How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits
Masoud Mohseni, Artur Scherer, K. Grace Johnson, Oded Wertheim, Matthew Otten, Namit Anand, Navid Anjum Aadit, Yuri Alexeev, Gilad Ben-Shach, Kirk M. Bresniker, Kerem Y. Camsari, Barbara Chapman, Soumitra Chatterjee, Shuvro Chowdhury, Gebremedhin A. Dagnew, Tom Dvir, Aniello Esposito, Farah Fahim, Michael Ferguson, Marco Fiorentino, Archit Gajjar, Katerina Gratsea, Gaurav Gyawali, Christian Heiter, Ali H. Z. Kavaki, Abdullah Khalid, Xiangzhou Kong, Bohdan Kulchytskyy, Elica Kyoseva, Ruoyu Li, P. Aaron Lott, Igor L. Markov, Robert F. McDermott, Lucas Morais, Giacomo Pedretti, Pooja Rao, Eleanor Rieffel, 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
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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.