Scaling quantum computing systems from today's 27-127-qubit NISQ devices to the thousands of error-corrected logical qubits required for fault-tolerant quantum advantage demands architectural innovations across the full hardware stack -- qubit technology, connectivity, control electronics, error correction codes, and classical co-processing. This paper proposes the Quantum System Architecture Design (QSAD) framework, a structured methodology for evaluating and comparing quantum computing architectures across five design dimensions: qubit technology (superconducting, trapped-ion, photonic, neutral atom, spin); interconnect topology; error correction code selection; classical co-processor interface; and software-hardware co-design. QSAD is applied to evaluate six current quantum computing architectures -- IBM Heron, IonQ Forte, Google Willow, QuEra Aquila, Intel Tunnel Falls, and Quantinuum H2 -- across 28 quantitative metrics. IBM Heron and Quantinuum H2 score highest on QSAD composite (0.848 and 0.864 respectively). QSAD identifies neutral atom arrays as the highest-potential scalability pathway for 2025-2030, with QuEra Aquila's reconfigurable connectivity and low crosstalk offering a clear route to 1,000+ qubit operation. An architectural roadmap to fault-tolerant quantum computing by 2030 is proposed.
Scaling quantum computing systems from today's 27-127-qubit NISQ devices to the thousands of error-corrected logical qubits required for fault-tolerant quantum advantage demands architectural innovations across the full hardware stack -- qubit technology, connectivity, control electronics, error correction codes, and classical co-processing. This paper proposes the Quantum System Architecture Design (QSAD) framework, a structured methodology for evaluating and comparing quantum computing architectures across five design dimensions: qubit technology (superconducting, trapped-ion, photonic, neutral atom, spin); interconnect topology; error correction code selection; classical co-processor interface; and software-hardware co-design. QSAD is applied to evaluate six current quantum computing architectures -- IBM Heron, IonQ Forte, Google Willow, QuEra Aquila, Intel Tunnel Falls, and Quantinuum H2 -- across 28 quantitative metrics. IBM Heron and Quantinuum H2 score highest on QSAD composite (0.848 and 0.864 respectively). QSAD identifies neutral atom arrays as the highest-potential scalability pathway for 2025-2030, with QuEra Aquila's reconfigurable connectivity and low crosstalk offering a clear route to 1,000+ qubit operation. An architectural roadmap to fault-tolerant quantum computing by 2030 is proposed.
The pursuit of a universal, fault-tolerant quantum computer has transformed quantum computing from a predominantly theoretical discipline into a rapidly diversifying hardware ecosystem. This review presents a comprehensive and technically grounded analysis of the principal physical qubit modalities: superconducting circuits, trapped ions, photonic platforms, neutral atoms, semiconductor spin qubits, and topological approaches, focusing on their underlying operating principles, performance characteristics, and system-level constraints. We examine how these qubit technologies are embedded within scalable quantum processor architectures, including control and readout infrastructures, connectivity topologies, and quantum error correction strategies. The review provides a comparative study of key performance metrics, such as coherence times, gate fidelities, error rates, and scalability, reveals fundamental trade-offs between speed, robustness, and operational complexity that shape distinct technological roadmaps and target applications. We further highlight the ongoing transition from noisy intermediate-scale quantum (NISQ) devices toward architectures centred on logical qubits, driven by advances in materials engineering, precision control, and quantum information theory. Together, these developments sketch the emerging routes toward fault-tolerant quantum computing and practical quantum advantage, while offering readers a coherent entry point and reference guide to the current quantum hardware landscape.
Quantum computing has progressed from early demonstrations across superconducting, trapped ion, photonic, and spin-based platforms to increasingly integrated systems. However, transitioning from the noisy intermediate-scale quantum (NISQ) era to robust, fault tolerant implementations remain a central challenge. This paper presents a systematic review and synthesis of practical quantum computing, encompassing experimental advances, integrated photonics, error correction breakthroughs, software stack evolution, benchmarking practices, algorithmic readiness, and socio-technical dimensions. We critically assess current limitations and highlight emerging directions, including integrated photonic modules with high fidelity interconnects, real time decoders for surface code logical qubits, and hardware aware compiler strategies. Our analysis is grounded in peer-reviewed experimental data and model-based evaluations. Key findings include demonstrated logical error suppression via increased code distance, scalable photonic module prototypes, and refined resource estimates for cryptanalytic and simulation tasks. This paper concludes with a structured research agenda for 2025–2030, proposing four priority thrusts such as hardware QEC co design, reproducible cross platform benchmarking, modular/distributed architectures, and quantum networking with governance frameworks. This evidence based roadmap aims to align researchers and practitioners toward actionable milestones on the path to practical quantum computing.
Quantum computing promises to revolutionize several scientific and industrial domains by tackling problems that are intractable for classical supercomputers. At the centre of this effort is the quantum processor unit (QPU), implemented today across multiple physical qubit platforms. This review provides a comparative analysis of the state of quantum processors as of late 2024/early 2025, focusing on superconducting qubits, trapped ions, photonic systems, neutral atoms, silicon spin qubits, and emerging topological approaches. We compile and compare recent processor generations such as IBM’s Heron family, Google’s updated Sycamore-class devices, Quantinuum’s H-series, IonQ’s Aria and Forte, neutral-atom platforms with more than 1,000 trapped atoms, and silicon spin prototypes like Tunnel Falls. Key performance metrics, including qubit count, gate and readout fidelity, coherence time, operation speed, and holistic benchmarks such as Quantum Volume (QV) and Algorithmic Qubits (#AQ) are examined with reference to concrete experimental milestones. We discuss algorithmic capabilities demonstrated on current hardware, early realizations of logical qubits and fault-tolerant primitives, and the distinction between feasibility-oriented “quantum advantage” experiments and emerging signs of practical “quantum utility”. Finally, we outline the main technical bottlenecks for each platform on the path from today’s NISQ devices toward universal fault-tolerant quantum computers.
The maturation of quantum computing from theoretical physics to engineering reality presents a complex landscape for stakeholders navigating the transition from Noisy Intermediate-Scale Quantum (NISQ) devices toward Fault-Tolerant Quantum Computation (FTQC). This report provides a comprehensive, technically rigorous analysis of the current quantum ecosystem, examining the hardware mosaic of leading qubit modalities—superconducting circuits, trapped ions, neutral atoms, and silicon spin qubits—and evaluating them against operational metrics, including coherence times, gate fidelities, and cryogenic infrastructure requirements. We dissect the full software stack, differentiating the near-term utility of error mitigation techniques such as Zero-Noise Extrapolation (ZNE) from the long-term necessity of Quantum Error Correction (QEC) and the milestone of logical qubit break-even. The analysis benchmarks hybrid algorithms (VQE, QAOA) and circuit knitting strategies against current cloud-accessible hardware performance, while also addressing the imminent non-computational impact of PostQuantum Cryptography (PQC) migration. Experimental data synthesised from leading vendor roadmaps and pre-print literature indicate that superconducting platforms currently lead in gate speed (nanosecond regime) but face crosstalk scaling challenges beyond 1,000 qubits. In contrast, trapped-ion and neutral-atom architectures demonstrate superior connectivity and coherence. The report concludes with a grounded, evidence-based five-year projection, identifying high-value vertical use cases in pharmaceuticals and finance, and providing a strategic framework for enterprises to separate quantum hype from tangible commercial opportunity. Future research directions include the realisation of low-overhead Quantum LDPC codes and the development of modular photonic interconnects to scale beyond single-chip architectures.
The transition from noisy intermediate-scale quantum (NISQ) processors to large-scale fault-tolerant machines requires more than incremental improvements in physical qubit fidelity. While advances in quantum error correction (QEC) theory continue to lower the theoretical overhead of quantum computing, much of this research remains abstracted from the physical realities of qubit implementations. This disconnect often results in hardware-agnostic designs that are inefficient or impractical to build. In this dissertation, I identify three primary gaps between theoretical QEC research and practical hardware: (1) the high levels of noise and temporal instability in experimental devices that impede scalable QEC; (2) the prohibitively large qubit and time costs of useful algorithms, driven largely by the limitations of the standard surface code; and (3) the failure of current resource estimates to account for hardware-level constraints, such as limited connectivity and the complexity of implementing non-local operations. To bridge these gaps, I propose a holistic, cross-stack approach where the error correction protocols and the physical hardware are designed in concert. By developing realistic device models and hardware-aware solutions at the lowest levels of the software stack, we can accelerate the path toward functional fault-tolerance. I present my work across three research thrusts: first, leveraging codesign to mitigate hardware-specific noise and reduce physical error rates; second, developing software tools for fault-tolerant quantum computers to reduce the space-time overhead of the QEC layer; and third, proposing two novel hardware-aware QEC architectures that provide practical, high-performance routes to scalable quantum computation.
Quantum chips form the foundational hardware for quantum computing, promising transformative impacts across optimization, secure communications, and artificial intelligence. This paper surveys five leading qubit implementations—superconducting circuits, neutral-atom arrays, trapped ions, photonic systems, and silicon-based quantum dots—highlighting their distinct operating principles, recent milestones, and inherent trade-offs. Superconducting platforms leverage mature microfabrication to achieve hundreds to thousands of qubits with rapid gate times but require millikelvin environments and face millisecond-scale coherence limits. Neutral-atom and ion-trap architectures offer exceptionally long coherence (seconds to hours) and high-fidelity operations, making them ideal for quantum memory and precision simulation, yet scaling remains constrained by control complexity. Photonic chips excel at room-temperature operation and quantum networking via entangled photons, though deterministic two-qubit gates and on-chip integration pose challenges. Silicon quantum dots promise CMOS compatibility and sub-microsecond spin coherence, but demand atomic-scale fabrication precision and advanced cryogenic control. We analyze the tension between qubit count, error rates, and engineering practicality, and discuss emerging solutions such as hybrid integration, advanced error-correction codes, and novel materials. Looking ahead, coordinated optimization across these platforms—combining fast superconducting processors, long-lived atomic memories, photonic interconnects, and scalable silicon arrays—will be critical to transition quantum chips from research prototypes to industrially viable systems that redefine computing, communications, and AI.