# Natural Evolutionary Strategies for Variational Quantum Computation

@article{Anand2021NaturalES, title={Natural Evolutionary Strategies for Variational Quantum Computation}, author={Abhinav Anand and Matthias Degroote and Al{\'a}n Aspuru-Guzik}, journal={ArXiv}, year={2021}, volume={abs/2012.00101} }

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…

## Figures and Tables from this paper

## 11 Citations

Entanglement devised barren plateau mitigation

- Computer SciencePhysical Review Research
- 2021

This work defines barren plateaus in terms of random entanglement and proposes and demonstrates a number of barren plateau ameliorating techniques, including initial partitioning of cost function and non-cost function registers, meta-learning of lowentanglement circuit initializations, selective inter-register interaction, entanglements regularization, and rotation into preferred cost function eigenbases.

Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks

- Computer ScienceMSML
- 2021

This work presents a hybrid policy gradient algorithm capable of simultaneously optimizing continuous and discrete degrees of freedom in an uncertainty-resilient way, modeled by a deep autoregressive neural network to capture causality.

Variational Quantum Algorithms

- Physics, Computer ScienceNature Reviews Physics
- 2021

An overview of the field of Variational Quantum Algorithms is presented and strategies to overcome their challenges as well as the exciting prospects for using them as a means to obtain quantum advantage are discussed.

My title

- Computer Science
- 2022

A new algorithm called MCTS-QAOA is proposed, which combines a Monte Carlo tree search method with an improved natural policy gradient solver to optimize the discrete and continuous variables in the quantum circuit, respectively and is found to have excellent noiseresilience properties and outperforms prior algorithms in challenging instances of the generalized QAOA.

Natural evolutionary strategies applied to quantum-classical hybrid neural networks

- Computer Science
- 2022

The NES method is applied to the binary classiﬁcation task, showing that this method is a viable alternative for training quantum neural networks and seeing that the phenomenon of gradient disappearance is also present in the NES method.

VQE method: a short survey and recent developments

- Computer ScienceMaterials Theory
- 2022

Recent developments in the field of designing efficient ansatzes that fall into two categories—chemistry–inspired and hardware–efficient—that produce quantum circuits that are easier to run on modern hardware are presented.

Automatic design of quantum feature maps

- Computer ScienceQuantum Science and Technology
- 2021

A new technique for the automatic generation of optimal adhoc ansätze for classification by using quantum support vector machine (QSVM) based on NSGA-II multiobjective genetic algorithms, which allow both maximize the accuracy and minimize the ansatz size.

Effect of barren plateaus on gradient-free optimization

- Computer ScienceQuantum
- 2021

It is shown that gradient-free optimizers do not solve the barren plateau problem, and the main result proves that cost function differences, which are the basis for making decisions in a gradient- free optimization, are exponentially suppressed in a barren plateau.

Variational quantum reinforcement learning via evolutionary optimization

- Computer Science, PhysicsMachine Learning: Science and Technology
- 2021

A hybrid framework is proposed where the quantum RL agents are equipped with a hybrid tensor network-variational quantum circuit (TN-VQC) architecture to handle inputs of dimensions exceeding the number of qubits, enabling further quantum RL applications on noisy intermediate-scale quantum devices.

Calculating nonadiabatic couplings and Berry's phase by variational quantum eigensolvers

- Physics
- 2020

Investigating systems in quantum chemistry and quantum many-body physics with the variational quantum eigensolver (VQE) is one of the most promising applications of forthcoming near-term quantum…

## References

SHOWING 1-10 OF 68 REFERENCES

Adaptive pruning-based optimization of parameterized quantum circuits

- Computer Science
- 2020

PECT can enable optimizations of certain ansatze that were previously difficult to converge and more generally can improve the performance of variational algorithms by reducing the optimization runtime and/or the depth of circuits that encode the solution candidate(s).

Natural evolution strategies and quantum approximate optimization

- Computer Science, PhysicsArXiv
- 2020

It is found that natural evolution strategies can achieve state-of-art approximation ratios for Max-Cut, at the expense of increased computation time.

The theory of variational hybrid quantum-classical algorithms

- Computer Science
- 2016

A quantum variational error suppression that allows some errors to be suppressed naturally in this algorithmon a pre-threshold quantumdevice is introduced and the use of modern derivative free optimization techniques can offer dramatic computational savings of up to three orders ofmagnitude over previously used optimization techniques.

Exponential natural evolution strategies

- Computer ScienceGECCO '10
- 2010

The new algorithm, exponential NES (xNES), is significantly simpler than its predecessors and is more principled than CMA-ES, as all the update rules needed for covariance matrix adaptation are derived from a single principle.

Barren plateaus in quantum neural network training landscapes

- Computer Science, PhysicsNature Communications
- 2018

It is shown that for a wide class of reasonable parameterized quantum circuits, the probability that the gradient along any reasonable direction is non-zero to some fixed precision is exponentially small as a function of the number of qubits.

Training of quantum circuits on a hybrid quantum computer

- Computer Science, PhysicsScience Advances
- 2019

This study trains generative modeling circuits on a quantum hybrid computer showing an optimization strategy and a resource trade-off and shows that the convergence of the quantum circuit to the target distribution depends critically on both the quantum hardware and classical optimization strategy.

Large gradients via correlation in random parameterized quantum circuits

- Computer Science
- 2020

It is proved that reducing the dimensionality of the parameter space by utilizing circuit modules containing spatially or temporally correlated gate layers can allow one to circumvent the vanishing gradient phenomenon.

Learning to learn with quantum neural networks via classical neural networks

- Computer ScienceArXiv
- 2019

This work trains classical recurrent neural networks to assist in the quantum learning process, also know as meta-learning, to rapidly find approximate optima in the parameter landscape for several classes of quantum variational algorithms.

Natural Evolution Strategies

- Computer Science2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
- 2008

NES is presented, a novel algorithm for performing real-valued dasiablack boxpsila function optimization: optimizing an unknown objective function where algorithm-selected function measurements constitute the only information accessible to the method.

Noise-induced barren plateaus in variational quantum algorithms

- PhysicsNature communications
- 2021

This work rigorously proves a serious limitation for noisy VQAs, in that the noise causes the training landscape to have a barren plateau, and proves that the gradient vanishes exponentially in the number of qubits n if the depth of the ansatz grows linearly with n.