Computer Science Challenges in Quantum Computing: Early Fault-Tolerance and Beyond

TL;DR

The paper argues that the arrival of early fault-tolerant quantum computing will pivot bottlenecks from hardware scale to computer science challenges across algorithms, error correction, software, and architecture. It advocates a co-design, verification-driven approach, with a strong emphasis on end-to-end benchmarks, modular abstractions, and domain-specific architectures to enable practical early usefulness. By framing progress as structured learning, the work outlines concrete milestones in QEC design automation, software tooling, and scalable architectures that will shape future quantum systems. The study highlights cross-cutting themes such as verification, heterogeneity, and automation, underscoring their central role in achieving reliable, scalable quantum computation before full fault-tolerance is achieved.

Abstract

Quantum computing is entering a period in which progress will be shaped as much by advances in computer science as by improvements in hardware. The central thesis of this report is that early fault-tolerant quantum computing shifts many of the primary bottlenecks from device physics alone to computer-science-driven system design, integration, and evaluation. While large-scale, fully fault-tolerant quantum computers remain a long-term objective, near- and medium-term systems will support early fault-tolerant computation with small numbers of logical qubits and tight constraints on error rates, connectivity, latency, and classical control. How effectively such systems can be used will depend on advances across algorithms, error correction, software, and architecture. This report identifies key research challenges for computer scientists and organizes them around these four areas, each centered on a fundamental question.