Q-Profile: Profiling Tool for quantum control stacks applied to the Quantum Approximate Optimization Algorithm

Current variational quantum algorithm implementation runtimes are dominated by classical overhead. Profiling quantum control stacks is an essential step towards mitigating these bottlenecks. However, existing benchmark suites only provide highly abstracted runtime assessment. In this work, we present Q-Profile, an open-source and hardware-agnostic tool to profile quantum control stacks. It uses direct access to the control stack, providing high accuracy in identifying performance bottlenecks in the steps of classical optimization, compilation, communication and quantum circuit execution. We demonstrate the use of our tool by profiling the execution of QAOA on a Qblox Cluster for a simulated 5 to 14-qubit transmon system. Our results identify the major execution bottlenecks in the communication and qubit reset. We provide both demonstrated and expected performance gains by implementing parallel initialization of the hardware modules and implementing active qubit reset. Furthermore, we predict the performance scaling up to 400 qubits. The tool is applicable to other benchmarks and it is included in the open-source quantify-scheduler quantum control software which supports multiple hardware back-ends.