The design of next-gen materials has undergone remarkable progress in recent years, as evidenced by the emergence of automated platforms combining artificial intelligence (AI)-driven synthesis planning and robotics for execution. In this Mini-Review, we analyze how synergistic approaches that combine driven quantum dynamics, AI/machine learning, and quantum computing accelerate the discovery and design process of quantum materials with enhanced properties and novel functionalities. Building on the capabilities of each of the three methods, synergistic approaches can provide access to the materials' response to time-dependent fields, enable the rapid exploration of vast design spaces, and identify novel quantum phases and materials with optimal properties. We examine recent successes in next-gen materials science for quantum batteries, colloidal quantum dots solar cells, quantum phototransistors, rare-earth-free materials, and applications in quantum information processing. We conclude with a discussion of recent research efforts in AI-for-quantum computing and quantum machine learning for next-gen materials discovery.
The design of next-gen materials has undergone remarkable progress in recent years, as evidenced by the emergence of automated platforms combining artificial intelligence (AI)-driven synthesis planning and robotics for execution. In this Mini-Review, we analyze how synergistic approaches that combine driven quantum dynamics, AI/machine learning, and quantum computing accelerate the discovery and design process of quantum materials with enhanced properties and novel functionalities. Building on the capabilities of each of the three methods, synergistic approaches can provide access to the materials' response to time-dependent fields, enable the rapid exploration of vast design spaces, and identify novel quantum phases and materials with optimal properties. We examine recent successes in next-gen materials science for quantum batteries, colloidal quantum dots solar cells, quantum phototransistors, rare-earth-free materials, and applications in quantum information processing. We conclude with a discussion of recent research efforts in AI-for-quantum computing and quantum machine learning for next-gen materials discovery.
Quantum computing and artificial intelligence (AI) are converging into a distinct research frontier commonly referred to as quantum-enhanced artificial intelligence, or quantum machine learning (QML). This paper presents a conceptual and integrative review of how principles from quantum physics superposition, entanglement, and interference can be embedded into machine learning pipelines to reshape computational paradigms for classification, optimization, and representation learning. Using a structured narrative-review methodology, the study synthesizes theoretical foundations, algorithmic building blocks (quantum feature maps, variational quantum circuits, quantum kernel methods), and application domains spanning drug discovery, finance, materials science, and natural language processing. The review develops a hybrid quantum-classical architecture model and a complexity-comparison framework contrasting classical algorithms with their quantum counterparts, including Grover's search and Shor's factoring algorithm. Findings indicate that while theoretical speedups are well established, practical quantum advantage on noisy intermediate-scale quantum (NISQ) hardware remains constrained by decoherence, barren plateaus, and limited qubit connectivity. The paper contributes a synthesized taxonomy of quantum-enhanced AI methods and an evidence-based research agenda emphasizing error mitigation, hardware-aware ansatz design, and hybrid workload partitioning. The discussion further situates these developments within the broader trajectory of next-generation computing, arguing that near-term value will accrue primarily through hybrid quantum-classical systems rather than fully quantum pipelines. Implications for researchers, industry practitioners, and policymakers are discussed, alongside limitations inherent to a literature-synthesis approach.
The convergence of quantum computing and artificial intelligence has introduced a new paradigm in computational science known as Quantum Artificial Intelligence (QAI). By leveraging quantum mechanical principles such as superposition, entanglement, and quantum parallelism, QAI aims to overcome the limitations of classical machine learning, particularly in handling high-dimensional data, complex optimization, and scalability issues. This paper presents a comprehensive review of foundational concepts in both classical machine learning and quantum computing, followed by an in-depth discussion of emerging quantum algorithms tailored for AI applications, such as quantum neural networks, quantum support vector machines, and variational quantum classifiers. We explore the practical implications of these approaches across key sectors, including healthcare, finance, cybersecurity, and logistics. Furthermore, we identify critical challenges related to hardware limitations, algorithmic stability, data encoding, and ethical considerations. Finally, we outline research directions necessary to advance the field, highlighting the transformative potential of QAI in shaping the next generation of intelligent technologies
There has been remarkable development in the data-driven decision-making domain of Artificial Intelligence (AI). However, the complexity of the AI models and the amount of data have shown some drawbacks with conventional computing models. One of the interdisciplinary branches of quantum computing and machine learning is Quantum Machine Learning (QML), which is a combination of quantum computing and machine learning and relies on the unique characteristics of quantum computing, such as superposition, entanglement, and quantum parallelism, to enhance the performance of the machines. This review provides a comprehensive description of theory, architectures, algorithms, implementational approaches, applications, problems, and future directions of QML. The core concepts of quantum computing and machine learning are introduced. Different representations of quantum states, quantum data encoding, variational quantum circuits, quantum kernel methods, and hybrid quantum or classical optimization are discussed. Significant QML algorithms like Quantum Support Vector Machines, Quantum Neural Networks, Variational Quantum Eigen solvers, Quantum Approximate Optimization Algorithms, and Quantum reinforcement learning are explored and evaluated for computational power and applications. The scaling up of quantum computer sizes, data encoding overhead, complexity of optimizations, benchmarking, and decoherence are discussed in detail, as well as recent developments in fault-tolerant quantum computing and hybrid learning approaches. Finally, a conceptual framework is given to demonstrate the unification of classical computation and quantum computation to intelligently process data. This review demonstrates how QML could be the next-generation computational method to accelerate scientific advancements and enable new advanced AI technologies.
The integration of Artificial intelligence (AI) and Quantum computing (QC) is redefining the future of pharmaceutical research and development.While AI-particularly Machine learning (ML) and deep learning-accelerates target identification, virtual screening, and de novo drug design, it often suffers from interpretability issues, data bias, and limited mechanistic understanding.Conversely, QC leverages quantum mechanics to achieve unprecedented accuracy in molecular simulations, yet remains restricted by noise, limited qubits in the Noisy Intermediate-Scale Quantum (NISQ) era, and high costs.Emerging hybrid quantum-classical algorithms and Quantum Machine Learning (QML) approaches bridge these gaps, enabling high-fidelity in silico drug discovery workflows.Despite these advances, the regulatory environment has not kept pace.Current frameworks, such as Physiologically Based Pharmacokinetic (PBPK) modelling and Quantitative Structure-Activity Relationship (QSAR) models, provide limited guidance for validating complex AI-QC systems.Challenges include the lack of explainable artificial intelligence (XAI), uncertainty in verifying quantum outputs, data integrity, and accountability in hybrid pipelines.These gaps highlight the urgent need for regulatory science to adopt dynamic validation frameworks, algorithmic audits, and international harmonization.This review critically examines the technological and regulatory dimensions of AI-QC synergy in drug discovery, outlining key applications, limitations, and ethical considerations.We propose pathways for explainability-by-design, continuous learning oversight, and regulatory sandboxes to facilitate safe and effective adoption.The synergy of AI and QC offers the potential to accelerate timelines, reduce costs, and deliver precision medicines globally-but its promise can only be realized through proactive, transparent, and ethical governance.
Quantum Artificial Intelligence (Quantum AI) embodies the integration of quantum computational paradigms with artificial intelligence frameworks to address the computational intractability and scalability constraints inherent in classical AI systems. By leveraging quantum mechanical properties such as superposition, entanglement, and quantum interference, Quantum AI enables enhanced representation learning, accelerated optimization, and improved probabilistic inference in high-dimensional problem spaces. In parallel, deep-tech innovations—including advanced deep learning architectures, neuromorphic computing, and edge-based intelligent processing—are redefining the architectural foundations of next- generation intelligent systems through energy-efficient, adaptive, and real-time computation. This paper presents a technically rigorous and comprehensive review of Quantum AI and deep-tech innovations, focusing on algorithmic formulations, architectural models, and system-level integration strategies. Core methodologies such as quantum machine learning algorithms, parameterized quantum circuits, variational quantum algorithms, and hybrid quantum–classical optimization frameworks are critically analysed with respect to computational complexity, repressibility, and feasibility on noisy intermediate-scale quantum (NISQ) hardware. Additionally, the role of neuromorphic computing in enabling spiking neural architectures and event-driven processing is examined as a complementary paradigm to quantum-enhanced learning models, particularly for low-power and latency-critical applications. The paper further investigates application- driven deployments of Quantum AI across domains including medical diagnostics, cryptographic security, autonomous decision-making, and smart cyber-physical systems, highlighting performance trade-offs and implementation constraints. Key challenges such as quantum noise, decoherence, data encoding bottlenecks, limited qubit connectivity, and ethical governance are systematically discussed. Finally, future research directions are identified, emphasizing fault-tolerant quantum learning architectures, scalable hybrid integration with deep-tech systems, and co-design methodologies for realizing practical next-generation intelligence platforms.
Artificial Intelligence is rapidly transforming materials science and engineering, offering powerful tools to navigate complexity, accelerate discovery, and optimize material design in ways previously unattainable. Driven by the accelerating pace of algorithmic advancements and increasing data availability, AI is becoming an essential competency for materials researchers. This review provides a comprehensive and structured overview of the current landscape, synthesizing recent advancements and methodologies for materials scientists seeking to effectively leverage these data-driven techniques. We survey the spectrum of machine learning approaches, from traditional algorithms to advanced deep learning architectures, including CNNs, GNNs, and Transformers, alongside emerging generative AI and probabilistic models such as Gaussian Processes for uncertainty quantification. The review also examines the pivotal role of data in this field, emphasizing how effective representation and featurization strategies, spanning compositional, structural, image-based, and language-inspired approaches, combined with appropriate preprocessing, fundamentally underpin the performance of machine learning models in materials research. Persistent challenges related to data quality, quantity, and standardization, which critically impact model development and application in materials science and engineering, are also addressed.