No Tile Left Behind: Multiprogramming for Surface-Code Architectures

TL;DR

This paper introduces a formal framework and scheduling policies for multiprogramming in fault-tolerant quantum computing architectures, addressing structural constraints to improve throughput and resource utilization.

Contribution

It develops a comprehensive framework for FTQC multiprogramming, including static, limited-resource, online, and dynamic magic-state generation scenarios, with a novel scheduler that enhances performance.

Findings

Scheduler achieves 3.1x system speedup in simulations.

Improves over prior baselines by approximately 29%.

Maintains low mean slowdown while increasing throughput.

Abstract

Fault-tolerant quantum computing (FTQC) is emerging as the architectural regime in which practical large-scale quantum workloads will execute. In this setting, however, multiprogramming is no longer a matter of partitioning a flat pool of qubits. Quantum error correction exposes a structured floorplan of data tiles, ancilla tiles, and magic-state service resources, so concurrent execution must account for compact placement, connectivity, routing headroom, and shared support infrastructure. This makes FTQC multiprogramming fundamentally harder than its NISQ counterpart: admission decisions can fragment the remaining floorplan, conservative reservations can waste ancilla, and dynamic contention across data, ancilla, and magic-state resources can degrade both throughput and quality of service. In this work, we develop a formal framework for FTQC multiprogramming that captures these structural constraints and their runtime implications. We formulate the baseline static allocation problem, extend it to limited-resource and online settings through hierarchy-aware scheduling policies, and further generalize it to cultivation-enabled architectures with dynamic magic-state generation. Through simulation on synthetic Clifford+T workloads, the proposed scheduler achieves a normalized system speedup of 3.1x, improving over prior FTQC multiprogramming baselines by ~29% while maintaining low mean slowdown.