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13

This is very much an open question, but yes, there is a considerable amount of work that is being done on this front. Some clarifications It is, first of all, to be noted that there are two major ways to merge machine learning (and deep learning in particular) with quantum mechanics/quantum computing: 1) ML $\to$ QM Apply classical machine learning ...


8

Yes, all classical algorithms can be run on quantum computers, moreover any classical algorithm involving searching can get a $\sqrt{\text{original time}}$ boost by the use of grovers algorithm. An example that comes to mind is treating the fine tuning of neural network parameters as a "search for coefficients" problem. For the fact there are clear ...


6

Again, this is still an open question. There are two lines of work that come to mind when you talk of "hardware-based neural networks" which try/claim to use photonics as a mean to speed-up processing, and make direct reference to speeding up machine learning tasks. Shen et al. 2016 (1610.02365) propose a method to implement "fully-optical neural networks" ...


5

Taking the density matrix $$\rho=W+\frac{I_d}{d}=\frac 1M \sum_{m=1}^M\left|x^{\left(m\right)}\rangle\langle x^{\left(m\right)}\right|,$$ many of the details are all contained in the following paragraph on page 2: Crucial for quantum adaptations of neural networks is the classical-to-quantum read-in of activation patterns. In our setting, ...


4

First, they reduce the size from 28*28 to 4*4 images (by downsampling), then convert into binary values for pixels by just comparing to a value. Then, they encode the data in a quantum uniform superposition (with computational basis representing a bitstring data image with its label).


2

I will assume you are asking about D-Wave's quantum annealer. If there is a part of the learning process that can fit the QUBO (Quadratic Unconstrained Binary Optimization) formulation, then yes. The problem however is what to consider as binary variables of your problem. In CNN, we have in general real-valued parameters that we tweak for training (using ...


2

All of the answers here seem to be ignoring a fundamental practical limitation: Deep Learning specifically works best with big data. MNIST is 60000 images, ImageNet is 14 Million images. Meanwhile, the largest quantum computers right now have 50~72 Qbits. Even in the most optimistic scenarios, quantum computers that can handle the volumes of data that ...


2

Here is a latest development from Xanadu, a photonic quantum circuit which mimics a neural network. This is an example of a neural network running on a quantum computer. This photonic circuit contains interferometers and squeezing gates which mimic the weighing functions of a NN, a displacement gate acting as bias and a non-linear transformation similar to ...


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