# Quantum Approximate Optimization

9 papers with code • 0 benchmarks • 0 datasets

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# Datasets

# Greatest papers with code

# TensorFlow Quantum: A Software Framework for Quantum Machine Learning

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.

# PennyLane: Automatic differentiation of hybrid quantum-classical computations

PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantum-classical computations.

# Learning to learn with quantum neural networks via classical neural networks

Quantum Neural Networks (QNNs) are a promising variational learning paradigm with applications to near-term quantum processors, however they still face some significant challenges.

# Natural evolution strategies and variational Monte Carlo

A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization.

# Policy Gradient based Quantum Approximate Optimization Algorithm

Taking such constraints into account, we show that policy-gradient-based reinforcement learning (RL) algorithms are well suited for optimizing the variational parameters of QAOA in a noise-robust fashion, opening up the way for developing RL techniques for continuous quantum control.

# Quantum annealing initialization of the quantum approximate optimization algorithm

This motivates studies of the optimization landscape and search for heuristic ways of parameter initialization.

Quantum Approximate Optimization Quantum Physics Disordered Systems and Neural Networks Statistical Mechanics Computational Physics

# Exploiting Symmetry Reduces the Cost of Training QAOA

We show how by considering only the terms that are not connected by symmetry, we can significantly reduce the cost of evaluating the QAOA energy.

Quantum Approximate Optimization Quantum Physics

# Local classical MAX-CUT algorithm outperforms $p=2$ QAOA on high-girth regular graphs

We show that for all degrees $D \ge 2$ and every $D$-regular graph $G$ of girth $> 5$, QAOA$_2$ has a larger expected cut fraction than QAOA$_1$ on $G$.

Combinatorial Optimization Quantum Approximate Optimization Quantum Physics

# Quantum Approximation for Multi-Scale Scheduling

Then, for the given MWIS, the proposed QAOS designs the Hamiltonian of the problem.