This book targets computer scientists and engineers who are familiar with concepts in classical computer systems but are curious to learn the general architecture of quantum computing systems. It gives a concise presentation of this new paradigm of computing from a computer systems' point of view without assuming any background in quantum mechanics. As such, it is divided into two parts. The first part of the book provides a gentle overview on the fundamental principles of the quantum theory and their implications for computing. The second part is devoted to state-of-the-art research in designing practical quantum programs, building a scalable software systems stack, and controlling quantum hardware components. Most chapters end with a summary and an outlook for future directions. This book celebrates the remarkable progress that scientists across disciplines have made in the past decades and reveals what roles computer scientists and engineers can play to enable practical-scale quantum computing.

Quantum mechanics, the subfield of physics that describes the behavior of very small (quantum) particles, provides the basis for a new paradigm of computing. First proposed in the 1980s as a way to improve computational modeling of quantum systems, the field of quantum computing has recently garnered significant attention due to progress in building small-scale devices. However, significant technical advances will be required before a large-scale, practical quantum computer can be achieved. Quantum Computing: Progress and Prospects provides an introduction to the field, including the unique characteristics and constraints of the technology, and assesses the feasibility and implications of creating a functional quantum computer capable of addressing real-world problems. This report considers hardware and software requirements, quantum algorithms, drivers of advances in quantum computing and quantum devices, benchmarks associated with relevant use cases, the time and resources required, and how to assess the probability of success.

This volume presents papers on the topics covered at the National Academy of Engineering's 2018 US Frontiers of Engineering Symposium. Every year the symposium brings together 100 outstanding young leaders in engineering to share their cutting-edge research and innovations in selected areas. The 2018 symposium was held September 5-7 and hosted by MIT Lincoln Laboratory in Lexington, Massachusetts. The intent of this book is to convey the excitement of this unique meeting and to highlight innovative developments in engineering research and technical work.

Recent advancements in quantum-enabled systems present a variety of new opportunities and challenges. These technologies are important developments for a variety of computing, communications, and sensing applications. However, many materials and components relevant to quantum-enabled systems exist outside of the United States, and it is important to promote the development of assured domestic sources of materials, manufacturing capabilities, and expertise. The National Academies of Sciences, Engineering, and Medicine convened a 2-day workshop to explore implications and concerns related to the application of quantum-enabled systems in the United States. This workshop focused on quantum-enabled computing systems, quantum communications and networks, and quantum sensing opportunities. Participants explored the path to quantum computing, communications, and networks, opportunities for collaboration, as well as key gaps, supply chain concerns, and security issues. This publication summarizes the presentations and discussions from the workshop.

In the evolving landscape of quantum computing, the emergence of quantum computers in the Noisy Intermediate Scale Quantum (NISQ) regime marks a significant stride. Superconducting qubits have garnered popularity in both academic and industrial groups. However, the journey towards achieving a large-scale, fully error-corrected quantum computer faces challenges. This thesis addresses some of these challenges within an academic setup. One prominent challenge with superconducting qubits is Purcell decay. This work aims to tackle the issue by delving into the implementation of on-chip Purcell filters with Transmon qubits. The overarching goal is to pave the way for further scalability by ensuring compatibility of these designs with scalability plans. The thesis also introduces novel architectures for superconducting qudit processors, focusing on their already presented implementation in 3D cavities. Efforts are directed towards transitioning these processors to a planar platform for enhanced scalability. The coupling of these processors to environment is explored using coplanar waveguides, with the system's physics governed by the principles of circuit quantum electrodynamics. Finally, the thesis also delves into the packaging of planar qubit devices, aiming to facilitate easy scalability. This platform enables interfacing the devices with control equipment, shielding from stray fields, and offers the essential thermal link to the dilution refrigerator where they are housed. Each section of the thesis presents results emphasizing potential areas for improvement and refinement of the systems.

Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.

Quantum computers are poised to kick-start a new computing revolution—and you can join in right away. If you’re in software engineering, computer graphics, data science, or just an intrigued computerphile, this book provides a hands-on programmer’s guide to understanding quantum computing. Rather than labor through math and theory, you’ll work directly with examples that demonstrate this technology’s unique capabilities. Quantum computing specialists Eric Johnston, Nic Harrigan, and Mercedes Gimeno-Segovia show you how to build the skills, tools, and intuition required to write quantum programs at the center of applications. You’ll understand what quantum computers can do and learn how to identify the types of problems they can solve. This book includes three multichapter sections: Programming for a QPU—Explore core concepts for programming quantum processing units, including how to describe and manipulate qubits and how to perform quantum teleportation. QPU Primitives—Learn algorithmic primitives and techniques, including amplitude amplification, the Quantum Fourier Transform, and phase estimation. QPU Applications—Investigate how QPU primitives are used to build existing applications, including quantum search techniques and Shor’s factoring algorithm.

Many-body nuclear physics is the bridge that takes us from the fundamental laws governing individual nucleons to understanding how groups of them interact together to form the nuclei that lie at the heart of all atoms-the building blocks of our universe. Many powerful techniques of classical computation have been developed over the years in order to study ever more complex nuclear systems. However, we seem to be approaching the limits of such classical techniques as the complexity of many-body quantum systems grows exponentially. Yet, the recent development of quantum computers offers one hope as they are predicted to provide a significant advantage over classical computers when tackling problems such as the quantum many-body problem. In this thesis, we focus on developing and applying algorithms to tackle various many-body nuclear physics problems that can be run on the near-term quantum computers of the current noisy intermediate-scale quantum (NISQ) era. As these devices are small and noisy, we focus our algorithms on various many-body toy models in order to gain insight and create a foundation upon which future algorithms will be built to tackle the intractable problems of our time. In the first part, we tailor current quantum algorithms to efficiently run on NISQ devices and apply them to three pairing models of many-body nuclear physics, the Lipkin model, the Richardson pairing model, and collective neutrino oscillations. For the first two models, we solve for the ground-state energy while for the third, we simulate the time evolution and characterize the entanglement. In the second part, we develop novel algorithms to increase the efficiency and applicability of current algorithms on NISQ devices. These include an algorithm that compresses circuit depth to allow for less noisy computation and a variational method to prepare an important class of quantum states. Error mitigation techniques used to improve the accuracy of results are also discussed. All together, this work provides a road map for applications of the quantum computers of tomorrow to solve what nuclear phenomena mystify us today.

Quantum computers can (in theory) solve certain problems far faster than a classical computer running any known classical algorithm. While existing technologies for building quantum computers are in their infancy, it is not too early to consider their scalability and reliability in the context of the design of large-scale quantum computers. To architect such systems, one must understand what it takes to design and model a balanced, fault-tolerant quantum computer architecture. The goal of this lecture is to provide architectural abstractions for the design of a quantum computer and to explore the systems-level challenges in achieving scalable, fault-tolerant quantum computation. In this lecture, we provide an engineering-oriented introduction to quantum computation with an overview of the theory behind key quantum algorithms. Next, we look at architectural case studies based upon experimental data and future projections for quantum computation implemented using trapped ions. While we focus here on architectures targeted for realization using trapped ions, the techniques for quantum computer architecture design, quantum fault-tolerance, and compilation described in this lecture are applicable to many other physical technologies that may be viable candidates for building a large-scale quantum computing system. We also discuss general issues involved with programming a quantum computer as well as a discussion of work on quantum architectures based on quantum teleportation. Finally, we consider some of the open issues remaining in the design of quantum computers. Table of Contents: Introduction / Basic Elements for Quantum Computation / Key Quantum Algorithms / Building Reliable and Scalable Quantum Architectures / Simulation of Quantum Computation / Architectural Elements / Case Study: The Quantum Logic Array Architecture / Programming the Quantum Architecture / Using the QLA for Quantum Simulation: The Transverse Ising Model / Teleportation-Based Quantum Architectures / Concluding Remarks

Quantum computers can (in theory) solve certain problems far faster than a classical computer running any known classical algorithm. While existing technologies for building quantum computers are in their infancy, it is not too early to consider their scalability and reliability in the context of the design of large-scale quantum computers. To architect such systems, one must understand what it takes to design and model a balanced, fault-tolerant quantum computer architecture. The goal of this lecture is to provide architectural abstractions for the design of a quantum computer and to explore the systems-level challenges in achieving scalable, fault-tolerant quantum computation. In this lecture, we provide an engineering-oriented introduction to quantum computation with an overview of the theory behind key quantum algorithms. Next, we look at architectural case studies based upon experimental data and future projections for quantum computation implemented using trapped ions. While we focus here on architectures targeted for realization using trapped ions, the techniques for quantum computer architecture design, quantum fault-tolerance, and compilation described in this lecture are applicable to many other physical technologies that may be viable candidates for building a large-scale quantum computing system. We also discuss general issues involved with programming a quantum computer as well as a discussion of work on quantum architectures based on quantum teleportation. Finally, we consider some of the open issues remaining in the design of quantum computers. Table of Contents: Introduction / Basic Elements for Quantum Computation / Key Quantum Algorithms / Building Reliable and Scalable Quantum Architectures / Simulation of Quantum Computation / Architectural Elements / Case Study: The Quantum Logic Array Architecture / Programming the Quantum Architecture / Using the QLA for Quantum Simulation: The Transverse Ising Model / Teleportation-Based Quantum Architectures / Concluding Remarks