Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Efficient simulation of a quantum system's dynamics is expected to be an important application area for quantum computers as existing classical computers cannot do this. However, quantum systems are not unique in being hard to simulate. For example, classical nonlinear continuum systems and fields are governed by nonlinear partial differential equations whose solution is also hard for classical computers. Solving such equations is essential for many economically important industries/applications such as the aerospace industry, weather‐forecasting, fiber‐optics communication, and plasma magneto‐hydrodynamics. This raises the question: can a quantum computer speed‐up solving these equations? In this Review, a new quantum algorithm is described for solving nonlinear partial differential equations for which the answer is yes. First, a new quantum algorithm is discussed for solving the Navier‐Stokes nonlinear partial differential equations which govern the flow of a viscous fluid. Its construction, verification, and computational cost are described, and it is shown that a significant quantum speed‐up is possible. Its generalization to a quantum algorithm for solving nonlinear partial differential equations is described. The Review closes with a discussion of next steps forward. These new quantum algorithms open up a large new application area for quantum computing with substantial economic impact, including the trillion‐dollar aerospace industry.

Surveillance systems like CCTVs help in security as a way of preventing crime, but they are also prone to possible threats like data breaches, unauthorized access to data, and data manipulation. The research examines the challenges of CCTV, including cybersecurity, and proposes intelligent mitigation strategies. To address these threats, a machine learning-based intrusion detection system (IDS) is used for real-time detection. Moreover, for systems to stop or minimize unauthorized alterations, the summation of blockchain technology is enabled to ensure CCTV integrity. The past methods of threat alarming and detection are highly insufficient in refuting and challenging intellectual cyber threats, and here is where Artificial Intelligence (AI) comes in, offering a positive and most suitable solution for real-time cybersecurity issues, with the help of algorithms that detect and identify errors or malfunctions within a system operation and network traffic. AI operates at a very high speed in identifying and providing solutions to complex problems, utilizing search algorithms and optimization techniques to find the best and most satisfying results from a set of commands. As the era of AI is growing incrementally, the blockchain system is not standing still. Since there is a combination of AI and blockchain, the technological systems have accelerated, as the unity has brought data integrity, especially where cryptographic foundations like RSA and ECC are threatened by quantum computing. Blockchain enables trustworthy AI, where the data used in AI can be verified, thereby enhancing secure data exchange and integrity. Real-time monitoring is one of the key benefits of anomaly detection and quantum systems, which utilize efficient and robust algorithms and infrastructure to process data quickly and accurately, following model training and optimization. The use of quantum-resistant cryptography ensures that the data logged remains intact, even in the face of potential threats. Therefore, this system integrates surveillance technology, ranging from intelligent data analysis to cryptography, which is more secure against human interference. It is imperative in governance, businesses, and other sectors of the economy. These sectors require secure monitoring and transparency, making the era of AI and quantum computing a crucial time for data management systems.

This rapid growth in data and the increased complexity of machine learning models have made it almost impossible to continue pushing forward with classical computing, thereby creating a pressing need for new computational paradigms. The following paper will examine how quantum machine learning answers the above questions by applying the principles of quantum mechanics to the art of artificial intelligence (AI) and big data analytics. We examine some of the current Quantum Machine Learning (QML) algorithms and their applications based on a framework that employs quantum-enhanced feature spaces, variational quantum circuits, and quantum-inspired classical algorithms. Our methodology combines theoretical analysis with empirical evaluation of these methods over standard benchmark datasets, using both quantum simulators and real quantum hardware. Key findings include a set of advantages carried by QML in specific fields, such as dimensionality reduction, optimization problems, and some clustering tasks. These are the quadratic speedup for support vector machines and the exponential advantage for principal component analysis. Our results also show that quantum error correction isn’t perfect and that loading classical data into a quantum system can be hard. We conclude that, while QML holds much promise, it is, by all means, clearly still a way off from being considered a panacea. It is awaiting further breakthroughs in quantum hardware, error mitigation techniques, and algorithm design in order to fully realize its potential. This work thus presents a roadmap for future research and highlights where QML is best placed to make major impactful differences in artificial intelligence and big data analytics.

We review the latest developments in Fiber-optic Telecommunications and Networks presented at OFC 2022 and compare the progress accomplished since the previous year conference, OFC 2021. In particular, we note this year’s emphasis on the application of Neural Networks (NN) as part of Artificial Intelligence (AI) and Machine Learning (ML), as well as continued research in quantum communications and key distribution. We also expect future conferences to include papers about optical variants of quantum computers, a field of great interest that can exploit many quantum systems, as well as papers on continuing developments in High-Speed Communications beyond 800 Gb/s, and increased use of coherent modulation and detection in Data Centers Interconnect, Cloud and Edge computing, the latter in support of 5 G Internet Of Things (IOT).

Quantum Neural Computing

There's a lot of hope that, with quantum computing, the future can look much brighter and be focused on solving a lot of complex issues we face in modern society. The power to give and manipulate information with quantum systems can be game-changing for fields such as secure data transfer, unmanageable optimization, drug discovery in medical applications, and certain facets of artificial intelligence. Algorithms over the discouraging search spaces would be able to break existing encryption, and significant progress has been achieved in algorithmic attacks on both secret key (cryptographic hashes and message authentication codes) and public key systems. A thorough survey is required so that the state-of-the-art quantum resilient theoretical and experimental methods and models may be understood from academia as well as from industry, and new directions which need to be promoted are suggested. It is important to investigate the possible security threats, challenges, impact and risk factors introduced by the integration of quantum computing in application systems, middleware, quantum system core components networks and cloud services critical infrastructures standards and regulation.

A real-time visual processing theory is used to analyze and explain a wide variety of perceptual grouping and segmentation phenomena, including the grouping of textured images, randomly defined images, and images built up from periodic scenic elements. The theory explains how “local” feature processing and “emergent” features work together to segment a scene, how segmentations may arise across image regions that do not contain any luminance differences, how segmentations may override local image properties in favor of global statistical factors, and why segmentations that powerfully influence object recognition may be barely visible or totally invisible. Network interactions within a Boundary Contour (BC) System, a Feature Contour (FC) System, and an Object Recognition (OR) System are used to explain these phenomena. The BC System is defined by a hierarchy of orientationally tuned interactions, which can be divided into two successive subsystems called the OC filter and the CC loop. The OC filter contains two successive stages of oriented receptive fields which are sensitive to different properties of image contrasts. The OC filter generates inputs to the CC loop, which contains successive stages of spatially short-range competitive interactions and spatially long-range cooperative interactions. Feedback between the competitive and cooperative stages synthesizes a global context-sensitive segmentation from among the many possible groupings of local featural elements. The properties of the BC System provide a unified explanation of several ostensibly different Gestalt rules. The BC System also suggests explanations and predictions concerning the architecture of the striate and prestriate visual cortices. The BC System embodies new ideas concerning the founda-tions of geometry, on-line statistical decision theory, and the resolution of uncertainty in quan-tum measurement systems. Computer simulations establish the formal competence of the BC System as a perceptual grouping system. The properties of the BC System are compared with probabilistic and artificial intelligence models of segmentation. The total network suggests a new approach to the design of computer vision systems, and promises to provide a universal set of rules for perceptual grouping of scenic edges, textures, and smoothly shaded regions.

Tensor network states and methods have erupted in recent years. Originally developed in the context of condensed matter physics and based on renormalization group ideas, tensor networks lived a revival thanks to quantum information theory and the understanding of entanglement in quantum many-body systems. Moreover, it has been not-so-long realized that tensor network states play a key role in other scientific disciplines, such as quantum gravity and artificial intelligence. In this context, here we provide an overview of basic concepts and key developments in the field. In particular, we briefly discuss the most important tensor network structures and algorithms, together with a sketch on advances related to global and gauge symmetries, fermions, topological order, classification of phases, entanglement Hamiltonians, AdS/CFT, artificial intelligence, the 2d Hubbard model, 2d quantum antiferromagnets, conformal field theory, quantum chemistry, disordered systems, and many-body localization.

Abstract: This chapter explores the transformative potential of the synergy between quantum computing and artificial intelligence (AI) across genomics, economics, and scientific research. Quantum-AI integration leverages the computational power of quantum systems and the analytical capabilities of AI to solve high-dimensional problems, enhance predictive modeling, and accelerate innovation. In genomics, quantum computing drives advancements in genome sequencing, protein-folding simulations, and personalized medicine, enabling breakthroughs in diagnostics and drug discovery. In economics, quantum-AI models revolutionize forecasting, optimize financial decisions, and influence global trade and policy frameworks. Additionally, this synergy advances interdisciplinary research in fields like high-energy physics, climate modeling, and materials science. Despite challenges such as scalability, data security, and access barriers, the future of quantum-AI lies in democratizing these technologies, fostering interdisciplinary collaboration, and paving the way for unprecedented scientific and technological progress. Keywords Quantum computing, artificial intelligence, quantum-AI synergy, genomics, economics, scientific research, genome sequencing, protein folding, drug discovery, economic forecasting, financial optimization, interdisciplinary research, high-energy physics, climate modeling, materials science, data security, quantum neural networks.

A perspective on machine learning and data science for strongly correlated electron problems

Quantum computing, leveraging the principles of superposition and entanglement, has emerged as a revolutionary technology with the potential to outperform classical computers in specific tasks. This paper explores the concept of quantum supremacy, marked by Google's Sycamore processor, and its implications for classical computing. It discusses the foundations of quantum computing, including qubits, superposition, and key quantum algorithms like Shor's and Grover's, highlighting their advantages over classical systems. This study addresses the milestones in achieving quantum supremacy, including experimental benchmarks and performance comparisons. The challenges in scaling quantum systems, mitigating decoherence, and addressing criticisms of quantum supremacy claims are examined. Additionally, the paper outlines emerging applications in cryptography, optimization, and artificial intelligence and emphasizes the importance of hybrid quantum-classical systems in bridging current technological gaps. The future of quantum computing lies in advancing fault-tolerant systems and fostering interdisciplinary collaborations to realize its transformative potential across industries.

This paper presents a novel interdisciplinary framework that integrates quantum supersymmetry with artificial intelligence to develop an algebraically structured foundation for generalized intelligence. Drawing from the mathematical rigor of supersymmetric quantum mechanics and Lie superalgebras such as osp(1∣2) and sl(2∣1), we propose AI architectures where learning dynamics are governed by graded algebraic transformations and symmetry-preserving constraints. By modeling intelligent agents as evolving quantum systems under the action of supercharges, the framework introduces dual representations—bosonic and fermionic—into latent spaces, enabling semantically coherent, interpretable, and generalizable learning. The research outlines a hybrid simulation environment combining symbolic computation and quantum frameworks to validate superalgebra-guided models on symbolic reasoning and structured prediction tasks. This approach not only enhances model robustness and generalization but also opens new frontiers in quantum-algebraic AI, offering a blueprint for the design of principled, high-order intelligence. Keywords: quantum supersymmetry, Lie superalgebras, generalized intelligence, artificial intelligence, algebraic AI, osp(1∣2), symmetry-preserving learning, supercharges, graded vector spaces, quantum AI, interpretability, symbolic reasoning, scientific discovery, algebraic machine learning, Hamiltonian learning

Quantum walks are a promising framework that can be used to both understand and implement quantum information processing tasks. The quantum stochastic walk is a recently developed framework that combines the concept of a quantum walk with that of a classical random walk, through open system evolution of a quantum system. Quantum stochastic walks have been shown to have applications in as far reaching fields as artificial intelligence. However, there are significant constraints on the kind of open system evolutions that can be realized in a physical experiment. In this work, we discuss the restrictions on the allowed open system evolution, and the physical assumptions underpinning them. We show that general implementations would require the complete solution of the underlying unitary dynamics, and sophisticated reservoir engineering, thus weakening the benefits of experimental investigations.

All-optical neural networks (AONNs) promise transformative gains in speed and energy efficiency for artificial intelligence (AI) by leveraging light's intrinsic parallelism and wave nature. However, their scalability has been fundamentally limited by the high power requirements of conventional nonlinear optical elements. Here, we present a low-power nonlinear activation scheme based on a three-level quantum system driven by dual laser fields. This platform introduces a two-channel nonlinear activation matrix with self- and cross-nonlinearities, enabling true multi-input, multi-output optical processing. The system supports tunable activation behaviors, including sigmoid and ReLU functions, at ultralow power levels (17 μW per neuron). We validate our approach through theoretical modeling and experimental demonstration in rubidium vapor cells, showing the feasibility of scaling to deep AONNs with millions of neurons operating under 20 W of total optical power. Crucially, we demonstrate the all-optical generation of gradient-like signals with backpropagation, paving the way for all-optical training. These results mark a significant advance toward scalable, high-speed, and energy-efficient optical AI hardware.

Recurrent neural networks have seen widespread use in modeling dynamical systems in varied domains such as weather prediction, text prediction and several others. Often one wishes to supplement the experimentally observed dynamics with prior knowledge or intuition about the system. While the recurrent nature of these networks allows them to model arbitrarily long memories in the time series used in training, it makes it harder to impose prior knowledge or intuition through generic constraints. In this work, we present a path sampling approach based on principle of Maximum Caliber that allows us to include generic thermodynamic or kinetic constraints into recurrent neural networks. We show the method here for a widely used type of recurrent neural network known as long short-term memory network in the context of supplementing time series collected from different application domains. These include classical Molecular Dynamics of a protein and Monte Carlo simulations of an open quantum system continuously losing photons to the environment and displaying Rabi oscillations. Our method can be easily generalized to other generative artificial intelligence models and to generic time series in different areas of physical and social sciences, where one wishes to supplement limited data with intuition or theory based corrections.