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:
Each digit is a 28 x 28 image, meaning there’s a total of 784 pixels in the whole image. We take our image and flatten it (instead of 28 x 28, we turn the shape into 784 x 1). We pop this guy into our neural network and on the other side we end up with our prediction !
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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...
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!
The brain is able to take those electrical signals, convert them into ...
opposite by exponentially decreasing the number of qubits.
The bottleneck here is the range of possible qubits which the reversed MERA can reach.In other words, the QCNN might not be able to produce the labels. But we can overcome this by implementing Quantum Error Correction
To tackle the first problem, we could just let qubits represent the quantum system! Introducing: Quantum Convolutional Neural Networks .
This is a neural network that literally replicates the whole CNN architecture. C onvolutional layers and max pooling ar...
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!
Many of our existing ML algorithms could be translated i...
Turns out this simple neural network is able to achieve an 88% accuracy ! That’s pretty impressive, considering that it’s just a bunch of linear layers and activations. But let’s try something even better…
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!
Much like a normal convolutional layer, we ta...
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:
Max Pooling Layers : These layers reduce the feature map (the size of the features), reduce the computational resources needed and prevents overfitting
Fully Connected Layers : They appear at the end of the CNN, they’re just linear layers w...
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.
Layer 3 becomes even cooler! Repeating patterns, car...
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