How to Build a Quantum Supercomputer: Scaling Challenges and Opportunities

• URL: http://arxiv.org/abs/2411.10406v1
• Date: Fri, 15 Nov 2024 18:22:46 GMT
• Title: How to Build a Quantum Supercomputer: Scaling Challenges and Opportunities
• Authors: Masoud Mohseni, Artur Scherer, K. Grace Johnson, Oded Wertheim, Matthew Otten, Navid Anjum Aadit, Kirk M. Bresniker, Kerem Y. Camsari, Barbara Chapman, Soumitra Chatterjee, Gebremedhin A. Dagnew, Aniello Esposito, Farah Fahim, Marco Fiorentino, Abdullah Khalid, Xiangzhou Kong, Bohdan Kulchytskyy, Ruoyu Li, P. Aaron Lott, Igor L. Markov, Robert F. McDermott, Giacomo Pedretti, Archit Gajjar, Allyson Silva, John Sorebo, Panagiotis Spentzouris, Ziv Steiner, Boyan Torosov, Davide Venturelli, Robert J. Visser, Zak Webb, Xin Zhan, Yonatan Cohen, Pooya Ronagh, Alan Ho, Raymond G. Beausoleil, John M. Martinis,
• Abstract summary: Small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits. Despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits.
• Score: 3.864855748348313
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• 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, distributed quantum-assisted probabilistic computing with custom-designed accelerators should be considered as a complementary path toward scalability.

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