The objective of this work is to study the application of quantum computing methods for the development of Machine Learning algorithms and their integration into neutral atom computers. Quantum computing is an emerging technology that is gaining significant relevance in both industrial and academic research and development sectors. This work will provide a historical introduction to the emergence of quantum computing, analyzing the motivation for its development and discussing its current state. An introduction to the fundamentals of quantum computing will also be provided, starting from the very basics of quantum mechanics. Following this, the operation of Pasqal’s neutral atom quantum computer will be explained. Finally, the work will demonstrate how to use the quantum computing tools offered by Pasqal’s computer to implement a purely quantum algorithm (Bernstein-Vazirani) and to solve a clustering problem.
The objective of this work is to study the application of quantum computing methods for the development of Machine Learning algorithms and their integration into neutral atom computers. Quantum computing is an emerging technology that is gaining significant relevance in both industrial and academic research and development sectors. This work will provide a historical introduction to the emergence of quantum computing, analyzing the motivation for its development and discussing its current state. An introduction to the fundamentals of quantum computing will also be provided, starting from the very basics of quantum mechanics. Following this, the operation of Pasqal’s neutral atom quantum computer will be explained. Finally, the work will demonstrate how to use the quantum computing tools offered by Pasqal’s computer to implement a purely quantum algorithm (Bernstein-Vazirani) and to solve a clustering problem.
<!--HTML-->PASQAL manufactures quantum processors based on neutral atom arrays offering attractive scaling, flexible topology and both analog and digital computational modes. To discuss their current and eventual capabilities, we will elaborate on the physics and technologies that powers these quantum devices. PASQAL works jointly with industrial and academic players to identify relevant challenges and enabling ideas; we will showcase how tailored applications and experiments are constructed with them, while building for the future.
Summary This talk explores the potential of quantum computing and the current market status. It focuses on PASQAL’s technology using neutral atoms for scalable quantum computing. The main categories of algorithms designed for neutral atom quantum computers and their real-world applications are summarized. The energy industry has numerous quantum applications, including energy resource management, grid optimization, complex system optimization, simulation, and battery design. Three disruptive examples are highlighted. Integration of quantum hardware with HPC centers is crucial. The talk addresses challenges, the expected maturity of quantum computing, and the integration challenges with HPC centers. It prompts us to consider the future of HPC centers in the era of quantum computing.
Quantum computing is an emerging form of computing that uses the rules of quantum physics to process information in ways that are far different from how computers do it now. Quantum computing uses quantum bits, or qubits, which can exist in superpositions of states and get entangled with each other, which is different from classical computing, which uses a bit that represents binary values (0 or 1). These characteristics allow quantum devices to execute parallel computations, enabling significant speedups for specific categories of problems considered intractable for classical machines. This chapter gives a thorough introduction to quantum computing, starting with a historical overview of its foundations in quantum physics and significant theoretical advancements. It analyzes fundamental ideas including superposition, entanglement, quantum interference, and the no-cloning theorem, explaining how these elements distinguish quantum computation from traditional approaches. The advancement of quantum hardware is examined through several architectures, trapped ions, superconducting circuits, photonics, and topological qubits, together with software frameworks like Qiskit, Cirq, and PennyLane, which support algorithm development and simulation. The chapter examines fundamental quantum algorithms, such as Grover’s algorithm for database search, Shor’s algorithm for integer factorization, and hybrid variational methods designed for noisy intermediate-scale quantum devices. The practical applications in domains including as encryption, optimization, machine learning, chemistry, and finance emphasize the real-world significance of quantum technology. Challenges like decoherence, noise, scalability, and algorithmic maturity are rigorously analyzed, followed by an anticipated discourse on future trajectories including fault-tolerant architectures, quantum networking, and the development of quantum cloud services. This chapter presents a fundamental introduction for students, researchers, and practitioners seeking to comprehend the essential principles, functionalities, and possibilities of quantum computing in the post–Moore’s law period.
Abstract Artificial intelligence (AI) and quantum computing represent a new frontier in computing, offering the promise of solving problems previously considered unsolvable. This article aims to provide an in-depth analysis of the current state of research at the intersection of quantum computing and artificial intelligence. We will explore the basic concepts, major applications, and challenges of quantum machine learning (QML). We will start by offering a definition of the motivation for the convergence of quantum computing and artificial intelligence. We will then proceed to offer a description of the basic algorithmic frameworks for quantum machine learning, such as quantum differential algorithms (VQA) for near-term quantum (NISQ) devices and the prospects for error-tolerant quantum algorithms such as the Harrow-Hassidim-Lloyd algorithm. We will then proceed to offer a methodical analysis of the current applications of quantum machine learning in finance, cybersecurity, materials science, among other fields. We will then proceed to discuss the basic challenges facing the implementation of quantum machine learning, while offering a glimpse of the future of quantum-enhanced artificial intelligence.
Quantum computing, a transformative field that emerged from quantum mechanics and computer science, has gained immense attention for its potential to revolutionize computation. This paper aims to address the fundamentals of quantum computing and provide a comprehensive guide for both novices and experts in the field of quantum computing. Beginning with the foundational principles of quantum computing, we introduce readers to the fundamental concepts of qubits, superposition, entanglement, interference, and noise. We explore quantum hardware, quantum gates, and basic quantum circuits. This study offers insight into the current phase of quantum computing, including the noisy intermediate-scale quantum (NISQ) era and its potential for solving real-world problems. Furthermore, we discuss the development of quantum algorithms and their applications, with a focus on famous algorithms like Shor’s algorithm and Grover’s algorithm. We also touch upon quantum computing’s impact on various industries, such as cryptography, optimization, machine learning, and material science. By the end of this paper, readers will have a solid understanding of quantum computing’s principles, applications, and the steps involved in developing quantum circuits. Our goal is to provide a valuable resource for those eager to embark on their quantum computing journey and for researchers looking to stay updated on this rapidly evolving field.
Data clustering consists of grouping data without prior training, a process known as unsupervised learning. Given the constant growth of database volumes, contemporary computers require significant computational time to perform this partitioning. To effectively address this challenge, quantum computing offers a promising perspective, as it could eventually provide more efficient resources for clustering massive datasets. However, current quantum computers are not yet mature; they are prone to noise, which limits their practical utility. In this context, we have designed a hybrid classical-quantum clustering algorithm tailored for the current Noisy Intermediate-Scale Quantum (NISQ) era. We evaluated this algorithm under various conditions and across different types of datasets, comparing it against several well-known clustering algorithms. Our results demonstrate that, under certain conditions, quantum noise can actually provide an advantage to our algorithm. Furthermore, we worked on deploying this algorithm on actual quantum processors and developed tools to optimize this deployment. Finally, to diversify the quantum architectures used, we extended our research to include neutral-atom quantum computers.