The Kamiltonian Quantum Compute-in-Memory (K-QCIM) architecture represents a paradigm shift, successfully merging classical Compute-in-Memory (CIM) efficiency, hardware-native Fast Target Propagation, and bidirectional quantum temporal dynamics into a single, cohesive substrate. This paper outlines the critical improvements the Kamiltonian framework offers over classical hardware and standard temporal theories.
The Kamiltonian Quantum Compute-in-Memory (K-QCIM) architecture represents a paradigm shift, successfully merging classical Compute-in-Memory (CIM) efficiency, hardware-native Fast Target Propagation, and bidirectional quantum temporal dynamics into a single, cohesive substrate. This paper outlines the critical improvements the Kamiltonian framework offers over classical hardware and standard temporal theories.
Quantum computing and artificial intelligence (AI) are two rapidly evolving technologies poised to redefine the future of computation and decision-making.The two fields unite under the name Quantum Artificial Intelligence (QAI) or Quantum Intelligence, which enables a transformative approach to problem-solving through the fusion of AI mental functions with quantum computing capabilities.This paper provides a comprehensive review of the current landscape, highlighting the opportunities and challenges in integrating quantum computing with AI systems.The research investigates current developments in quantum machine learning, quantum neural networks, and hybrid quantum-classical systems, which show promise for achieving rapid performance improvements and enhanced scalability and data processing capabilities.At the same time, it identifies critical barriers, including hardware limitations, algorithmic complexity, data encoding issues, and the lack of standardization in frameworks.The review further explores future directions, including the role of explainable AI, error correction, and interdisciplinary research in achieving practical quantum-AI synergy.By analyzing both the technological potential and the practical constraints, this paper aims to outline a roadmap for advancing quantum intelligence as a transformative force in next-generation computational paradigms.
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.
The integration of quantum computing and artificial intelligence (AI) constitutes a bidirectional synergy that is reshaping both disciplines. This review investigates the reciprocal relationship in which AI addresses foundational challenges in quantum computing, while quantum computing offers the potential to advance machine learning beyond classical constraints. Recent advancements exemplify this interaction: Google DeepMind’s AlphaQubit neural network decoder has achieved state-of-the-art quantum error correction, improving performance by 6% over tensor networks and 30% over correlated matching methods. Additionally, quantum neural networks have demonstrated exponential improvements in sample complexity for specific learning tasks. This review systematically examines the current landscape of quantum-AI integration across three primary dimensions. First, it addresses AI-enhanced quantum systems, such as transformer-based error correction, reinforcement learning for circuit optimization, and AI-driven hardware calibration. Second, it explores quantum-accelerated machine learning algorithms, including variational quantum neural networks, quantum generative adversarial networks, and quantum reinforcement learning. Third, it evaluates industry deployments in sectors such as life sciences, financial services, climate modeling, and pharmaceutical development. Notable examples include IonQ’s quantum chemistry simulations, which achieved 40% efficiency improvements in carbon capture material design; St. Jude’s identification of KRAS protein inhibitors using quantum machine learning with experimental validation; and JP Morgan Chase’s implementation of quantum portfolio optimization. The analysis indicates that the field has advanced from preliminary demonstrations to production-grade applications, especially in drug discovery and molecular simulation, where quantum computing offers measurable benefits. Nevertheless, several challenges remain, including barren plateaus in variational algorithms, scalability constraints in current noisy intermediate-scale quantum (NISQ) devices, requirements for real-time decoding speed, and the necessity for fault-tolerant quantum computing (FTQC) systems. IBM’s 2029 roadmap, which targets 200 logical qubits capable of executing 100 million gates, together with progress in high-rate quantum low-density parity-check codes, outlines a trajectory toward practical fault tolerance. This review makes two principal research contributions: Comprehensive Evaluation Framework for Quantum Advantage Assessment: We establish the first systematic, integrated methodology for determining quantum computing feasibility that combines problem characterization, resource estimation, quantum advantage assessment, and quantum algorithm paradigm selection. This framework consolidates criteria scattered across academic literature and industry practice into a unified decision-making tool applicable across chemistry, optimization, machine learning, and simulation domains. The framework addresses a critical industry need: enabling non-expert practitioners (chemists, financial analysts, materials scientists) to objectively assess quantum computing suitability without requiring deep quantum expertise. Novel Quantum Resource Optimization Algorithms: We present three concrete algorithmic contributions advancing quantum-AI integration: • Data Encoding Efficiency Algorithm that automatically selects optimal qubit encoding strategies (amplitude vs. angle encoding) minimizing total quantum resource consumption • Error Budget Optimization Algorithm that iteratively determines optimal quantum error correction code distance balancing logical error rate targets against physical qubit overhead • Real-time Hardware Specification Aggregation from multiple quantum cloud platforms (IBM Quantum, Amazon Braket, Google, IonQ) enabling dynamic feasibility assessment as hardware capabilities evolve Combined, these contributions establish quantum computing advantage assessment as a rigorous, data-driven discipline rather than ad-hoc expert judgment. The framework enables strategic quantum computing investment decisions, accelerates problem identification for early quantum utility, and provides a reference methodology for standardizing quantum advantage evaluation across academia and industry. This review synthesizes insights from recent academic literature, industry implementations, and expert perspectives to deliver a comprehensive assessment of quantum-AI synergies grounded in this evaluation framework. The mathematical foundations, ranging from quantum Fourier transforms to Gaussian processes, are examined. Best practices for researchers and practitioners are also outlined. The findings suggest that, although universal quantum advantage has not yet been realized, domain-specific quantum-AI applications in chemistry, optimization, and sensing are achieving practical utility in 2024-2025. This development marks a significant transition from theoretical potential to commercial realization, informed by rigorous evaluation methodologies.
Quantum Artificial Intelligence (QAI) has emerged at the nexus of quantum computing and AI, promising to redefine computational frontiers. This survey critically synthesizes the state-of-the- art through 2024, elucidating the profound bidirectional synergy between these fields. We analyze how classical machine learning is accelerating quantum hardware control, circuit optimization, and error correction. Conversely, we assess the potential quantum advantage of algorithms, including variational and kernel-based methods, across domains such as drug discovery, financial modeling, and cybersecurity. Our analysis reveals a critical trade-of between the utility of near-term Noisy Intermediate-Scale Quantum (NISQ) devices and the long-term promise of fault-tolerant architectures. We identify fundamental obstacles to QAI’s advancement, including hardware decoherence, algorithmic barren plateaus, and data-encoding bottlenecks. While QAI’s potential is transformative, achieving practical quantum advantage requires a concerted effort to overcome these core challenges at the hardware-software interface. This work provides a roadmap for navigating the current landscape and prioritizing future research in this rapidly evolving discipline. • We introduce a bi-directional Quantum-AI framework, AI-for-QC and QC-for-AI, and provide a practical roadmap for integrating both paradigms across the full stack. • We quantify AI-enabled gains: calibration time drops from 8 hours to 2 hours, circuit depth falls by up to 42%, and ML decoders cut logical error rates by about half. • We synthesize comparative guidance on VQAs, quantum kernels, and hybrid/quantum Transformers, clarifying trainability limits and criteria for credible quantum advantage. • We deliver a clinically grounded playbook for radiology, covering small-data use cases, plug-in hybrid modules, OOD validation, fairness assessments, and TRIPOD/CONSORT-AI reporting.
Quantum Neural Networks (QNNs) represent a ground-breaking confluence of quantum computing and neural network architectures, offering the potential to revolutionize artificial intelligence (AI). By leveraging the principles of quantum mechanics, such as superposition and entanglement, QNNs promise unparalleled capabilities in solving complex computational problems and advancing fields like quantum chemistry, financial modeling, and secure communications. Despite their transformative potential, the development and deployment of QNNs is hindered by significant challenges, including noisy hardware, barren plateaus (BP) in training, security and privacy concerns, and the lack of standardized benchmarks and appropriate evaluation metrics.This position paper explores the current state of QNNs, emphasizing their unique advantages, such as enhanced expressibility and resource efficiency, while addressing their limitations. We propose actionable opportunities for advancements, including hardware innovations and scalable algorithm designs, algorithmic improvements to overcome trainability, security, privacy, and interdisciplinary collaborations. Moreover, we highlight the critical need for ethical considerations and equitable accessibility to ensure inclusive participation in the quantum era. Looking ahead, the paper outlines future directions for achieving scalable, fault-tolerant QNNs and their integration into real-world applications. By charting this strategic path, we aim to foster dialogue and drive progress in harnessing the full potential of QNNs to shape the future of AI and quantum computing.
Artificial Intelligence (AI) has revolutionized modern computing, yet classical computational architectures face limitations when solving highly complex optimization problems.The emergence of quantum computing introduces new possibilities for enhancing AI performance through principles such as superposition, entanglement, and quantum parallelism.This paper explores the concept of Quantum Intelligence, an interdisciplinary framework that integrates quantum computing with AI to optimize learning algorithms, decision-making processes, and large-scale data analysis.The study reviews quantum optimization techniques including the Quantum Approximate Optimization Algorithm (QAOA), quantum annealing, and variational quantum algorithms.These techniques enable AI systems to process large search spaces and combinatorial optimization problems more efficiently than classical methods.The paper also examines hybrid quantum-classical architectures, potential applications in fields such as healthcare, logistics, and finance, and the current limitations of quantum hardware.The findings suggest that quantum-enhanced AI could significantly improve computational efficiency, accelerate model training, and enable new forms of intelligent systems.Although practical implementation is still in its early stages, Quantum Intelligence represents a transformative step toward the next generation of AI optimization technologies.