How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

2024-11-15

AI Summary

Building a scalable, fault-tolerant quantum supercomputer with one million physical qubits faces three fundamental bottlenecks: insufficient physical qubit fidelity, limitations in distributed interconnectivity, and inefficiencies in quantum-classical heterogeneous coordination. Method: This work proposes a tripartite co-scaling paradigm—semiconductor-process enhancement, distributed quantum computing, and quantum-probabilistic heterogeneous acceleration—integrating surface-code-based fault-tolerant architecture modeling, superconducting hardware simulation, resource-sensitivity analysis, and full-stack quantum-classical integration, validated on representative quantum chemistry algorithms. Contribution/Results: For the first time, we perform end-to-end resource estimation under realistic error distributions, establishing feasibility boundaries and a quantitative cost model for million-qubit fault-tolerant systems. Our results provide a verifiable engineering pathway and quantified design benchmarks for large-scale quantum computing deployment.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Scaling quantum computers to millions of qubits
• Overcoming hardware and software architecture challenges
• Enabling efficient quantum simulation and learning

Innovation

• Semiconductor technology for qubits
• Distributed quantum computation systems
• Heterogeneous quantum-probabilistic computing