# Tag Info

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 ...

12

I will only answer to the part of the question regarding how quantum mechanics can be useful for analysis of classical data via machine learning. There are also works related to "quantum AI", but that is a much more speculative (and less defined) kind of thing, which I do not want to go into. So, can quantum computers be used to speed-up data analysis via ...

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Here's a list of other resources to learn about quantum machine learning: An introduction to quantum machine learning The quest for a Quantum Neural Network Quantum Machine Learning: What Quantum Computing Means to Data Mining Quantum Machine Learning 1.0

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I know this is not what you are asking but this paper: Quantum Algorithm Implementations for Beginners explains the implementation of some machine learning algorithms. Hope this helps!

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 ...

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Much of the research on quantum algorithms that may have applications to AI is centered on quantum machine learning (QML). While I'd argue there are quite a few hypothetical reasons that QML could be used in machine learning some time in the future, QML research is in its infancy relative to classical machine learning research and its practical benefits ...

6

You are not swapping the first register (one qubit) with the entire second register ($k$ qubits), but just with the first qubit of the second register. What you need to know is what is meant by $\langle x | y \rangle$ when $x$ is one qubit and $y$ is $k$ qubits. The resulting state is the $k-1$ qubit state you get when you project one qubit (generally the ...

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One can recommend PennyLane by Xanadu.AI. You can find complete examples of quantum machine learning algorithms (e.g. Iris Classification), using hybrid quantum-classical computations. Additionally, they offer built-in plugins for IBM QisKit, Pyquil etc., to enable running Pennylane QML codes on IBM and Rigetti quantum hardwares.

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There are arguments that our brains are quantum mechanical, and arguments against, so that's a hotly debated topic. Fisher at UCSB has some speculative thinking about how brains might still use quantum effects even though they aren't quantum mechanical in nature. While there's no direct experimental evidence there are two references you might want to read: ...

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I've not looked at those papers specifically, but there are several different models for quantum computation (see here), including the gate model and the adiabatic model, which are polynomial time equivalent. That means if one has an exponential speedup, so does the other. The discussion should be interchangeable. The title, if not the question body, also ...

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In general, the efficiency of Quantum Machine Learning Techniques will be calibrated and measured more in terms of the energy efficiency, ability to handle complex computational problems, NP-hard problems and the ability to ensemble different domain algorithms than the speed and learning rate. However, there could be exceptionally faster quantum algorithms ...

5

Much of the work done so far with quantum computers has been focused on solving combinatorial optimization problems. Both D-Wave style Quantum Annealers and the more recent Gate Model machines from Rigetti, IBM, and Google have been solving combinatorial optimization problems. One promising approach to connecting machine learning and quantum computing ...

4

Gaussian Processes are a key component of the model-building procedure at the core of Bayesian Optimization. Therefore speeding up the training of Gaussian processes directly enhances Bayesian Optimization. The recent paper by Zhao et. al on Quantum algorithms for training Gaussian Processes does exactly this.

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Have a look at these for quantum machine learning: Supervised learning with quantum computers by Schuld and Petruccione (2018) An introduction to quantum machine learning by the same authors of the textbook above Quantum machine learning published in Nature 2017 by some experts in the field: Wittek, Rebentrost, Lloyd, et al Video presentations by Dr. Schuld ...

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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).

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There are different review overviews about Quantum Machine Learning (see the question referenced in comments to find a few) but it is an evolving field so you will have to keep updated. There is also an EDX online course about the subject made by Wittek recently released if you would like a little more hands-on format. I would advise to start with basics ...

3

The paper you refer is incomplete and not very right on this part. First a minus sign should be present in : $$|\phi\rangle = \frac{1}{\sqrt{Z}} (|a||0\rangle - |b||1\rangle)$$ Secondly, if you look at the original reference of this procedure on a special case of algorithm but it can be generalized, what you swap is actually the ancilla qubit of $|\psi\... 3 In this particular one (by quickly overlooking), they refer mostly to the logic gate approach. But nothing prevent them from talking about both. It depends on the algorithm and on which original model it was thought/designed on. Generally, if it is linear algebra based, it will be the logic gate approach. If they refer to optimization of a QUBO, they will ... 3 I was not able to find references specifically in quantum biology. I found however a review called Quantum Assisted biomolecular modeling. You may find it interesting but this is from 2010. The field has evolved since but I guess the ideas remain similar. The authors focus more on the idea of the ability of a quantum computer to try every classical paths ... 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 ... 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 Quantum simulation can be used to test models that could describe certain biological process. For example, a 2018 paper by Potočnik et al. examined light harvesting models using superconducting quantum circuits (see figure below). Currently, it's an open question whether quantum mechanics plays an important functional role in biological processes. Some ... 2 The most recent quantum machine learning textbook is Schuld and Petruccione (2018). Supervised Learning with Quantum Computers while a nice companion to Nielsen and Chuang for introductory quantum maths is Marinescu and Marinescu (2011). Classical and Quantum Information, Chapter 1: Preliminaries 2 Since quantum machine learning with NISQ hardware is such a relatively new field, it is still very highly research driven, and a lot of the potential is still being determined. To make these new research implementations more accessible, we've begun building implementations over at https://pennylane.ai/qml. Interesting ones include: Quantum Generative ... 2 The Context The algorithm Ewin Tang originally examined and dequantized was the quantum recommendation system algorithm by Kerenidis & Prakash. Many QML algorithms, including the quantum recommendation system algorithm, exploit the quantum linear systems algorithm (QLSA), which was posted on arXiv in 2008 by Harrow, Hassidim, and Lloyd (that's why it'... 1 The problem is that you applied a Swap gate when you should have applied a CSWAP, and so you never entangled the readout qubit with your query states (as a result the readout qubit will always return a "0", which makes sense because the net effect of$HH|0\rangle$is$I|0\rangle\$). Continuing your derivation starting from just after the first Hadamard, we ...

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