# Tag Info

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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 ...

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First we should take a step back. Is there any machine learning done a quantum computer that cannot be efficiently simulated on a classical computer? The answer currently (2020) is no. In this respect quantum machine learning (which has many variants) is at the fundamental research phase. None of this is at a stage where it is at all considered something ...

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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 ...

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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" ...

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We can use the SWAP test to determine the inner product of 2 states $|\phi\rangle$ and $|\psi\rangle$. The circuit is shown below The state of the system at the beginning of the protocol is $|0\rangle \otimes |\phi \rangle \otimes |\psi \rangle$. After the Hadamard gate, the state of the system is $|+\rangle \otimes |\phi \rangle \otimes |\psi \rangle$. The ...

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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, ...

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First: The paper references [37] for Levy's Lemma, but you will find no mention of "Levy's Lemma" in [37]. You will find it called "Levy's Inequality", which is called Levy's Lemma in this, which is not cited in the paper you mention. Second: There is an easy proof that this claim is false for VQE. In quantum chemistry we optimize the parameters of a ...

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I don't think a hello world really exists here. You can have different points of view or goals here. I will give references. The first one is speeding up parts of the algorithm with a quantum version (here is an example reference). But here, we assume a perfect hardware. Another one is to apply it to quantum many-body systems. The interesting point here is ...

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What are some other proposed applications of quantum neural networks? Absolutely any application of classical neural networks can be an application of quantum neural networks. There's a lot of examples beyond the two you listed. Also, have any of those proposed solutions been programmed/simulated? Yes, for example Ed Farhi of MIT and Hartmut Neven of ...

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