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

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

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

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.

Our approach formalizes the connection between quantum symmetry properties of the QAOA dynamics and the group of classical symmetries of the objective function.

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.

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

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

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