How to Build a Quantum Supercomputer: Scaling Challenges and Opportunities

The paper titled "How to Build a Quantum Supercomputer: Scaling Challenges and Opportunities" discusses the evolution of quantum computation over the past four decades, highlighting its transition from a theoretical concept to a feasible technology. The authors note that while small-scale demonstrations of quantum algorithms and error correction have been achieved, significant challenges remain in scaling quantum technology to a utility level. These challenges encompass hardware, fabrication, software architecture, and algorithmic issues that could hinder progress. The authors propose leveraging existing semiconductor technology to enhance qubit quality, employing system engineering methods, and utilizing distributed quantum computation within high-performance computing frameworks as potential solutions. They emphasize the importance of research and development in these areas to unlock applications such as efficient quantum simulations and machine learning. Additionally, the paper provides a resource and sensitivity analysis for quantum chemistry calculations on error-corrected quantum computers, considering realistic error distributions. The authors advocate for distributed quantum-assisted probabilistic computing with custom accelerators as a complementary approach to address large-scale classical optimization and machine learning challenges.

- Quantum computation has progressed from theory to practical demonstrations over 40 years.

- Significant scaling challenges in hardware, software, and algorithms remain unresolved.

- Proposed solutions include using semiconductor technology and distributed quantum computation.

- The paper includes a resource analysis for quantum chemistry calculations on superconducting qubits.

- Distributed quantum-assisted computing is suggested as a way to tackle classical optimization problems.