23 votes
Accepted

Introductory material for quantum machine learning

The Nielsen and Chuang of Quantum Machine Learning is this extensive review called "Quantum Machine Learning" published in Nature in 2017. The arXiv version is here and has been updated as recently as ...
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14 votes

Is there any potential application of quantum computers in machine learning or AI?

I will only answer to the part of the question regarding how quantum mechanics can be useful to analyse classical data with machine-learning-like techniques. There are also works related to "quantum ...
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  • 19k
14 votes

Will deep learning neural networks run on quantum computers?

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 ...
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  • 19k
13 votes
Accepted

Quantum machine learning after Ewin Tang

I am not an expert in the field but there are a few points that I am aware of: There are proofs that certain quantum machine learning algorithms cannot be efficiently simulated on a classical ...
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13 votes
Accepted

How Mature is the Tensorflow Quantum Library

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 ...
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  • 655
13 votes
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Comparing method of differentiation in variational quantum circuit

Both finite differences and the parameter-shift rule can be used to compute quantum gradients on quantum hardware. However, there are several reasons that lead to the parameter-shift rule being ...
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11 votes

Introductory material for quantum machine learning

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 ...
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9 votes
Accepted

Where can I find example circuits to learn from?

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!
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  • 842
8 votes
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Will deep learning neural networks run on quantum computers?

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 ...
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8 votes
Accepted

What language is more suitable for quantum machine learning algorithms?

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, ...
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8 votes
Accepted

Can quantum computing contribute to the development of artificial intelligence?

In my view, if artificial general intelligence (AGI) is ever 'solved', it likely won't be because of the development of a quantum AI algorithm. Rather, it will be because of a breakthrough in the ...
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8 votes
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Quantum Circuit To Compute Any Inner Product

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\...
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  • 752
8 votes

What is the advantage of quantum machine learning over traditional machine learning?

As so often, and especially in young research areas, the answer depends quite a lot on how you break down the question. Let me try a few examples: Does quantum mechanics change what is theoretically ...
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7 votes

Swap Test for vector difference - how are different sized inputs combined?

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 $\...
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7 votes
Accepted

Is a "kernel" just the quantum equivalent of classical SVMs?

Consider a simple implementation of a Support Vector Machine (SVM) that finds a hyperplane (defined by its normal vector $w$) that maximally separates vectors $\{v_1, \dots, v_m\}$ according to their ...
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6 votes

Is there any potential application of quantum computers in machine learning or AI?

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 ...
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  • 519
6 votes

How to recognize if a paper is talking about quantum annealing or gate logic?

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 ...
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6 votes
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Quantum speedup in Bayesian machine learning on NISQ computers

As far as I know, there are four possibilities for having a quantum advantage in Bayesian machine learning: Gaussian processes: there is a known quantum speed-up for Gaussian processes that you can ...
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6 votes

How is data encoded in a quantum neural network?

There are many possible ways to encode data into a quantum neural network (QNN). In one of the first papers to suggest the use of variational circuits to classify data [1], the authors suggest the ...
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6 votes

What are the differences between the IBM machines?

Besides number of qubits, the devices can have other differences as well. The architecture of the device can be different, meaning that each device could have different connectivity maps. This would ...
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6 votes

Claimed "potential revenue" from machine learning in 2023?

"IonQ is claiming to have a potential application in machine learning by 2023. What applications could they have in mind?" None. The plot you showed has no units on the y-axis. It doesn't ...
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5 votes

Distance calculation between two vectors

Thanks for the answers from @forky40. I accept it as the right answer, but do want to provide a complete derivation as follows. (Same as in the original question) First, initialize per DistCalc: $$ |...
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5 votes

Is there any potential application of quantum computers in machine learning or AI?

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 ...
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5 votes
Accepted

Can quantum computing speed up Bayesian learning?

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 ...
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5 votes

Introductory material for quantum machine learning

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 ...
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5 votes
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Embedding classical information into norm of a quantum state

I don't understand their notion of a $2^n$ dimensional complex vector. If each of the components of their classical data array has two floating point numbers, wouldn't encoding that into a $n$-...
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5 votes

Pennylane and Qiskit for quantum machine learning

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 ...
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5 votes

What are the differences between the IBM machines?

First of all, the name of backends (devices) have nothing to do with their location! They are all located in US. Back to your question, as others already mentioned the difference is in the ...
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  • 438
5 votes
Accepted

What does the quantum part of the quantum support vector machine actually do?

The basic idea of how the quantum feature map works is that you're using a quantum computer to map each input datapoint $x$ from your training domain $\mathcal{X}$ into a quantum state $|\phi(x)\...
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4 votes
Accepted

Are there any examples of anyone applying quantum algorithms to problems in computational biology?

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 ...
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