<!--HTML-->The quest to understand the fundamental constituents of the universe is at the heart of particle physics. However, the complexity of particle interactions, the volume of data produced by experiments, and the intricacy of theoretical models present significant challenges to advancements in this field. In recent years, artificial intelligence has emerged as a transformative tool for overcoming these challenges, offering new pathways to accelerate the pace of discovery and fostering a deeper understanding of the fundamental forces of nature. This talk aims to elucidate the pivotal role AI plays in particle physics, from optimizing detector design and operation to analyzing vast datasets and validating theoretical models.
<!--HTML-->The quest to understand the fundamental constituents of the universe is at the heart of particle physics. However, the complexity of particle interactions, the volume of data produced by experiments, and the intricacy of theoretical models present significant challenges to advancements in this field. In recent years, artificial intelligence has emerged as a transformative tool for overcoming these challenges, offering new pathways to accelerate the pace of discovery and fostering a deeper understanding of the fundamental forces of nature. This talk aims to elucidate the pivotal role AI plays in particle physics, from optimizing detector design and operation to analyzing vast datasets and validating theoretical models.
The intersection of artificial intelligence (AI) and quantum mechanics represents a frontier of scientific exploration, offering the potential to revolutionize our understanding of quantum systems. Despite the promise, significant challenges remain in effectively integrating AI techniques within quantum mechanics frameworks. This study aims to investigate the applications of AI in quantum mechanics, identifying both the challenges and opportunities presented by this interdisciplinary approach. The focus is on understanding how AI can enhance quantum simulations, optimize computations, and improve experimental designs. A comprehensive literature review was conducted, analyzing recent advancements in AI algorithms applied to quantum mechanics. Case studies were examined to illustrate successful implementations and the limitations encountered. Key metrics for evaluation included computational efficiency, accuracy, and scalability. Findings indicate that AI techniques, particularly machine learning and neural networks, can significantly expedite quantum simulations and enhance predictive accuracy. However, challenges such as data sparsity, interpretability of AI models, and the integration of AI with quantum algorithms were identified as significant barriers to progress. This research highlights the transformative potential of AI in advancing quantum mechanics while acknowledging the inherent challenges. Addressing these challenges will require collaborative efforts across disciplines, paving the way for innovative solutions that leverage AI to deepen our understanding of quantum phenomena and improve technological applications.
The simulation of quantum matter with classical hardware plays a central role in the discovery and development of quantum many-body systems, with far-reaching implications in condensed matter physics and quantum technologies. In general, efficient and sophisticated algorithms are required to overcome the severe challenge posed by the exponential scaling of the Hilbert space of quantum systems. In contrast, hardware built with quantum bits of information are inherently capable of efficiently finding solutions of quantum many-body problems. While a universal and scalable quantum computer is still beyond the horizon, recent advances in qubit manufacturing and coherent control of synthetic quantum matter are leading to a new generation of intermediate scale quantum hardware. \n \nThe complexity underlying quantum many-body systems closely resembles the one encountered in many problems in the world of information and technology. In both contexts, the complexity stems from a large number of interacting degrees of freedom. A powerful strategy in the latter scenario is machine learning, a subfield of artificial intelligence where large amounts of data are used to extract relevant features and patterns. In particular, artificial neural networks have been demonstrated to be capable of discovering low-dimensional representations of complex objects from high-dimensional dataset, leading to the profound technological revolution we all witness in our daily life. \n \nIn this Thesis, we envision a new paradigm for scientific discovery in quantum physics. On the one hand, we have the essentially unlimited data generated with the increasing amount of highly controllable quantum hardware. On the other hand, we have a set of powerful algorithms that efficiently capture non-trivial correlations from high-dimensional data. Therefore, we fully embrace this data-driven approach to quantum mechanics, and anticipate new exciting possibilities in the field of quantum many-body physics and quantum information science. We revive a powerful stochastic neural network called a restricted Boltzmann machine, which slowly moved out of fashion after playing a central role in the machine learning revolution of the early 2010s. We introduce a neural-network representation of quantum states based on this generative model. We propose a set of algorithms to reconstruct unknown quantum states from measurement data and numerically demonstrate their potential, with important implications for current experiments. These include the reconstruction of experimentally inaccessible properties, such as entanglement, and diagnostics to determine sources of noise. Furthermore, we introduce a machine learning framework for quantum error correction, where a neural network learns the best decoding strategy directly from data. We expect that the full integration between quantum hardware and artificial intelligence will become the gold standard, and will drive the world into the era of fault-tolerant quantum computing and large-scale quantum simulations.
Some of the most significant achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating large-scale experiments like the Large Hadron Collider or the Tevatron. In these facilities, the ultimate theory to describe matter at the most fundamental level is constantly probed and verified. These experiments often produce large amounts of data that require storing, processing, and analysis techniques that continually push the limits of traditional information processing schemes. Thus, the High-Energy Physics (HEP) field has benefited from advancements in information processing and the development of algorithms and tools for large datasets. More recently, quantum computing applications have been investigated to understand how the community can benefit from the advantages of quantum information science. Nonetheless, to unleash the full potential of quantum computing, there is a need to understand the quantum behavior and, thus, scale up current algorithms beyond what can be simulated in classical processors. In this work, we explore potential applications of quantum machine learning to data analysis tasks in HEP and how to overcome the limitations of algorithms targeted for Noisy Intermediate-Scale Quantum (NISQ) devices.
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.
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.