Robust and efficient algorithms for high-dimensional black-box quantum optimization
arXiv:1910.03591
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.
References in corpus (27)
- Adam: A Method for Stochastic Optimization
- Quantum Computing in the NISQ era and beyond
- ADADELTA: An Adaptive Learning Rate Method
- Quantum Machine Learning
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- On the Convergence of Adam and Beyond
- Quantum Convolutional Neural Networks
- Characterizing Quantum Supremacy in Near-Term Devices
- Simple pulses for elimination of leakage in weakly nonlinear qubits
- Fluxonium: single Cooper pair circuit free of charge offsets
- Demonstration of a small programmable quantum computer with atomic qubits
- Validating quantum computers using randomized model circuits
- Benchmarking an 11-qubit quantum computer
- A blueprint for demonstrating quantum supremacy with superconducting qubits
- Efficient measurement of quantum gate error by interleaved randomized benchmarking
- Observation of topological phenomena in a programmable lattice of 1,800 qubits
- Optimal quantum control using randomized benchmarking
- Unsupervised Machine Learning on a Hybrid Quantum Computer
- Training of Quantum Circuits on a Hybrid Quantum Computer
- Quantum computing and the entanglement frontier
- Characterizing errors on qubit operations via iterative randomized benchmarking
- Equilibrated adaptive learning rates for non-convex optimization
- Reduced phase error through optimized control of a superconducting qubit
- A Lyapunov Analysis of Momentum Methods in Optimization
- Simultaneous gates in frequency-crowded multilevel systems using fast, robust, analytic control shapes
- Control and Coherence Time Enhancement of the 0- Qubit
- Experimental realization of self-guided quantum process tomography