Quantum Machine Learning: learning on neural networks

Quantum Machine Learning: learning on neural networks

Quantum Machine Learning: learning on neural networks. Analytical gradient computation, the Hadamard test, and more. This time, we’re going a little deeper into the rabbit hole and looking at how to build a neural network on a quantum computer.

My last articles tackled Bayes nets on quantum computers (read it here!), and k-means clustering, our first steps into the weird and wonderful world of quantum machine learning.

This time, we’re going a little deeper into the rabbit hole and looking at how to build a neural network on a quantum computer.

In case you aren’t up to speed on neural nets, don’t worry — we’re starting with neural nets 101.


What even is a (classical) neural network?

Almost everyone has heard of neural networks — they’re used to run some of the coolest tech we have today — self driving cars, voice assistants, and even the software that generates super realistic pictures of famous people doing questionable things.

What makes them different from regular algorithms is that instead of having to write down a set of rules, we need to provide networks with examples of the problem we want it to solve.

machine-learning quantum-computing artificial-intelligence quantum-machine-learning math

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