Low-Depth Gradient Measurements Can Improve Convergence in Variational Hybrid Quantum-Classical Algorithms

An Adaptive Optimizer for Measurement-Frugal Variational Algorithms

(2020) Jonas M. Kübler et al.   Quantum

Quantum Natural Gradient

(2020) James Stokes et al.   Quantum

Stochastic gradient descent for hybrid quantum-classical optimization

(2020) Ryan Sweke et al.   Quantum

Quantum Machine Learning in Feature Hilbert Spaces

(2019) Maria Schuld et al.   PHYSICAL REVIEW LETTERS

Supervised learning with quantum-enhanced feature spaces

(2019) Vojtěch Havlíček et al.   NATURE

Adversarial quantum circuit learning for pure state approximation

(2019) Marcello Benedetti et al.   NEW JOURNAL OF PHYSICS

Random Compiler for Fast Hamiltonian Simulation

(2019) Earl Campbell   PHYSICAL REVIEW LETTERS

Variational ansatz-based quantum simulation of imaginary time evolution

(2019) Sam McArdle et al.   npj Quantum Information

Quantum Generative Adversarial Networks for learning and loading random distributions

(2019) Christa Zoufal et al.   npj Quantum Information

Barren plateaus in quantum neural network training landscapes

(2018) Jarrod R. McClean et al.   Nature Communications

Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets

(2017) Abhinav Kandala et al.   NATURE

Hybrid Quantum-Classical Approach to Quantum Optimal Control

(2017) Jun Li et al.   PHYSICAL REVIEW LETTERS

Efficient Variational Quantum Simulator Incorporating Active Error Minimization

(2017) Ying Li et al.   Physical Review X

Optimizing Variational Quantum Algorithms Using Pontryagin’s Minimum Principle

(2017) Zhi-Cheng Yang et al.   Physical Review X

The theory of variational hybrid quantum-classical algorithms

(2016) Jarrod R McClean et al.   NEW JOURNAL OF PHYSICS

Progress towards practical quantum variational algorithms

(2015) Dave Wecker et al.   PHYSICAL REVIEW A

A variational eigenvalue solver on a photonic quantum processor

(2014) Alberto Peruzzo et al.   Nature Communications

Information-Based Complexity, Feedback and Dynamics in Convex Programming

(2011) Maxim Raginsky et al.   IEEE TRANSACTIONS ON INFORMATION THEORY

Lower Bounds for the Minimax Risk Using $f$-Divergences, and Applications

(2011) Adityanand Guntuboyina   IEEE TRANSACTIONS ON INFORMATION THEORY

Robust Stochastic Approximation Approach to Stochastic Programming

(2009) A. Nemirovski et al.   SIAM JOURNAL ON OPTIMIZATION