Quantum Neural Network Classifier for Cancer Registry System Testing: A Feasibility Study

In this work, we studied the use of Quantum Machine Learning (QML) algorithms for binary classification and compared their performance with classical Machine Learning (ML) methods. QML merges principles of Quantum Computing (QC) and ML, offering improved efficiency and potential quantum advantage in data-driven tasks and when solving complex problems. In binary classification, where the goal is to assign data to one of two categories, QML uses quantum algorithms to process large datasets efficiently. Quantum algorithms like Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) exploit quantum parallelism and entanglement to enhance performance over classical methods. This study focuses on two common QML algorithms, Quantum Support Vector Classifier (QSVC) and QNN. We used the Qiskit software and conducted the experiments with three different datasets. Data preprocessing included dimensionality reduction using Principal Component Analysis (PCA) and standardization using scalers. The results showed that quantum algorithms demonstrated competitive performance against their classical counterparts in terms of accuracy, while QSVC performed better than QNN. These findings suggest that QML holds potential for improving computational efficiency in binary classification tasks. This opens the way for more efficient and scalable solutions in complex classification challenges and shows the complementary role of quantum computing.

Recent advances in quantum computing and machine learning have brought about a promising intersection of these two fields, leading to the emergence of quantum machine learning (QML). However, the integration of quantum computing and machine learning poses several challenges. One of the prominent challenges lies in the presence of barren plateaus (BP) in QML algorithms, particularly in quantum neural networks (QNNs). Recent studies have successfully identified the fundamental causes underlying the existence of BP in QNNs. This paper presents a framework designed to explore the interplay of multiple factors contributing to the BP problem in quantum neural networks (QNNs), which poses a critical challenge for the practical applications of QML. We focus on the combined influence of data encoding, qubit entanglement, and ansatz expressibility in hybrid quantum neural networks (HQNNs) for multi-class classification tasks. Our framework aims to empirically analyze the joint impact of these factors on the training landscape of HQNNs. Our results show that the occurrence of the BP problem in HQNNs is contingent upon the expressibility of the underlying ansatz and the type of the adopted data encoding technique. Additionally, we observe that qubit entanglement also plays a role in exacerbating the BP problem. Leveraging various evaluation metrics for classification tasks, we systematically evaluate the performance of HQNNs and provide recommendations tailored to different constraint scenarios. Our findings emphasize the significance of our framework in addressing the practical success of QNNs.

Recent theoretical results in quantum machine learning have demonstrated a general trade-off between the expressive power of quantum neural networks (QNNs) and their trainability; as a corollary of these results, practical exponential separations in expressive power over classical machine learning models are believed to be infeasible as such QNNs take a time to train that is exponential in the model size. We here circumvent these negative results by constructing a hierarchy of efficiently trainable QNNs that exhibit unconditionally provable, polynomial memory separations of arbitrary constant degree over classical neural networks—including state-of-the-art models, such as Transformers—in performing a classical sequence modeling task. This construction is also computationally efficient, as each unit cell of the introduced class of QNNs only has constant gate complexity. We show that contextuality—informally, a quantitative notion of semantic ambiguity—is the source of the expressivity separation, suggesting that other learning tasks with this property may be a natural setting for the use of quantum learning algorithms.

Quantum Machine Learning (QML) is an emerging field at the intersection of quantum computing and machine learning, aiming to enhance classical machine learning methods by leveraging quantum mechanics principles such as entanglement and superposition. However, skepticism persists regarding the practical advantages of QML, mainly due to the current limitations of noisy intermediate-scale quantum (NISQ) devices. This study addresses these concerns by extensively assessing Quantum Neural Networks (QNNs)-quantum-inspired counterparts of Artificial Neural Networks (ANNs), demonstrating their effectiveness compared to classical methods. We systematically construct and evaluate twelve distinct QNN configurations, utilizing two unique quantum feature maps combined with six different entanglement strategies for ansatz design. Experiments conducted on a wind energy dataset reveal that QNNs employing the $\mathbf{Z}$ feature map achieve up to $93 \%$ prediction accuracy when forecasting wind power output using only four input parameters. Our findings show that QNNs outperform classical methods in predictive tasks, underscoring the potential of QML in real-world applications.

Despite the promise for performance and accuracy improvements of quantum inspired (QI) algorithms over classical machine learning (ML) algorithms, such gains have not been realized in practice. The quantum inspired algorithms can theoretically achieve significant speed up based on sampling assumptions and have thus far failed to outperform the existing classical ML models in practical applications. The speedup of quantum machine learning (QML) algorithms assume the access to data in quantum random access memory (QRAM) which is a strong assumption with current quantum architectures. QI algorithms assume sample and query (SQ) access to input vector and norms of matrices using a dynamic data structure. We explore the components of these models and the assumptions in this paper by surveying the recent works in QML and QI Machine learning (QIML) algorithms. We limit our study to QML and QIML models on achieving a speed up over classical ML techniques rather than individual proofs of these algorithms. This study highlights the assumptions being made that are currently not practical for QML and QIML algorithms in achieving performance advantage over classical ML algorithms.

The astounding acceleration in Artificial Intelligence and Quantum Computing advances naturally gives rise to a line of research, which unrolls the potential advantages of quantum computing on classical Machine Learning tasks, known as Quantum Machine Learning or Quantum Machine Intelligence. The typical objectives are either (1) exploring the potential quantum advantages on classical learning tasks or (2) levering well-established classical ML algorithms to tackle quantum-related problems on Noisy Intermediate-Scale Quantum (NISQ) devices. Along the second research direction, we study Quantum Neural Networks (QNNs) to accomplish the purpose of Bayesian learning. By observing a wide range of studies on QNNs, in which the sole training method is based on frequentist training, we find that Bayesian learning benefits QNNs from two aspects. First, Bayesian-trained models enjoy a high level of generalization due to the prior and posterior distribution usage compared to frequentist training, which will be justified by this paper’s theoretical study of model capacity. Second, Bayesian Inference offers epistemic uncertainty estimation, which merits the decision-making process. It is worth mentioning that frequentist-trained QNNs generally lack this desirable property. Under the Bayesian training procedure, our derived models can be considered a new class of QNNs (called BayesianQNNs) which possesses both desirable properties of Bayesian Inference while maintaining comparable predictive performance as frequentist counterparts. The proposed Bayesian Quantum Neural Networks is justified by empirical evidence from numerical experiments.

Emotion Recognition (ER) is an important capability that is increasingly being used to enhance the user experience in various fields such as, Human-Computer interaction, Cognitive Neuroscience, Neurofeedback, Robotics, Artificial Intelligence, Augmented Reality, Virtual Reality, Mental Health, etc. Emotions are generated in the limbic system, a set of brain structures that are involved in emotional processing, memory, and motivation. ER from electroencephalography (EEG) signals has gained attention from researchers in recent years as it provides a more accurate and non-intrusive way of understanding human emotions. This research paper presents a comprehensive study of the use of Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs) for ER using EEG signals. The aim of this study is to investigate the potential of quantum machine learning algorithms in improving the performance of ER tasks compared to classical Machine Learning (ML) algorithms. The model was simulated using IBM Qiskit due to the limited resources of quantum computers. Intriguingly, the experimental results contradict the speed-up claims of Quantum Computing (QC) by exhibiting longer computation times and lesser accuracy when compared to traditional methods. SVM and NN yield 68.5% and 69.2% accuracy while the quantum version gives 63% and 65.2% accuracy on the benchmark dataset SEED. This research provides evidence that Quantum Machine Learning (QML) algorithms need to be improved for ER from EEG signals and provide a reliable and non-intrusive way of understanding human emotions.

As the limits of classical machine learning (ML) become increasingly evident—especially in terms of computational scalability and data processing—quantum computing emerges as a promising frontier capable of addressing these challenges. Quantum Machine Learning (QML) represents the intersection of quantum computing and traditional ML, aiming to leverage quantum phenomena such as superposition, entanglement, and quantum parallelism to enhance learning capabilities, accelerate training, and solve complex problems that are intractable for classical algorithms. This paper provides a comprehensive overview of the evolving field of QML, beginning with the theoretical foundations of quantum computing and its synergy with classical machine learning principles. We explore key QML algorithms, including quantum support vector machines (QSVM), variational quantum classifiers (VQC), quantum neural networks (QNN), and hybrid quantum-classical approaches that exemplify the practical integration of both paradigms. We further examine real-world applications of QML across domains such as drug discovery, finance, climate modeling, and optimization, where quantum enhancements have begun to show early but promising results. Despite its theoretical potential, QML faces substantial limitations, including quantum hardware instability, limited qubit scalability, noise sensitivity, and unresolved challenges in quantum data encoding and interpretability. In synthesizing recent advancements and persistent obstacles, this paper highlights the importance of continued interdisciplinary research, algorithmic refinement, and hardware evolution. We also outline future directions that could lead QML from experimental promise to practical utility, including developments in quantum error correction, benchmarking standards, and application-specific quantum architectures

Applying quantum computing techniques to machine learning has attracted widespread attention recently and quantum machine learning has become a hot research topic. There are three major categories of machine learning: supervised, unsupervised, and reinforcement learning (RL). However, quantum RL has made the least progress when compared to the other two areas. In this study, we implement the well-known RL algorithm Q learning with a quantum neural network and evaluate it in the grid world environment. RL is learning through interactions with the environment, with the aim of discovering a strategy to maximize the expected cumulative rewards. Problems in RL bring in unique challenges to the study with their sequential nature of learning, potentially long delayed reward signals, and large or infinite size of state and action spaces. This study extends our previous work on solving the contextual bandit problem using a quantum neural network, where the reward signals are immediate after each action.

Quantum Machine Learning (QML) is an emerging interdisciplinary field that integrates quantum computing with classical machine learning techniques to enhance computational efficiency and solve complex problems beyond the capabilities of classical systems. This paper explores fundamental QML algorithms, including quantum-enhanced data processing, quantum neural networks, and quantum support vector machines. We discuss how quantum speedup can be achieved through quantum parallelism and entanglement, leading to improvements in optimization and data classification tasks. Additionally, we highlight applications of QML in areas such as drug discovery, financial modeling, and cryptography. While current quantum hardware imposes limitations, ongoing advancements in quantum algorithms and error correction techniques suggest a promising future for QML. We conclude with a discussion on the challenges and future directions in the field, emphasizing the need for hybrid quantum-classical approaches and scalable quantum hardware.

Quantum Machine Learning (QML) is emerging as a transformative technology in cybersecurity, particularly in anomaly detection for cyber security audits. Traditional machine learning models are effective but face scalability and efficiency limitations as cyber threats grow more sophisticated. QML, leveraging quantum computing’s ability to process and analyze large datasets in parallel, offers potential breakthroughs in identifying anomalous patterns that could signify cyber threats such as data breaches, insider threats, or unauthorized access. Content Analysis Research Methodology used in this research work. This paper explores the integration of QML into anomaly detection systems for cyber security audits, where detecting deviations from normal behavior is crucial. Quantum algorithms, particularly those based on Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), and Quantum Principal Component Analysis (QPCA) can enhance the detection of subtle anomalies that classical algorithms may overlook due to noise or the complex, high-dimensional nature of cyber data. The inherent properties of quantum computing, such as superposition and entanglement, allow for more efficient feature selection and optimization, potentially leading to faster and more accurate anomaly detection. The impact of implementing QML in cyber security audits is profound. First, it enhances detection capabilities by identifying anomalies with greater precision, reducing false positives, and improving response times to cyber incidents. Second, quantum algorithms’ ability to manage exponentially large datasets makes them ideal for environments with extensive data logs, such as enterprise networks and cloud infrastructures. Third, as cyber threats become increasingly adaptive and stealthy, QML offers a dynamic solution that evolves alongside these threats by continuously learning from new patterns of attack. However, practical challenges remain, including the need for quantum hardware advancements, the development of hybrid quantum-classical models, and ensuring the interpretability of quantum models in audit scenarios. Despite these challenges, early research and experimental implementations demonstrate the potential of QML to revolutionize anomaly detection in cybersecurity audits. This paper concludes that while QML is still in its early stages, its application to anomaly detection holds promise for significantly enhancing the effectiveness of cyber security audits. The impact of this technology, when fully realized, could redefine how organizations protect their networks and data from ever-evolving cyber threats, making QML a critical area for future research and development in cybersecurity.

The rapid evolution of cyber threats has rendered conventional security approaches inadequate for managing increasingly sophisticated risks. This study introduces a Quantum Machine Learning Cybersecurity Framework that leverages quantum computing and machine learning to enhance cybersecurity across multiple dimensions. The research employs a structured methodology, beginning with the integration of Quantum Key Distribution (QKD) for secure key exchange and progressing through the deployment of Quantum Neural Networks (QNN) and Quantum Support Vector Machines (QSVM) for anomaly detection and adversarial threat management. The framework also incorporates Quantum Reinforcement Learning (QRL) for autonomous incident response, a Quantum Authentication module for securing identity verification using biometric and behavioral data, and a Policy Compliance Interface powered by Quantum Compliance Analyzers for regulatory adherence. Experimental results demonstrated substantial improvements in cybersecurity metrics, including a 96% accuracy in threat detection, a 28% reduction in incident response time, and a 96% success rate in compliance simulations. These findings underscore the framework's capacity to offer adaptive, scalable, and efficient cybersecurity solutions tailored to modern challenges. This study provides a significant step toward integrating quantum technologies into practical cybersecurity applications, paving the way for future innovations in intelligent, secure, and adaptable defense systems.

Quantum Machine Learning (QML) is a new phraseology in the world today. With various upcoming Artificial Intelligence (AI) technologies, everyday data scientists and corporate around the world are trying to harness its power. QML borrows concepts of Quantum Computing and QML algorithms enhance the existing Machine Learning (ML) algorithms by processing datasets more efficiently. Quantum Computers are a powering force driving QML algorithms that boosts computational power and helps analyse data. QML algorithms can be applied in sectors involving number crunching, pattern identification and so on. In the existing world, QML can be a game-changer for organizations that work on identifying product correlations and customer behaviour in a brief time. This paper discusses studies related to principles of Quantum Mechanics, Quantum Computing and QML algorithm. Additionally, it explores opportunities and challenges faced in the field of QML. Lastly, the paper talks about QML applications and compares various available QML toolkits from the market. Future direction and exhaustive survey of papers from various spectrums related to Quantum Computing is presented in this paper.

With the increasing demand for high-performance materials in energy, healthcare, and electronics, the discovery of novel nanomaterials has become a critical area of research. Traditional methods for predicting material properties, such as Density Functional Theory (DFT) and classical machine learning (ML), often struggle with scalability and computational efficiency when dealing with complex, high-dimensional quantum systems. This study explores the potential of Quantum Machine Learning (QML) as a transformative tool for accelerating nanomaterial property prediction. By leveraging quantum computing frameworks like IBM Qi skit and PennyLane, quantum algorithms such as Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) were applied to a real-world dataset containing 486 nanomaterial entries. Comparative analysis using performance metrics like MAE, RMSE, and R2 demonstrated that quantum models outperformed classical ones in both accuracy and adaptability to data complexity. Power BI was used for advanced visualization to interpret feature relationships, entropy levels, and prediction patterns across models. Key findings suggest that QML provides a promising alternative to conventional methods by offering faster, more accurate predictions, particularly for datasets with high entropy. While challenges remain—such as limited dataset scope and hardware access—this study lays the groundwork for broader adoption of QML in computational material science and calls for future research involving real quantum hardware and larger, more diverse datasets.

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 Adenocarcinoma versus Squamous cell carcinoma. The data consists of approximately 20,000 gene expression values for each of 104 patients. The data was curated from Kuner (Geo lung cancer data set—gse10245, 2009, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10245 ) and Golumbic (Geo lung cancer data set—gse18842, 2010, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18842 ). We used a combination of classical and quantum machine learning models to successfully classify these patients. We utilized feature selection methods based on univariate statistics in addition to XGBoost (Guestrin and Chen in Xgboost: a scalable tree boosting system, 2007, https://dl.acm.org/citation.cfm?id=1273596 ). 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.