Many believe that the future of innovation lies in simulation. However, as computers are becoming ever more powerful, so does the hyperbole used to discuss their potential in modelling across a vast range of domains, from subatomic physics to chemistry, climate science, epidemiology, economics and cosmology. As we are about to enter the era of quantum and exascale computing, machine learning and artificial intelligence have entered the field in a significant way. In this article we give a brief history of simulation, discuss how machine learning can be more powerful if underpinned by deeper mechanistic understanding, outline the potential of exascale and quantum computing, highlight the limits of digital computing - classical and quantum - and distinguish rhetoric from reality in assessing the future of modelling and simulation, when we believe analogue computing will play an increasingly important role.
Many believe that the future of innovation lies in simulation. However, as computers are becoming ever more powerful, so does the hyperbole used to discuss their potential in modelling across a vast range of domains, from subatomic physics to chemistry, climate science, epidemiology, economics and cosmology. As we are about to enter the era of quantum and exascale computing, machine learning and artificial intelligence have entered the field in a significant way. In this article we give a brief history of simulation, discuss how machine learning can be more powerful if underpinned by deeper mechanistic understanding, outline the potential of exascale and quantum computing, highlight the limits of digital computing - classical and quantum - and distinguish rhetoric from reality in assessing the future of modelling and simulation, when we believe analogue computing will play an increasingly important role.
Quantum science underpins many of the most significant advances in modern physics, chemistry, and information technology. Quantum physics provides the fundamental laws governing matter and energy at microscopic scales, quantum chemistry applies these principles to explain molecular structure, bonding, and reactivity, and quantum computing seeks to exploit quantum phenomena such as superposition and entanglement to enable new computational paradigms. Despite their long-standing theoretical foundations and experimental successes, all three domains remain surrounded by persistent misconceptions, often amplified by abstract formalisms, popular science narratives, and rapid technological developments. This contemporary review critically examines the established facts and common myths associated with quantum physics, quantum chemistry, and quantum computing. Emphasis is placed on experimentally verified principles, computational feasibility, and realistic assessments of current capabilities. Recent advances reported between 2020 and 2025 are reviewed to highlight genuine progress in areas such as quantum simulations, molecular modeling, and noisy intermediate-scale quantum (NISQ) devices, while also addressing their practical limitations. By distinguishing validated achievements from speculative claims, this review provides a balanced, evidence-based perspective on modern quantum science. The article aims to clarify enduring misconceptions, promote conceptual clarity across disciplines, and outline realistic future directions for research and applications in quantum technologies.
The era of exascale computing presents both exciting opportunities and unique challenges for quantum mechanical simulations. While the transition from petaflops to exascale computing has been marked by a steady increase in computational power, the shift towards heterogeneous architectures, particularly the dominant role of graphical processing units (GPUs), demands a fundamental shift in software development strategies. This review examines the changing landscape of hardware and software for exascale computing, highlighting the limitations of traditional algorithms and software implementations in light of the increasing use of heterogeneous architectures in high-end systems. We discuss the challenges of adapting quantum chemistry software to these new architectures, including the fragmentation of the software stack, the need for more efficient algorithms (including reduced precision versions) tailored for GPUs, and the importance of developing standardized libraries and programming models.
The era of exascale computing presents both exciting opportunities and unique challenges for quantum mechanical simulations. While the transition from petaflops to exascale computing has been marked by a steady increase in computational power, the shift towards heterogeneous architectures, particularly the dominant role of graphical processing units (GPUs), demands a fundamental shift in software development strategies. This review examines the changing landscape of hardware and software for exascale computing, highlighting the limitations of traditional algorithms and software implementations in light of the increasing use of heterogeneous architectures in high-end systems. We discuss the challenges of adapting quantum chemistry software to these new architectures, including the fragmentation of the software stack, the need for more efficient algorithms (including reduced precision versions) tailored for GPUs, and the importance of developing standardized libraries and programming models.
Emerging technologies, including quantum information science and artificial education systems, have the potential to have significant implications for the future of human civilization. Quantum information, on the one hand, and machine learning (ML) and artificial intelligence (AI), on the other, consume their personal unique set of queries and contests that have been studied in isolation up until now. However, a recent study is starting to examine whether these disciplines can teach one another anything useful. The discipline of quantum ML investigates how quantum computing and ML may work together to find solutions to challenges in both areas. Major advancements in the two areas of effect have been made recently. Particularly relevant in today's “big data” era is the use of quantum computing to speed up the solution of machine learning (ML) challenges. However, ML is already present in many state-of-the-art technologies and may play a crucial role in future quantum technologies.
Quantum simulation and computing traditionally has been based on two main paradigms, namely, digital and analog. In the digital paradigm, usually single and two-qubit gates (where qubit is an acronym for quantum bit) are employed as building blocks for scalable, universal quantum computing, although errors add up fast and error correction will be ultimately needed for scaling up. In the analog paradigm, large analog blocks are normally employed for a unitary dynamics that carries out the computation, enabling quantum operations on many qubits with reduced errors, but with the drawback of a limited choice of evolutions and lack of universality. In the past decade, a new paradigm has emerged, showing interesting possibilities for quantum simulation and computing in the near and mid term. This is the paradigm of digital-analog quantum technologies, which proposes to combine the best of both paradigms: large analog blocks, provided by native interactions of the employed quantum platform, enabling scalability, combined with digital gates, allowing for more versatility and, ultimately, universality. In this Perspective, I give an overview of the evolution of the field along the past decade, and an outlook for its future possibilities.
The quantum race is on and the hype around quantum computing is only exceeded by artificial intelligence (AI). Tech giants, multinational corporations, and startups across a wide range of sectors, and even governments are betting on quantum technology, collaborating and competing to gain power that comes with having access to a quantum computer that can hopefully solve complex and relevant problems. But what is a quantum computer? How does it differ from a classical computer and why is it so difficult to build one? How could the realisation of such a mysterious machine represent power? Can quantum computing learn anything from the field of AI? This article answers these questions in a concise form without assuming any advanced knowledge in physics.