How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits (2411.10406v2)

Abstract: 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 and proof-of-principle error-correction on a single logical qubit. Nevertheless, despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation/learning of quantum data generated by natural or engineered quantum systems. To estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations 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. Furthermore, we argue that, to tackle industry-scale classical optimization and machine learning problems in a cost-effective manner, heterogeneous quantum-probabilistic computing with custom-designed accelerators should be considered as a complementary path toward scalability.

• The paper investigates the roadmap for building utility-scale quantum supercomputers by analyzing scaling challenges and opportunities in hardware, error correction, and integration.
• Key areas for advancement include enhancing qubit fabrication for scalable integration, achieving higher coherence times and lower error rates, and developing robust error correction architectures.
• Realizing practical quantum supercomputers requires integrating quantum systems with classical high-performance computing and optimizing algorithms through hardware-software co-design.

Quantum Supercomputing: Challenges and Opportunities in Scaling

The paper "How to Build a Quantum Supercomputer: Scaling Challenges and Opportunities" presents a comprehensive investigation of the hurdles and possibilities in the evolution of quantum computing technology. As the quantum computing field transitions from small-scale experimental systems towards potentially achieving utility-scale quantum hardware, the authors assess the trajectory for building viable quantum supercomputers. They highlight the existing semiconductor technologies and suggest system engineering approaches as potential pathways for overcoming current obstacles.

Key Areas and Numerical Highlights

The authors delineate several crucial domains within quantum computing where progress is essential for the realization of large-scale quantum systems:

1. Quantum Hardware and Fabrication: The paper illustrates the need for enhanced qubit design and fabrication techniques to reduce error rates. Superconducting qubits are highlighted as the present frontrunners, with coherence times targeted at over 200µs for near-term objectives. These improvements align with achieving the desired logical error rates indispensable for FTQC. The analysis emphasizes how fabrication advancements, notably in scalable wafer-scale integration and high-density interconnects, are crucial for scaling qubit numbers to the millions required for utility-scale applications.
2. Error Correction and Architecture: The implementation of Fault-Tolerant Quantum Computing (FTQC) hinges on reducing logical errors further via sophisticated error correction codes like surface codes. The authors present a detailed evaluation, noting orders of magnitude improvements in logical error rates due to advancements in algorithms and system architectures. The presented logic infidelity models suggest that performance could be significantly enhanced by focusing on gate fidelity over SPAM errors.
3. Quantum-HPC Integration: Successful scaling also involves integrating quantum computing into high-performance computing (HPC) environments. The authors propose a hybrid system framework combining classical and quantum resources for executing quantum algorithms more efficiently. For instance, techniques such as Adaptive Circuit Knitting (ACK) are investigated to distribute workload across multiple quantum and classical processors, aiming to sidestep the classical computational bottleneck of circuit knitting.
4. Resource Estimation for Quantum Algorithms: The empirical studies cited provide resource estimation for performing electronic structure quantum simulations using techniques like Trotterization and qubitization. Achieving chemically accurate ground state energy simulations for compounds like p-benzyne and FeMoco demands millions of qubits and significant runtime unless hardware quality is vastly improved. The results emphasize the critical role of optimizing qubit counts and gate operations in reducing computational costs.

Future Directions and Implications

The paper suggests that for quantum computers to transition into being practical and economically viable—termed "utility-scale"—a multilateral effort in both theory and engineering is essential. A crucial component lies in furthering the semiconductor-quantum synergies and utilizing existing semiconductor supply chains to mitigate costs. The authors call for innovative approaches in both algorithm design and hardware-software co-optimization to accelerate traditionally perceived Moore's law-like scaling in quantum technology. Key proposed developments include high-fidelity gates, efficient system-scale calibration, and the realization of robust near-term quantum applications.

Conclusion

Overall, the paper articulates a vision of the roadmap to quantum supercomputing, emphasizing the blend of technological advancement and academic inquiry. It underscores the necessity for both scalable quantum frameworks and the utilization of high-performance data architectures, pushing the boundary for the eventual realization of distributed quantum supercomputers. While the challenges ahead are non-trivial, the opportunities offered in quantum simulation, optimization, and more pave a promising path forward.