Machine Learning (ML) algorithms in real-world applications have created an enormous and significant impact in terms of accuracy, efficiency and reliability. However, maintaining an appropriate bias in these algorithms seems to be a rigorous challenge to ensure fair and precise results obtained from decision making models. Various ML algorithms like Fairness Representation Learning, Adversarial Debiasing and Equalized Odds Post-Processing exist to mitigate bias but due to their computational limitations they lack the ability to do so which can have an adverse impact in sensitive sectors like finance and healthcare. Hence quantum algorithms arise with their promising features that can improve model performance with enhanced accuracy and fairness. In this study, we explore quantum approaches like Grover’s search algorithm and analyse the performance using different metrics. A comparative study of classical machine learning algorithms against quantum algorithms is done on adult income dataset. The results display a significant drop in Statistical Parity Difference (SPD) from 0.5173 (Baseline) to 0.4956 (Quantum) which indicates that the model is now less biased and fair predictions are done by the model. In future, hybrid quantum-machine learning techniques will play a key role in mitigation framework and general ML real-world applications.
Machine Learning (ML) algorithms in real-world applications have created an enormous and significant impact in terms of accuracy, efficiency and reliability. However, maintaining an appropriate bias in these algorithms seems to be a rigorous challenge to ensure fair and precise results obtained from decision making models. Various ML algorithms like Fairness Representation Learning, Adversarial Debiasing and Equalized Odds Post-Processing exist to mitigate bias but due to their computational limitations they lack the ability to do so which can have an adverse impact in sensitive sectors like finance and healthcare. Hence quantum algorithms arise with their promising features that can improve model performance with enhanced accuracy and fairness. In this study, we explore quantum approaches like Grover’s search algorithm and analyse the performance using different metrics. A comparative study of classical machine learning algorithms against quantum algorithms is done on adult income dataset. The results display a significant drop in Statistical Parity Difference (SPD) from 0.5173 (Baseline) to 0.4956 (Quantum) which indicates that the model is now less biased and fair predictions are done by the model. In future, hybrid quantum-machine learning techniques will play a key role in mitigation framework and general ML real-world applications.
Abstract: This paper explores the potential of quantum amplitude amplification (QAA) to address algorithmic bias in machine learning datasets. The inherent biases present within large datasets necessitate innovative mitigation strategies, and quantum computing offers a novel pathway to address this challenge. We propose a framework for leveraging quantum algorithms to directly correct subtle biases, potentially surpassing existing post-processing techniques. The core mechanism centers around utilizing quantum superposition to amplify subtle variations in data, effectively 'correcting' the underlying data distribution. The research will outline a theoretical foundation for QAA's application to bias detection and correction within machine learning models, examining its potential impact on fairness and accuracy.
Abstract Due to the beyond-classical capability of quantum computing, quantum machine learning is applied independently or embedded in classical models for decision making, especially in the field of finance. Fairness and other ethical issues are often one of the main concerns in decision making. In this work, we define a formal framework for the fairness verification and analysis of quantum machine learning decision models, where we adopt one of the most popular notions of fairness in the literature based on the intuition—any two similar individuals must be treated similarly and are thus unbiased. We show that quantum noise can improve fairness and develop an algorithm to check whether a (noisy) quantum machine learning model is fair. In particular, this algorithm can find bias kernels of quantum data (encoding individuals) during checking. These bias kernels generate infinitely many bias pairs for investigating the unfairness of the model. Our algorithm is designed based on a highly efficient data structure—Tensor Networks—and implemented on Google’s TensorFlow Quantum. The utility and effectiveness of our algorithm are confirmed by the experimental results, including income prediction and credit scoring on real-world data, for a class of random (noisy) quantum decision models with 27 qubits ( 227 227 -dimensional state space) tripling ( 218 218 times more than) that of the state-of-the-art algorithms for verifying quantum machine learning models.
This paper introduces QAEB, a novel algorithmic approach to mitigate bias in machine learning models. Traditional bias mitigation techniques often involve post-processing, which can be computationally expensive and may not fully address the underlying causes of bias. QAEB leverages quantum phenomena, specifically quantum interference, to proactively shield data points from influencing model decision-making. We detail the core mechanism, the underlying theoretical framework, and preliminary experimental results demonstrating the algorithm's effectiveness in reducing bias in a specific machine learning scenario. The paper concludes with a discussion of future research directions and potential applications.
Quantum Machine Learning represents a paradigm shift at the intersection of Quantum Computing and Machine Learning, leveraging quantum phenomena such as superposition, entanglement, and quantum parallelism to address the limitations of classical approaches in processing high-dimensional and large-scale datasets. This survey provides a comprehensive analysis of Quantum Machine Learning, detailing foundational concepts, algorithmic advancements, and their applications across domains such as healthcare, finance, and quantum chemistry. Key techniques, including Quantum Support Vector Machine, Quantum Neural Network, Quantum Decision Trees, and hybrid quantum-classical models, are explored with a focus on their theoretical foundations, computational benefits, and comparative performance against classical counterparts. While the potential for exponential speedups and enhanced efficiency is evident, the field faces significant challenges, including hardware constraints, noise, and limited qubit coherence in the current era of Noisy Intermediate-Scale Quantum devices. Emerging solutions, such as error mitigation techniques, hybrid frameworks, and advancements in quantum hardware, are discussed as critical enablers for scalable and fault-tolerant Quantum Machine Learning systems. By synthesizing state-of-the-art developments and identifying research gaps, this survey aims to provide a foundational resource for advancing Quantum Machine Learning toward practical, real-world applications in tackling computationally intensive problems.
Abstract - The exponential growth of large-scale data has created significant challenges in storage, processing, and optimization that classical computing systems struggle to address efficiently. Quantum Machine Learning (QML) has emerged as a promising paradigm that integrates quantum computing concepts with machine learning techniques to tackle complex big data optimization problems. By utilizing quantum features such as superposition, entanglement, and parallelism, QML algorithms can accelerate tasks like clustering, classification, and dimensionality reduction, which are traditionally computationally demanding. Recent advancements indicate strong potential for quantum-inspired optimization in fields including cybersecurity, financial portfolio management, Industry 4.0, and healthcare analytics. This paper provides a comprehensive survey of QML algorithms designed for big data optimization, examining their advantages, limitations, and application domains. Additionally, it discusses the performance trade-offs associated with hybrid quantum–classical frameworks and highlights future research directions aimed at creating secure, scalable, and resource-efficient quantum solutions for large-scale, data-driven decision-making. As data volumes continue to expand at extremely rapid rates, processing big data with traditional machine learning (ML) techniques has become increasingly challenging. ML models often struggle with issues related to computational power, efficient parameter tuning, accurate model selection, and maintaining high performance when dealing with massive datasets. Deep learning approaches—such as convolutional neural networks (CNNs)—demand substantial computational resources to train on large-scale supervised learning tasks. Moreover, the difficulty of training these networks grows significantly as the size and complexity of the datasets increase. In response to these limitations, Quantum Machine Learning (QML) has emerged as a promising research domain at the intersection of quantum computing and machine learning. Quantum computers operate fundamentally differently from classical systems, using the principles of quantum mechanics to encode and process information. As a result, quantum computational methods have the potential to address certain problems that are computationally intensive or infeasible for classical machines. Index Terms— Quantum Machine Learning (QML); Big Data Optimization; Quantum Algorithms; Hybrid Quantum-Classical Models; Quantum Computing; Data Analytics
Hybrid quantum-classical algorithms have gained attention as a promising strategy to harness the advantages of both quantum and classical computing for machine learning applications. This review delves into the potential of these algorithms to address the limitations inherent in purely classical or quantum methods, offering a thorough overview of the current landscape in this area. We cover the basics of quantum computing, such as qubits, superposition, and entanglement, and emphasize the challenges that both quantum and classical computing face in the realm of machine learning. Various hybrid frameworks, including quantum-classical neural networks, quantum-enhanced optimization algorithms, and quantum feature maps, are explored in depth. We examine specific hybrid algorithms, like the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA), assessing their potential to enhance data preprocessing, feature selection, model training, and inference. The role of quantum-inspired algorithms in bridging the gap between quantum and classical methods is also discussed. We investigate the scalability of hybrid approaches and their potential to capitalize on future advancements in quantum hardware, while considering implementation challenges such as quantum error correction and noise mitigation. The implications of these algorithms for privacy, security, fairness, and interpretability in machine learning are analyzed. Finally, we consider the potential impact of hybrid quantum-classical algorithms on various industries and their integration with other emerging technologies. This review offers a comprehensive perspective on the current state and future directions of this rapidly evolving field.