Implementing the Quantum Approximate Optimization Algorithm (QAOA) with Open Source Software
Entropica releases QAOA package for Rigetti’s Quantum Cloud Service
Points to note…
+ QAOA is an algorithm designed for near-term quantum computers, and has applications to both machine learning and discrete optimisation. The EntropicaQAOA package integrates fully with our partner Rigetti’s Quantum Cloud Services™ (QCS).
[A] family of algorithms known as variational hybrid algorithms have been designed specifically for this so-called “NISQ” (i.e. near-term) era of quantum computing. These algorithms make use of both quantum and classical hardware, with each compensating for the other’s weaknesses. The QAOA belongs to this algorithmic family.
+ The basic goal of QAOA is to find a set of parameters that, when fed to specific operations in a quantum circuit, output the desired solution to the problem. Naturally, the relations that you choose to enforce between the parameters, as well as their initial values, can make a significant difference to the performance of the algorithm. The classical optimiser that provides updated parameters at each step also has an important impact on the efficacy of the algorithm.
+ Several different ways of initialising parameters are included, and it is very easy to switch from one classical optimiser to another. If Scipy’s methods are inadequate, you can easily import tools from other optimisation libraries such as NLopt, scikit-optimize, or use your own custom-built code.
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