Ideas from books, articles & podcasts.
1.Convolutional layers : Instead of kernals, you have gates that are applied to the qubits adjacent to it
2.Pooling Layers : Where you just measure half of the qubits and kick out the rest
3.Fully Connected Layer: Just like the normal one
If you’re a super quantum nerd you might have noticed that this architechture might have some resemblence to a reverse MERA (Multi-scale Entanglement Renormalization Ansatz).
A normal MERA takes 1 qubit and then exponentially increases the number of qubits by introducing new qubits into the circuit.
But in the reverse MERA, we’re doing the
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These new Quantum Machine Learning algorithms are but a testament to what there is to come. Even though quantum computers are at it’s infancy, we have already seen these new QML algorithms which are already outperforming our old ones!
Max Pooling Layers : These layers reduce the feature map (the size of the features), reduce the computational resources needed and prevents overfitting
By extracting those features you can put them in a neural network and classify your image! But as cool as that sounds, there are 2 Achilles heels:
To tackle the first problem, we could just let qubits represent the quantum system! Introducing: Quantum Convolutional Neural Networks .
Let’s do a quick run through of how a machine could see. First let’s take the MNIST Dataset, a dataset of digits from 0 to 9:
The ability for an organ to take photons from the outsight world, focus them, and then convert them into electrical signals is pure awesomeness! But what’s even more awesome is the organ behind your eyeballs — the brain!
A Quanvolutional Neural Network (QNN) is basically a CNN but with quanvolutional layers (much like how CNN’s have convolutioanl layers). A Quanvolutional layer acts and behaves just like a convolutional layer!
By the first layer the kernels can start telling which images have verticle lines, horizontal lines and different colors. By layer 2 you can put those features together and form more comple shapes like corners or circles.
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