As quantum computing advances from theoretical promise to experimental reality, the need for rigorous experiment tracking becomes critical. Drawing inspiration from best practices in machine learning (ML) and artificial intelligence (AI), we argue that reproducibility, scalability, and collaboration in quantum research can benefit significantly from structured tracking workflows. This paper explores the application of MLflow in quantum research, illustrating how it enables better development practices, experiment reproducibility, decision making, and cross-domain integration in an increasingly hybrid classical-quantum landscape.
As quantum computing advances from theoretical promise to experimental reality, the need for rigorous experiment tracking becomes critical. Drawing inspiration from best practices in machine learning (ML) and artificial intelligence (AI), we argue that reproducibility, scalability, and collaboration in quantum research can benefit significantly from structured tracking workflows. This paper explores the application of MLflow in quantum research, illustrating how it enables better development practices, experiment reproducibility, decision making, and cross-domain integration in an increasingly hybrid classical-quantum landscape.
As quantum computing advances from theoretical promise to experimental reality, the need for rigorous experiment tracking becomes critical. Drawing inspiration from best practices in machine learning (ML) and artificial intelligence (AI), we argue that reproducibility, scalability, and collaboration in quantum research can benefit significantly from structured tracking workflows. This paper explores the application of MLflow in quantum research, illustrating how it enables better development practices, experiment reproducibility, decision making, and cross-domain integration in an increasingly hybrid classical-quantum landscape.
As quantum software engineering (QSE) matures, practitioners are adopting classical software engineering practices. This paper proposes a quantum experiment tracking infrastructure that aids quantum researchers in making informed decisions while developing quantum software incrementally and iteratively. By leveraging tools and best practices from classical machine learning communities, we provide a mature environment for quantum software practitioners to enhance their practices. Our approach involves collecting a comprehensive quantum provenance dataset from each experiment execution for analysis and decision-making. To validate our approach, we developed an experimental environment where a program was iteratively developed on quantum simulators and an IQM computer, and we demonstrate how the development workflow backed by the tracking infrastructure meets expectations for quantum experiment reporting.
Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research-quantum information versus machine learning (ML) and artificial intelligence (AI)-have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our 'big data' world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement-exploring what ML/AI can do for quantum physics and vice versa-researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain.
Quantum information technologies, and intelligent learning systems, are both emergent technologies that will likely have a transforming impact on our society. The respective underlying fields of research -- quantum information (QI) versus machine learning (ML) and artificial intelligence (AI) -- have their own specific challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question to what extent these fields can learn and benefit from each other. QML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently, we have witnessed breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups in ML, critical in our "big data" world. Conversely, ML already permeates cutting-edge technologies, and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been demonstrated for interactive learning, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments, and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement, researchers have also broached the fundamental issue of quantum generalizations of ML/AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is described by quantum mechanics. In this review, we describe the main ideas, recent developments, and progress in a broad spectrum of research investigating machine learning and artificial intelligence in the quantum domain.
Abstract Quantum computing has immense potential to advance the field of Artificial Intelligence (AI), promising faster processing, better optimization, and the ability to handle complex data structures more effectively. However, several challenges need to be addressed, including hardware limitations, algorithm development, and integration with existing AI workflows. This work provides a comprehensive overview of the current state of quantum AI research, covering key areas such as quantum machine learning, quantum deep learning, quantum natural language processing, quantum problem solving, quantum fuzzy logic, quantum evolutionary computation, and quantum decision making. It also presents a collection of interesting use cases targeted at different vertical sectors. The research methodology involves a thorough literature review to capture the most impactful developments from the standpoint of algorithm scalability in the 2020–2024 timeframe. The review highlights the benefits of quantum approaches, such as improved learning capacity, robustness to overfitting and, in some specific cases, quantum speedups for solving various problems. The interdisciplinary research landscape demonstrates a discernible trajectory toward the implementation of practical, large‐scale quantum computational intelligence. This progression is contingent upon the adoption of scalable algorithms that can deliver a quantum advantage, diverging from the replication of classical AI workflows.
In the realm of artificial intelligence (AI), the integration of quantum computing with machine learning (ML) has sparked a significant shift in how we approach problems that were once deemed computationally infeasible. Quantum Machine Learning (QML) represents an exciting intersection of quantum mechanics and machine learning, potentially revolutionizing areas such as optimization, pattern recognition, and data analysis. Although quantum computing itself is still in its infancy, the synergy between quantum theory and classical machine learning algorithms promises to unlock new capabilities that classical computing could never achieve. This paper delves into the latest advancements in Quantum Machine Learning, its theoretical underpinnings, practical applications, and the challenges that researchers face as they explore this rapidly growing field.