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A Machine That Sees

A Machine That Sees

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 !




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

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…

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:

  • Noise Resistant : With Quantum Error Correction, along with it’s quantum nature, QNNs are resistant to constant noise. In current quatnum computers, there’s always a constant noise / error that occurs in quantum gates, but QNNs overcome it
  • B...

1.Convolutional layers : Instead of kernals, you have gates that are applied to the qubits adjacent to it

To tackle the first problem, we could just let qubits represent the quantum system! Introducing: Quantum Convolutional Neural Networks .

opposite by exponentially decreasing the number of qubits.

CNNs are actually able to achieve pretty insane results — 99.75% accuracy ! The reason why CNNs’ incredible power is due to it’s ability to look at the surrounding pixels and based off of that, extract f...

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!

  • Many Executions Needed: Since we have to stamp the kernal all over the image, and do this to the potential of thousands of images, you’re going to have to run run the quantum circuit a lot. Current quantum computers aren’t able to handle that many executions...

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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