Improving Performance and Debuggability of Variational Quantum Algorithms using Compressed Sensing

Variational quantum algorithms (VQAs) have the potential to solve practical problems using contemporary Noisy Intermediate Scale Quantum (NISQ) computers in the near term. VQAs find near-optimal solutions in the presence of qubit errors by classically optimizing a loss function computed by parameterized quantum circuits. However, developing and testing VQAs is challenging due to the limited availability of quantum hardware, their high error rates, and the significant overhead of classical simulations. Furthermore, VQA researchers must pick the right initialization for circuit parameters, utilize suitable classical optimizer configurations, and deploy appropriate error mitigation methods. Unfortunately, these tasks are done in an ad-hoc manner today, as there are no software tools to configure and tune the VQA hyperparameters.

We present OSCAR (cOmpressed Sensing based Cost lAndscape Reconstruction) to help researchers configure: 1) correct initialization, 2) noise mitigation techniques, and 3) classical optimizers to avoid barren plateaus and maximize the quality of the solution on NISQ hardware. OSCAR enables efficient debugging and performance tuning by providing users with the loss function landscape without running millions of quantum circuits as required by the standard grid search method. By using the compressed sensing technique, we can accurately reconstruct the complete cost function landscape with a 20X to 100X speedup for the Quantum Approximate Optimization Algorithm (QAOA). Furthermore, OSCAR can compute an optimizer function query in an instant by interpolating a computed landscape, thus enabling the trial run of a QAOA configuration with considerably reduced overhead.