Solving Deadlocks in Quantum Network Nodes: A Comparison of Opposing Strategies

Abstract

Quantum networks provide functionality not found in classical networks and realizing their potential requires nodes to execute quantum applications reliably.
Qoala, an execution environment for hybrid quantum-classical applications, runs several program instances concurrently on a single node, where they contend for a limited pool of qubits.
Because qubits are held with exclusive access and cannot be preempted into classical storage without destroying their state, the conditions for deadlock arise, possibly leaving programs unable to proceed.
In this thesis, we implement and compare the three established approaches to the deadlock problem, detection with recovery, prevention, and avoidance, in Qoala.
Waiting carries costs beyond execution time, because qubits in memory decohere.
A blocked program is less likely to succeed, and terminating a program to free up resources discards entanglement that is slow to generate.
We evaluate the strategies on classical and quantum metrics across workloads, study how they scale across various configuration, and introduce a Deadlock Impact Score whose coefficients tune recovery to prioritize either runtime or qubit decoherence during termination.
We find that, with a network schedule, success probabilities are largely similar across strategies, typically within about one percentage point, while avoidance consistently achieves the lowest makespan.
Without a network schedule, success probabilities vary more, by up to 13 percentage points, and prevention performs best, with a makespan comparable to avoidance.
This ordering was broadly stable across the network configurations we tested, though not without exceptions.
Our work enables concurrent execution of workloads in Qoala that previously had to run sequentially and opens the door to further research on concurrency in quantum network nodes.