Natural Evolutionary Strategies for Variational Quantum Computation

Abstract

Natural evolutionary strategies (NES) are a family of gradient-free black-box optimization algorithms. This study illustrates their use for the optimization of randomly-initialized parametrized quantum circuits (PQCs) in the region of vanishing gradients. We show that using the NES gradient estimator the exponential decrease in variance can be alleviated. We implement two specific approaches, the exponential and separable natural evolutionary strategies, for parameter optimization of PQCs and compare them against standard gradient descent. We apply them to two different problems of ground state energy estimation using variational quantum eigensolver (VQE) and state preparation with circuits of varying depth and length. We also introduce batch optimization for circuits with larger depth to extend the use of evolutionary strategies to a larger number of parameters. We achieve accuracy comparable to state-of-the-art optimization techniques in all the above cases with a lower number of circuit evaluations. Our empirical results indicate that one can use NES as a hybrid tool in tandem with other gradient-based methods for optimization of deep quantum circuits in regions with vanishing gradients.

References in corpus (33)

• Quantum Computing in the NISQ era and beyond
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• A variational eigenvalue solver on a quantum processor
• Barren plateaus in quantum neural network training landscapes
• The theory of variational hybrid quantum-classical algorithms
• A Quantum Approximate Optimization Algorithm
• Quantum computational chemistry
• Quantum Chemistry in the Age of Quantum Computing
• Circuit-centric quantum classifiers
• Evolution Strategies as a Scalable Alternative to Reinforcement Learning
• Noise-Induced Barren Plateaus in Variational Quantum Algorithms
• Data re-uploading for a universal quantum classifier
• Quantum generative adversarial learning
• Quantum generative adversarial networks
• Quantum autoencoders for efficient compression of quantum data
• Direct visualization of current induced spin accumulation in topological insulators
• Layerwise learning for quantum neural networks
• Qulacs: a fast and versatile quantum circuit simulator for research purpose
• Hierarchical quantum classifiers
• Quantum generalisation of feedforward neural networks
• Training of Quantum Circuits on a Hybrid Quantum Computer
• Deploying a Top-100 Supercomputer for Large Parallel Workloads: the Niagara Supercomputer
• Quantum generative adversarial learning in a superconducting quantum circuit
• Large gradients via correlation in random parameterized quantum circuits
• Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning
• Learning to learn with quantum neural networks via classical neural networks
• The Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation
• Tequila: A platform for rapid development of quantum algorithms
• Adaptive pruning-based optimization of parameterized quantum circuits
• Demonstration of quantum volume 64 on a superconducting quantum computing system
• Quantum Computer-Aided design of Quantum Optics Hardware
• Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies
• Natural evolution strategies and variational Monte Carlo

Cited by in corpus (27)

• Effect of barren plateaus on gradient-free optimization
• Artificial Intelligence and Machine Learning for Quantum Technologies
• Entanglement Devised Barren Plateau Mitigation
• Variational Quantum Reinforcement Learning via Evolutionary Optimization
• Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation
• A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
• Automatic design of quantum feature maps
• AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images
• Variational thermal quantum simulation of the lattice Schwinger model
• Benchmarking variational quantum eigensolvers for the square-octagon-lattice Kitaev model
• Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models
• Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction
• The quantum cost function concentration dependency on the parametrization expressivity
• Quantum Circuit Evolution on NISQ Devices
• Evolution strategies: Application in hybrid quantum-classical neural networks
• VQE Method: A Short Survey and Recent Developments
• Coarse grained intermolecular interactions on quantum processors
• Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks
• Random coordinate descent: a simple alternative for optimizing parameterized quantum circuits
• Random Natural Gradient
• Hamiltonian-based graph-state ansatz for variational quantum algorithms
• Learning to learn with an evolutionary strategy applied to variational quantum algorithms
• Barren plateaus are amplified by the dimension of qudits
• Stabilizer configuration interaction: Finding molecular subspaces with error detection properties
• Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
• A variational quantum algorithm for entanglement quantification
• Unbiased observable estimation with approximate channels in fault-tolerant quantum computation