2020/04/30 by Wim Lavrijsen, W. Lavrijsen, Ana Tudor +4 · 101 citations
Computer Science · Engineering · Physics and Astronomy · #Algorithm #Artificial intelligence #Black box #Computer engineering #Computer science #Distributed computing #Fault tolerance #Hyperparameter #Low-power high-performance VLSI design #Noise (video) #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum algorithm #Quantum circuit #Quantum computer #Quantum error correction #Scale (ratio) #quant-ph
paper · pdf · doi:10.1109/qce49297.2020.00041
11 pages, 17 figures
openalex publication_date 2020/10/01 · arxiv created 2021/04/15 · arxiv updated 2021/04/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We present a collection of optimizers tuned for usage on Noisy Intermediate-Scale Quantum (NISQ) devices. Optimizers have a range of applications in quantum computing, including the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization (QAOA) algorithms. They are also used for calibration tasks, hyperparameter tuning, in machine learning, etc. We analyze the efficiency and effectiveness of different optimizers in a VQE case study. VQE is a hybrid algorithm, with a classical minimizer step driving the next evaluation on the quantum processor. While most results to date concentrated on tuning the quantum VQE circuit, we show that, in the presence of quantum noise, the classical minimizer step needs to be carefully chosen to obtain correct results. We explore state-of-the-art gradient-free optimizers capable of handling noisy, black-box, cost functions and stress-test them using a quantum circuit simulation environment with noise injection capabilities on individual gates. Our results indicate that specifically tuned optimizers are crucial to obtaining valid science results on NISQ hardware, and will likely remain necessary even for future fault tolerant circuits.