-classical pipeline with embedding, decomposition, and chain strength optimization for efficient execution on existing quantum hardware, and (iii) a comprehensive evaluation on three heterogeneous medical imaging datasets—diabetic retinopathy, breast cancer histopathology, and chest X-ray pneumonia. Experimental findings exhibit uniform improvements in Classification performance (up to 12.1%), drastic feature reduction (65–80%), and much reduced computational time over six state-of-the-art classical approaches. With the exposure of clinically relevant features and robust statistical significance across benchmarks, our approach places quantum annealing as an influential and scalable tool for future medical AI applications.
The rapid explosion of medical image data poses a significant challenge to reliable disease Classification with high-dimensional feature spaces and intricate inter-feature correlations. In this paper, a new quantum annealing-inspired framework for optimal feature subset selection, specifically tailored to improve medical image Classification. In contrast to traditional filter, wrapper, or embedded techniques that tend to ignore redundancy and feature interactions, our solution poses feature selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solves it effectively using D-Wave quantum annealer hardware. The presented approach makes three primary contributions: (i) a QUBO formulation that captures feature importance, redundancy suppression, and interaction synergy simultaneously, (ii) a hybrid quantum-classical pipeline with embedding, decomposition, and chain strength optimization for efficient execution on existing quantum hardware, and (iii) a comprehensive evaluation on three heterogeneous medical imaging datasets—diabetic retinopathy, breast cancer histopathology, and chest X-ray pneumonia. Experimental findings exhibit uniform improvements in Classification performance (up to 12.1%), drastic feature reduction (65–80%), and much reduced computational time over six state-of-the-art classical approaches. With the exposure of clinically relevant features and robust statistical significance across benchmarks, our approach places quantum annealing as an influential and scalable tool for future medical AI applications.
High-dimensional medical datasets pose a persistent challenge for artificial intelligence because traditional classification algorithms often incur escalating computational costs and reduced predictive accuracy. As healthcare systems generate increasingly complex clinical records, imaging outputs, and genomic profiles, scalable analytic methods that balance precision and efficiency are critical. This study proposes a Quantum-Inspired Optimization (QIO) framework for efficient and accurate classification of high-dimensional healthcare data. Leveraging the exploratory power of variational quantum algorithms, specifically techniques analogous to the Quantum Approximate Optimization Algorithm, the framework integrates quantum-style search strategies with classical computation to achieve global optimization and numerical stability. Publicly available medical datasets with hundreds of features were used to evaluate the approach. Classification models were trained and tested across varying feature dimensionalities, and performance was assessed using accuracy, runtime, and scalability metrics. Empirical results demonstrate that QIO achieves up to 95.4% classification accuracy and reduces computational time by 40% compared with state-of-the-art classical baselines. The method demonstrates stable convergence and clear decision boundaries even as feature dimensionality grows, highlighting its resilience to the curse of dimensionality. These results indicate that QIO can enable fast and reliable healthcare analytics in data-rich clinical environments. Future research may examine domain-specific adaptations, real-time deployment, and integration with emerging quantum hardware to enhance the impact of quantum-inspired artificial intelligence further.
This paper explores the applications of quantum annealing (QA) and classical simulated annealing (SA) to a suite of combinatorial optimization problems in machine learning, namely feature selection, instance selection, and clustering. We formulate each task as a Quadratic Unconstrained Binary Optimization (QUBO) problem and implement both quantum and classical solvers to compare their effectiveness. For feature selection, we propose several QUBO configurations that balance feature importance and redundancy, showing that quantum annealing (QA) produces solutions that are computationally more efficient. In instance selection, we propose a few novel heuristics for instance-level importance measures that extend existing methods. For clustering, we embed a classical-to-quantum pipeline, using classical clustering followed by QUBO-based medoid refinement, and demonstrate consistent improvements in cluster compactness and retrieval metrics. Our results suggest that QA can be a competitive and efficient tool for discrete machine learning optimization, even within the constraints of current quantum hardware.
We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets. Feature selection is often formulated as a k of n selection problem, where the complexity grows binomially with increasing k and n. Quantum computers, particularly quantum annealers, are well-suited for such problems, which may offer advantages under certain problem formulations. We present a method to solve larger feature selection instances than previously demonstrated on commercial quantum annealers. Our approach combines a linear Ising penalty mechanism with subsampling and thresholding techniques to enhance scalability. The method is tested in a toy problem where feature selection identifies pixel masks used to reconstruct small-scale medical images. We compare our approach against a range of feature selection strategies, including randomized baselines, classical supervised and unsupervised methods, combinatorial optimization via classical and quantum solvers, and learning-based feature representations. The results indicate that quantum annealing-based feature selection is effective for this simplified use case, demonstrating its potential in high-dimensional optimization tasks. However, its applicability to broader, real-world problems remains uncertain, given the current limitations of quantum computing hardware. While learned feature representations such as autoencoders achieve superior reconstruction performance, they do not offer the same level of interpretability or direct control over input feature selection as our approach.
Feature selection is an essential step for reducing complexity of high dimensional data, usually in preparation for developing machine learning models such as computational biomarkers. However, there are limitations associated with feature selection such as for metabolomic data where there are hundreds or even thousands of features per study participant, while the available number of participants in a clinical trials is limited. Classical methods such as exhaustive search require evaluation of all possible feature combinations, making them costly in terms of computation and runtime, or even infeasible, as feature dimensionality increases. In this study, we propose a novel Quadratic Unconstrained Binary Optimization (QUBO) coefficient formulation and pose metabolomic feature selection as a QUBO problem that selects a specified number of features by balancing their relevance against redundancy among the selected variables. To evaluate our proposed QUBO objective function, we conducted a series of experiments using Bias-Field Digitized Counterdiabatic Quantum Optimization (BF-DCQO) and Quantum Approximate Optimization Algorithm (QAOA) on a quantum gate based computer. We also compared the method to several classical methods on three metabolomic datasets associated with Autism Spectrum Disorder (ASD) on a classical computer. Our method reduces runtime compared with exhaustive search and Iterative Tabu Search (ITS) and achieves competitive performance across classifiers compared to classical filter, wrapper, and embedded methods. These results do not claim quantum advantage; rather, they establish hardware feasibility and demonstrate the current capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices.
Quadratic unconstrained binary optimization (QUBO) has emerged as a powerful framework for medical computing problems. Binary decision variables naturally represent clinical choices, making QUBO formulations well-suited for quantum annealing hardware. However, a fundamental scalability challenge limits practical deployment: problem size grows rapidly with input dimensionality, creating computational bottlenecks that restrict applications to simplified scenarios. This paper addresses this challenge through hierarchical problem reduction, as demonstrated in medical image segmentation, where pixel-level QUBO formulations create over 65,000 variables for a 256x256 image, forcing existing approaches to downsample to 42x42 resolution and discard 97% of pixel information. A superpixel-based QUBO framework is proposed using simple linear iterative clustering (SLIC) to group pixels into perceptually meaningful regions, then formulate segmentation as QUBO over a region adjacency graph (RAG) combining min-cut and smoothness objectives. Validation on INbreast mammography breast cancer images demonstrates a 4.2% improvement in segmentation quality (mean IoU 0.76 vs 0.73) with 33 computational speedup (0.67s vs 21.97s) and a 97.3% reduction in problem size (1764 to 48 variables), all achieved while processing full-resolution images rather than downsampled versions. The reduced problem size also fits well within current quantum annealer connectivity limits, removing the embedding overhead that has historically blocked direct deployment of pixel-level QUBO segmentation on quantum hardware.
As the volume of data for classification tasks in machine learning grows, feature selection plays an increasingly crucial role in enhancing the efficiency and effectiveness of these tasks. Existing classical feature selection algorithms often encounter challenges, such as high computational complexity and vulnerability to local optima. Quantum algorithms have been proposed to overcome these limitations. Here, our work proposes a new approach that combines the Quantum Approximate Optimization Algorithm (QAOA) with the classical feature selection algorithm Maximum Relevance-Minimum Redundancy (MRMR). First, we transform the feature selection problem into the Quadratic Unconstrained Binary Optimization (QUBO) problem. Then, we map the QUBO formulation to Hamiltonian with the Ising model. Finally, we employ QAOA with a shallow circuit to search this Hamiltonian's ground state, which matches the specific solution for feature selection. We conducted numerical experiments on eight real-world datasets for classification tasks from diverse domains, including finance, medicine, and natural hazards. Experiment results suggest that QAOA-selected feature subsets size are typically less than half the complete set while achieving comparable or better classification accuracy than classical MRMR, demonstrating its preference for smaller, more efficient feature subsets.