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This paper introduces a formal framework for multiprogramming in fault-tolerant quantum computing (FTQC) architectures, accounting for the structured floorplan of data, ancilla, and magic-state resources. They address challenges like floorplan fragmentation, ancilla wastage, and resource contention that arise in FTQC multiprogramming. Through simulations with Clifford+T workloads, their proposed hierarchy-aware scheduler achieves a 3.1x normalized system speedup, a 29% improvement over existing FTQC multiprogramming approaches.
FTQC multiprogramming is not just about qubit partitioning; it's a complex puzzle of structured floorplans, resource contention, and dynamic magic-state generation, and this work provides a framework to solve it.
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.
