Artificial intelligence (AI) has transformed computer-assisted classical research and is beginning to demonstrate its power in quantum information science (QIS). While proposals and early demonstrations of AI+QIS are emerging, the debate remains: can AI generate unconventional designs of genuine scientific value, or is it limited to optimizing known methods? In this perspective, we argue that AI agents offer clear advantages in designing quantum optical systems, particularly where optimization across diverse variables and pattern recognition are required. We also highlight AI's potential to control realistic quantum devices in noisy environments through iterative, self-correcting strategies.
Artificial intelligence (AI) has transformed computer-assisted classical research and is beginning to demonstrate its power in quantum information science (QIS). While proposals and early demonstrations of AI+QIS are emerging, the debate remains: can AI generate unconventional designs of genuine scientific value, or is it limited to optimizing known methods? In this perspective, we argue that AI agents offer clear advantages in designing quantum optical systems, particularly where optimization across diverse variables and pattern recognition are required. We also highlight AI's potential to control realistic quantum devices in noisy environments through iterative, self-correcting strategies.
Recent advances in artificial intelligence (AI) and quantum computing are accelerating automation in scientific and engineering processes, fundamentally reshaping research methodologies. This perspective highlights parallels between scientific automation and established Computer-Aided Engineering (CAE) practices, introducing Quantum CAE as a framework that leverages quantum algorithms for simulation, optimization, and machine learning within engineering design. Practical implementations of Quantum CAE are illustrated through case studies for combinatorial optimization problems. Further discussions include advancements toward higher automation levels, highlighting the critical role of specialized AI agents proficient in quantum algorithm design. The integration of quantum computing with AI raises significant questions about the collaborative dynamics among human scientists and engineers, AI systems, and quantum computational resources, underscoring a transformative future for automated discovery and innovation.
In the twenty-first century, humanity is compelled to face global challenges. Such challenges involve complex systems. However, science has some cognitive and predictive limits in dealing with complex systems. Some of these limits are related to computational complexity and the recognition of variable patterns. To overcome these limits, artificial intelligence (AI) and quantum computing (QC) appear to be helpful. Even more promising is quantum AI (QAI), which emerged from the combination of AI and QC. The combination of AI and QC produces reciprocal, synergistic effects. This work describes some of these effects. It shows that QC offers new materials for implementing AI and innovative algorithms for solving optimisation problems and enhancing machine learning algorithms. Additionally, it demonstrates how AI algorithms can help overcome many of the experimental challenges associated with implementing QC. It also outlines several perspectives for the future development of quantum artificial intelligence.
Abstract Advanced AIs of the future will harness the most powerful computational operations known today: quantum computations. The synthesis of quantum computing and AI will give rise to Quantum AI–artificial intelligence with access to quantum computational resources–which is poised to expand the capabilities of intelligent systems. This advancement will lead to transformative abilities, such as ultraprecise sensing, the realistic simulation of complex natural phenomena, the efficient solution of classically intractable mathematical problems, or protocols for secure communication and enhanced coordination in multi-agent systems. The impact of this synthesis will be profound, promising to accelerate scientific discovery in areas from molecular biology to quantum gravity, while empowering engineers to create novel pharmaceuticals, viable fusion reactors, or designer materials with desirable properties. Quantum AI might also help to address a critical bottleneck in classical machine learning: its escalating demand for data and computational power. Beyond such applications lies another even more consequential possibility. If, as some recent theories and data from quantum neurobiology suggest, quantum processes give rise to consciousness and free will, then only Quantum AI–unconstrained by the limitations of classical computation–could enable the expansion of human consciousness and a transcendence of our biological origins.
Artificial Intelligence (AI) continues to drive innovation across industries, but faces mounting challenges in computational scalability, optimization, and interpretability as models grow increasingly complex. Quantum Artificial Intelligence (QAI) offers a promising path forward by leveraging quantum phenomena such as superposition and entanglement to augment learning processes beyond classical limits. This review synthesizes the recent progress in QAI, emphasizing a comparative perspective framed through its advantages and limitations. It examines how hybrid quantum-classical architectures and variational models seek to address constraints in the Noisy Intermediate-Scale Quantum (NISQ) era, and how applications in healthcare, finance, and cybersecurity illustrate both the potential and the barriers of the field. While several studies have broadly mapped the growth of QAI in algorithms and applications, this paper adopts a different perspective: emphasizing comparative analysis through advantages and limitations. The focus is on where QAI offers unique opportunities, where it encounters obstacles distinct from classical AI and quantum computing, and what research directions may transform these challenges into breakthroughs. By situating QAI within this comparative framework, the paper provides a realistic yet forward-looking assessment of its trajectory as a paradigm poised to reshape computational intelligence.
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.
Artificial intelligence (AI) technologies are attractive and growing universal and extensive in a broad range of perspectives from commercial applications to cutting-edge scientific research. In AI, one can simulate human intelligence processes via machines, especially computer systems. In a true sense, AI is a part of our modern life, as it is quite tough to identify an area where accomplishments in the field of AI would not be used. AI technologies enable machines to learn useful processes, including object recognition, speech recognition, natural language processing, machine vision, and digital personal assistants. In this context, quantum optics and quantum-based technologies have been found in prominent positions since they enable a speed up in learning processes [ 1–4 ]. It has been perceived that the demand for the algorithms that enable machines to learn quickly and efficiently have increased day-by-day as the field of AI has advanced. Quantum machine learning is a fast rising field connecting AI and quantum technology.