Q-Profile: Profiling Tool for Quantum Control Stacks applied to the Quantum Approximate Optimization Algorithm

Abstract

Scaling up the number of qubits and speeding up the execution of quantum algorithms are important steps towards reaching quantum advantage. This poses heavy demands particularly on the control stack, as pulses need to be distributed to an increasing number of control channels and variational algorithms require rapid interleaving of quantum and classical computation. Assessing the bottlenecks in the control stack is therefore key to making it ready for reaching quantum advantage. However, existing benchmark suites suffer from lack of detail due to indirect access to the control hardware. In this work, we present Q-Profile, a tool to profile quantum control stacks that circumvents these issues by utilizing a direct connection from the host CPU to the control stack, providing fine accuracy in measuring the runtime and allowing to identify performance bottlenecks. We demonstrate the use of our tool by benchmarking the Quantum Approximate Optimization Algorithm (QAOA) on a Qblox Cluster for a virtual 4 to 14-qubit transmon system. Our results identify the major execution bottlenecks in the passive qubit reset and communication overhead. We estimate a 1.40x~speedup with respect to the total runtime by using an active qubit reset, instead of passive reset, and demonstrate a further speedup of 1.37x by parallel initialization of the control modules. The presented method of profiling is applicable to other control-stack providers, as well as to other benchmarks, while still providing detailed information beyond a single metric. By extension, this tool will enable identifying and eliminating bottlenecks for future quantum acceleration. The profiling tool is included in the open-source Quantify quantum control software, which allows support for multiple back-ends.