The development of quantum computing systems has been a staple of academic research since the mid-1990s when the first proposal for physical platforms were proposed using Nuclear Magnetic Resonance and Ion-Trap hardware. These first proposals were very basic, essentially consisting of identifying a physical qubit (two-level quantum system) that could be isolated and controlled to achieve universal quantum computation. Over the past thirty years, the nature of quantum architecture design has changed significantly and the scale of investment, groups and companies involved in building quantum computers has increased exponentially. Architectural design for quantum computers examines systems at scale: fully error-corrected machines, potentially consisting of millions if not billions of physical qubits. These designs increasingly act as blueprints for academic groups and companies and are becoming increasingly more detailed, taking into account both the nature and operation of the physical qubits themselves and also peripheral environmental and control infrastructure that is required for each physical system. In this paper, several architectural structures that I have worked on will be reviewed, each of which has been adopted by either a national quantum computing program or a quantum startup.
The development of quantum computing systems has been a staple of academic research since the mid-1990s when the first proposal for physical platforms were proposed using Nuclear Magnetic Resonance and Ion-Trap hardware. These first proposals were very basic, essentially consisting of identifying a physical qubit (two-level quantum system) that could be isolated and controlled to achieve universal quantum computation. Over the past thirty years, the nature of quantum architecture design has changed significantly and the scale of investment, groups and companies involved in building quantum computers has increased exponentially. Architectural design for quantum computers examines systems at scale: fully error-corrected machines, potentially consisting of millions if not billions of physical qubits. These designs increasingly act as blueprints for academic groups and companies and are becoming increasingly more detailed, taking into account both the nature and operation of the physical qubits themselves and also peripheral environmental and control infrastructure that is required for each physical system. In this paper, several architectural structures that I have worked on will be reviewed, each of which has been adopted by either a national quantum computing program or a quantum startup.
Building a quantum computer is not only about having qubits or quantum processors, analogously to classical computers are not only based on transistors or integrated circuits. Accordingly, in the very recent times, a few unique research groups in academia and companies have been exploring and proposing high-level and low-level programming languages, compilers, microarchitecture and instruction set architectures for quantum computation as well as different computer architecture approaches. In other words, developing a quantum computer requires bridging quantum applications and quantum devices. This talk will address the main challenges of building a scalable full-stack quantum computing system followed by a discussion on its architecture focusing on fault tolerance and compilation of quantum algorithms on NISQ devices. I will also provide my vision on how the research community could accelerate the process towards building such a scalable quantum machine, potentially through vertical cross-layer co-design structured methodologies.
This paper presents a comprehensive review of quantum computing and the technological foundations of quantum processors. Quantum computing represents a transformative computational paradigm that leverages the fundamental principles of quantum mechanics—such as superposition, entanglement, and quantum interference—to solve complex problems that are intractable for classical computers. At the core of this paradigm lies the quantum processor, a specialized hardware system designed to initialize, control, and measure quantum bits (qubits) through precise gate operations and coherent interactions. The survey examines the essential theoretical concepts underlying quantum computation and provides a structured comparison of leading quantum processor platforms. Prominent architectures, including superconducting circuits, trapped-ion systems, and photonic implementations, are analyzed in terms of their physical mechanisms, scalability, coherence properties, and engineering challenges. Each platform offers distinct advantages and faces specific technical limitations related to noise, error correction, and fabrication complexity. In addition to reviewing hardware developments, this study highlights key obstacles in advancing quantum processors toward large-scale, fault-tolerant systems. Issues such as qubit stability, error mitigation, thermal management, and system integration are discussed to present a realistic perspective on current progress. Performance indicators including gate fidelity, coherence time, connectivity, and scalability are used to compare architectural approaches. The objective of this paper is to provide a clear and organized synthesis of recent advancements in quantum computing hardware, thereby offering researchers and practitioners a foundational reference to support continued innovation and development in this rapidly evolving field.
In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits. Nevertheless, there are significant outstanding challenges in quantum hardware, fabrication, software architecture, and algorithms on the path towards a full-stack scalable quantum computing technology. Here, we provide a comprehensive review of these scaling challenges. We show how to facilitate scaling by adopting existing semiconductor technology to build much higher-quality qubits, employing systems engineering approaches, and performing distributed heterogeneous quantum-classical computing. We provide a detailed resource and sensitivity analysis for quantum applications on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. We provide comprehensive resource estimates for several utility-scale applications including quantum chemistry calculations, catalyst design, NMR spectroscopy, and Fermi-Hubbard simulation. We show that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration. Furthermore, we introduce high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical optimization, machine learning, and quantum simulation tasks in a cost-effective manner.
Summary form only given. Last year saw the first quantum computers realized that can be programmed from a high level user interface to run arbitrary quantum algorithms [1, 2]. While these devices are still small in scale, only comprising a handful of qubits each, they nevertheless allow the implementation of quantum circuits in a way that is basically blind to the underlying hardware itself. This constitutes a new level of development in quantum computer technology for the two leading approaches, trapped atomic ions [1] and superconducting circuits [3]. It also gives us the opportunity to test quantum computers irrespective of their particular physical implementation for the first time. With multiple companies (IBM, Google, Microsoft, as well as several start-ups) aiming for a larger scale device of commercial viability in the near future, benchmarking quantum computers becomes a crucial pursuit and this work is a step in that direction. Our device consists of five171Yb+atomic ions trapped in a Paul trap [1]. Each provides a pristine “atomic clock” qubit in its atomic structure that can be manipulated with laser light and read out via state-dependent fluorescence. Two-qubit gates between any pair are implemented via laser-mediated interaction of qubits through the normal modes of motion in the trap [4] making it a fully connected system.
The global race to build the world's first quantum computer has attracted enormous investment from government and industry, and it attracts a growing pool of talent. As with many cutting-edge technologies, the optimal implementation is not yet settled. This important textbook describes four of the most advanced platforms for quantum computing: nuclear magnetic resonance, quantum optics, trapped ions, and superconducting systems. The fundamental physical concepts underpinning the practical implementation of quantum computing are reviewed, followed by a balanced analysis of the strengths and weaknesses inherent to each type of hardware. The text includes more than 80 carefully designed exercises with worked solutions available to instructors, applied problems from key scenarios, and suggestions for further reading, facilitating a practical and expansive learning experience. Suitable for senior undergraduate and graduate students in physics, engineering, and computer science, Building Quantum Computers is an invaluable resource for this emerging field.
Quantum computation exploits superposition, entanglement, interference, and measurement to process information.Research has moved from theoretical algorithms to functional devices where the performance is not only limited by qubit physics, but also by the control, compilation, error mitigation, and error correction.This review considers quantum computing on various levels of abstraction, including physical qubits, quantum algorithms, system architecture, fault-tolerance, and emerging applications.Study-specific evidence regarding scale, operational quality, connectivity, control and suppression of errors is compared across the different types of superconducting circuits, trapped ions, semiconductor spin qubits, neutral atoms and photonic processors.The review of the fundamental algorithms, noisy intermediate-scale quantum computing, variational and hybrid quantum-classical approaches, quantum simulation, quantum machine learning, and security implications is also discussed.Measurable progress has been demonstrated in published experiments for logical-qubit operation, below-threshold surface-code memory, programmable photonic sampling and platformspecific scaling.However, the reported metrics are not directly interchangeable, and practical advantage remains task-and baseline-dependent.Further progress will need to be accompanied by coordinated developments in hardware reliability, error correction, classical control, estimates of algorithmic resources, and clear classical method comparisons.