Commercial demands for products of Artificial Intelligence are undeniably at its golden age in the modern world. Advances in physical hardware have provided enough computation capacity to train large machine-learning models for decent accuracy. Commercial products such as autonomous vehicles are demanding the growing specificity and complexity of machine learning models, trading off increasing model size for high accuracy and low loss to produce intelligent models. Meanwhile, Quantum Computing has recently advanced into the NISQ (Noisy Intermediate-scale Quantum) era, offering a solid foundation for the potential of improving modern computation tasks with quantum algorithms. Previous quantum algorithms harness the random nature of quantum mechanics to preserve quantum information and construct the basis for Variational Quantum Circuits (VQC). In this paper, we produce three convolution regression models to predict robot directions for a simplified autonomous-driving task and demonstrate a quantum advantage in our quantum-enhanced hybrid approach as compared to two classical models. Our results in this minimal example reflect the near-term feasibility of hybrid quantum models to enhance modern AI industries, which provides a promising vision to incorporate quantum computing in commercial products.
Commercial demands for products of Artificial Intelligence are undeniably at its golden age in the modern world. Advances in physical hardware have provided enough computation capacity to train large machine-learning models for decent accuracy. Commercial products such as autonomous vehicles are demanding the growing specificity and complexity of machine learning models, trading off increasing model size for high accuracy and low loss to produce intelligent models. Meanwhile, Quantum Computing has recently advanced into the NISQ (Noisy Intermediate-scale Quantum) era, offering a solid foundation for the potential of improving modern computation tasks with quantum algorithms. Previous quantum algorithms harness the random nature of quantum mechanics to preserve quantum information and construct the basis for Variational Quantum Circuits (VQC). In this paper, we produce three convolution regression models to predict robot directions for a simplified autonomous-driving task and demonstrate a quantum advantage in our quantum-enhanced hybrid approach as compared to two classical models. Our results in this minimal example reflect the near-term feasibility of hybrid quantum models to enhance modern AI industries, which provides a promising vision to incorporate quantum computing in commercial products.
Modern progress in computing power provides great capacity to train large machine-learning models for sufficient accuracy needed for commercial products. Commercial products such as autonomous vehicles are demanding the growing specificity and complexity of machine learning models, trading off increasing model size for high accuracy and low loss to produce intelligent models. Meanwhile, Quantum Computing has recently advanced into the NISQ (Noisy Intermediate-scale Quantum) era, offering a solid foundation for the potential of improving modern computation tasks with quantum algorithms. Previous quantum algorithms (Variational Quantum Circuits) harness the random nature of quantum mechanics to preserve quantum information and improve model training by allowing parallel computing. This paper demonstrates a hybrid methodology implementing a Hybrid Quantum Neural Network (HQNN) to achieve quantum advantage for a convolution regression model. The resulting hybrid model performance shows an immediate reduction in model complexity training efficiency for the autonomous robot, this contrast with the classical control model provides a promising approach for the near-term feasibility of quantum-enhanced ML products for AI industries.
Modern progress in computing power provides great capacity to train large machine-learning models for sufficient accuracy demanded by commercial products. Commercial products such as autonomous vehicles are demanding the complexity of neural networks, traditionally increasing model size to achieve high accuracy and low loss to produce intelligent models. Meanwhile, Quantum Computing has recently advanced into the NISQ (Noisy Intermediate-scale Quantum) era, offering a solid foundation for the potential of improving modern computation tasks with quantum algorithms. Previous quantum algorithms (Variational Quantum Circuits) harness the random nature of quantum mechanics to preserve quantum information and improve model training by allowing parallel computing. This paper demonstrates a hybrid methodology implementing a Hybrid Quantum Neural Network (HQNN) to achieve quantum advantage for a convolutional regression model. The resulting hybrid model performance shows an immediate reduction in model complexity and improvement in training efficiency for the autonomous robot, this contrast with the classical control model provides a promising approach for the near-term feasibility of quantum-enhanced ML products for AI industries.
The rapid advancement of quantum computing hardware and software has ushered in a new era for computational science, promising to tackle problems that are intractable for classical computers. Parallelly, machine learning (ML) has revolutionized data analysis and artificial intelligence. The confluence of these two fields, known as Quantum Machine Learning (QML), aims to harness the principles of quantum mechanics-such as superposition, entanglement, and interference-to enhance and redefine classical machine learning algorithms. This survey provides a comprehensive overview of the burgeoning field of QML. We begin by elucidating the fundamental quantum concepts that underpin QML approaches. We then present a detailed taxonomy and analysis of prominent QML algorithms, categorizing them into quantum-enhanced classical models and fully quantum models. Key families of algorithms discussed include Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNNs), and Quantum Generative Adversarial Networks (QGANs). For each, we explain the theoretical quantum advantage, the circuit implementation, and the current limitations. Furthermore, we delve into the critical challenges facing the practical realization of QML, including the pervasive issue of noise in Noisy Intermediate-Scale Quantum (NISQ) devices, the problem of barren plateaus in training quantum circuits, and the complexities of efficient data encoding (quantum feature maps). We also present a small-scale experimental simulation of a variational quantum classifier on a standardized dataset, demonstrating its principle and comparing its performance with a classical counterpart. Finally, the paper discusses the future trajectory of QML, highlighting the importance of error mitigation, hybrid quantum-classical architectures, and the development of quantum-specific learning inductive biases. We conclude that while fault-tolerant quantum computers remain on the horizon, QML represents a paradigm shift with the potential to deliver profound computational advantages in the coming decades.
Background: Artificial Intelligence (AI) and Machine Learning (ML) have significantly transformed predictive analytics across domains such as healthcare, finance, and environmental sciences. However, traditional ML models face limitations when dealing with large-scale, high-dimensional datasets, particularly due to computational inefficiencies and the "curse of dimensionality." Quantum computing offers promising solutions by leveraging superposition and entanglement to perform complex computations at exponential speeds. Objective: This study aims to explore how quantum computing can enhance AI and ML models, particularly in the domain of predictive analytics. It proposes a comprehensive Quantum-AI framework that integrates quantum technologies into existing predictive systems to overcome the challenges posed by classical approaches. Methods: The study employed hybrid quantum-classical models using frameworks like Qiskit, TensorFlow Quantum, and Pennylane. Datasets were sourced from real-world applications in finance, healthcare, and climate science. Experiments were designed to compare classical and quantum- enhanced models on metrics such as accuracy, scalability, and computational time. Specific algorithms used include Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN). Result: The proposed quantum-enhanced models showed significant improvements: • Accuracy increased from 85% (classical) to 92% (quantum) in finance-related predictions. • Diagnostic models in healthcare achieved a 94% accuracy rate and reduced training time by 50%. • Climate modeling tasks achieved 90% improved accuracy and 20% faster simulation times. The results validate the effectiveness of quantum models in processing large, complex datasets more efficiently. Conclusion: Quantum-enhanced AI presents a transformative approach to predictive analytics by addressing the core limitations of classical ML systems. This study demonstrates the practical feasibility and industrial relevance of integrating quantum computing into AI workflows. It opens avenues for further research in developing scalable, domain-specific quantum algorithms and hybrid AI models for real-world applications.
Quantum Machine Learning (QML) unites quantum computing with modern AI to achieve potential gains in expressivity, runtime, and sample efficiency. This paper synthesizes advances in quantum feature maps, variational quantum circuits (VQCs), and hybrid quantum-classical workflows deployable on noisy intermediate-scale quantum (NISQ) hardware. We outline design principles for data encoding, ansatz selection, and trainability, and summarize error-aware practices—error mitigation, shot-frugal estimation, and compiler co-design. Using synthetic but instructive experiments, we illustrate scaling behavior, noise sensitivity, end-to-end inference latency across backends, and optimizer convergence in variational training. We conclude with an agenda emphasizing domain-aligned encodings, rigorous benchmarking, and reproducible open science to validate advantage beyond asymptotic claims.
With the advancement of quantum computing in the Noisy Intermediate-Scale Quantum (NISQ) era, hybrid quantum-classical algorithms have emerged as promising approaches for addressing certain machine learning tasks. This study investigates the Variational Quantum Classifier (VQC), a hybrid model that integrates parameterized quantum circuits with classical optimization routines, implemented via IBM’s Qiskit framework. We provide a detailed account of the VQC architecture, including feature encoding, ansatz construction, and optimization methodology. To assess its performance, we conduct a comparative analysis between VQC and classical Support Vector Classifiers (SVC) across synthetic and real-world datasets, including Iris and a binary-filtered version of MNIST. Experimental results reveal that classical models consistently achieve higher accuracy and faster training times. However, the VQC demonstrates functional viability, particularly under noise-free conditions and reduced-dimensionality settings. These findings underscore the current limitations of quantum models while highlighting the potential of VQC as a proof-of-concept for quantum-enhanced learning as quantum hardware matures.