Curator's Take
This research tackles one of quantum computing's most pressing infrastructure challenges: how to efficiently schedule and execute quantum jobs across multiple connected quantum processors as the field scales beyond single-chip limitations. The work addresses a critical gap as hardware vendors like IBM and IonQ move toward modular architectures where large quantum circuits must be split and coordinated across separate QPUs connected through classical or quantum links. The proposed QuMod scheduler is particularly significant because it considers the complex interplay of circuit cutting, teleportation operations, and measurement synchronization that become essential when quantum workloads span multiple processors. As quantum cloud services mature and support multiple users sharing modular quantum systems, this type of intelligent resource management will be crucial for making large-scale quantum computing practical and economically viable.
— Mark Eatherly
Summary
The quantum computing community is increasingly positioning quantum processors as accelerators within classical HPC workflows, analogous to GPUs and TPUs. However, many real-world applications require scaling to hundreds or thousands of physical qubits to realize logical qubits via error correction. To reach these scales, hardware vendors employing diverse technologies -- such as trapped ions, photonics, neutral atoms, and superconducting circuits -- are moving beyond single, monolithic QPUs toward modular architectures connected via interconnects. For example, IonQ has proposed photonic links for scaling, while IBM has demonstrated a modular QPU architecture by classically linking two 127-qubit devices. Using dynamic circuits, Bell-pair-based teleportation, and circuit cutting, they have shown how to execute a large quantum circuit that cannot fit on a single QPU. As interest in quantum computing grows, cloud providers must ensure fair and efficient resource allocation for multiple users sharing such modular systems. Classical interconnection of QPUs introduces new scheduling challenges, particularly when multiple jobs execute in parallel. In this work, we develop a multi-programmable scheduler for modular quantum systems that jointly considers qubit mapping, parallel circuit execution, measurement synchronization across subcircuits, and teleportation operations between QPUs using dynamic circuits.