Robust and efficient algorithms for high-dimensional black-box quantum optimization

2019/10/08 by Zhaoqi Leng, Pranav Mundada, Leng, Zhaoqi +5 · 6 citations
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Applied Physics (physics.app-ph) #Artificial intelligence #Black box #Computer science #FOS: Physical sciences #Mathematical optimization #Mathematics #Optimization algorithm #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Mechanics and Applications #Quantum Physics (quant-ph) #Quantum mechanics #physics.app-ph #quant-ph

paper · pdf · doi:10.48550/arxiv.1910.03591

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2019/10/08 · arxiv created 2019/10/10 · arxiv updated 2019/10/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Hybrid quantum-classical optimization using near-term quantum technology is an emerging direction for exploring quantum advantage in high-dimensional systems. However, precise characterization of all experimental parameters is often impractical and challenging. A viable approach is to use algorithms that rely only on black-box inference rather than analytical gradients. Here, we combine randomized perturbation gradient estimation with adaptive momentum gradient updates to create the AdamSPSA and AdamRSGF algorithms. We prove the asymptotic convergence of our algorithms in a convex setting, and we benchmark them against other gradient-based optimization algorithms on non-convex optimal control tasks. Our results show that these new algorithms accelerate the convergence rate, decrease the variance of loss trajectories, and efficiently tune up high-fidelity (above 99.9%) Hann-window single-qubit gates from trivial initial conditions with twenty variables.

Citations

• ADADELTA: An Adaptive Learning Rate Method
• Learning representations by back-propagating errors
• Adam: A Method for Stochastic Optimization
• Equilibrated adaptive learning rates for non-convex optimization
• Quantum computing and the entanglement frontier
• Stochastic Estimation of the Maximum of a Regression Function
• Fluxonium: Single Cooper-Pair Circuit Free of Charge Offsets
• Simple Pulses for Elimination of Leakage in Weakly Nonlinear Qubits
• Characterizing quantum supremacy in near-term devices
• A Lyapunov Analysis of Momentum Methods in Optimization
• Quantum Machine Learning
• A blueprint for demonstrating quantum supremacy with superconducting qubits
• Unsupervised Machine Learning on a Hybrid Quantum Computer
• Quantum Computing in the NISQ era and beyond
• On the Convergence of Adam and Beyond
• Quantum Convolutional Neural Networks

Cited by

• Preconditioning Natural and Second Order Gradient Descent in Quantum Optimization: A Performance Benchmark

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