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Questions tagged [quantum-enhanced-machine-learning]

For questions about quantum algorithms tackling machine learning tasks (e.g. the HHL algorithm or questions about quantum neural networks). For questions about applying classical machine learning to quantum-information-related problems, use machine-learning instead.

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Quantum machine learning after Ewin Tang

Recently, a series of research papers have been released (this, this and this, also this) that provide classical algorithms with the same runtime as quantum machine learning algorithms for the same ...
Alex's user avatar
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36 votes
5 answers
5k views

Introductory material for quantum machine learning

In the past few days, I have been trying to collect material (mostly research papers) related to Quantum machine learning and its applications, for a summer project. Here are a few which I found ...
Sanchayan Dutta's user avatar
29 votes
3 answers
2k views

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

A lot of people believe that quantum computers can prove to be a pivotal step in creating new machine learning and AI algorithms that can give a huge boost to the field. There have even been studies ...
Piyush Kathuria's user avatar
15 votes
5 answers
4k views

Will deep learning neural networks run on quantum computers?

Deep Learning (multiple layers of artificial neural networks used in supervised and unsupervised machine learning tasks) is an incredibly powerful tool for many of the most difficult machine learning ...
Bob Swain's user avatar
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15 votes
4 answers
2k views

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

As the title suggests, I'm searching for published examples of quantum algorithms being applied to problems in computational biology. Clearly the odds are high that practical examples don't exist (yet)...
Greenstick's user avatar
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13 votes
1 answer
1k views

Comparing method of differentiation in variational quantum circuit

Training of variational circuits needs to calculate the derivative to be optimized. Several methods were proposed (1), the most famous ones being the finite difference and the parameter shift rule. ...
incud's user avatar
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12 votes
1 answer
872 views

Embedding classical information into norm of a quantum state

According to An introduction to quantum machine learning (Schuld, Sinayskiy & Petruccione, 2014), Seth Lloyd et al. say in their paper: Quantum algorithms for supervised and unsupervised machine ...
Sanchayan Dutta's user avatar
10 votes
2 answers
1k views

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

Why exactly is machine learning on quantum computers different than classical machine learning? Is there a specific difference that allows quantum machine learning to outperform classical machine ...
Rob James's user avatar
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10 votes
1 answer
674 views

Barren plateaus in quantum neural network training landscapes

Here the authors argue that the efforts of creating a scalable quantum neural network using a set of parameterized gates are deemed to fail for a large number of qubits. This is due to the fact that, ...
asdf's user avatar
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10 votes
2 answers
520 views

What are the benefits of using quantum machine learning?

I have been investigating uses for quantum machine learning, and have made a few working examples (variations of variational quantum classifiers using PennyLane). However, my issue now is its ...
Andrew's user avatar
  • 333
6 votes
1 answer
443 views

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

I'm confused about the relationship between kernel methods and SVM methods used in quantum machine learning. Sometimes the two seem to be used interchangeably, but often I'll see them both in the same ...
Jay Muntz's user avatar
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6 votes
2 answers
507 views

Understanding the definition of quantum neural network of Abbas et al. 2020

My Question based on this Paper https://arxiv.org/pdf/2011.00027.pdf "Power of Quantum Neural Networks" - Section 2. So I know that there are different ways to implement Neural Networks into ...
Jeff24's user avatar
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6 votes
1 answer
183 views

Claimed "potential revenue" from machine learning in 2023?

In this plot: taken from here, IonQ is claiming to have a potential application in machine learning by 2023. What applications could they have in mind? From what I understand, modern error correction ...
Steven Sagona's user avatar
6 votes
1 answer
282 views

What's new in Quantum Natural Language Processing (QNLP) w.r.t classical NLP?

I recently discovered Cambridge Quantum people have developed lambeq, a quantum natural language processing high-level library. Before diving into it, I'd like to understand more in detail what ...
mpro's user avatar
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6 votes
0 answers
234 views

Is VQA quicker than classical machine learning?

Variational Quantum Algorithm (VQA) is a kind of quantum algorithm corresponding to classical machine learning. Unlike the square speed up of Grover's algorithm, the circuit in VQA does not seem to ...
narip's user avatar
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6 votes
0 answers
361 views

Calculating gradient of a gate using Parameter shift rule

I've been following this website to check out how parameter-shift works for calculation of gradients for backpropagation in Variational Quantum Machine Learning Circuits Most of it made makes sense ...
MetaInformation's user avatar
5 votes
2 answers
369 views

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

I'm implementing a quantum support vector machine on qiskit and I was wondering what the quantum part of the algorithm actually does. I'm aware that it's a feature map that executes the kernel ...
Sinestro 38's user avatar
5 votes
4 answers
502 views

Getting started with Quantum Machine Learning

I have some working knowledge in Machine Learning and Deep Learning. I am currently in the process of studying Quantum Computing fundamentals. I would like to know whether there are any Quantum ...
radar101's user avatar
  • 111
5 votes
1 answer
86 views

How is quantum machine learning reversible?

Say I have a binary classification network, which takes in inputs and classifies them. I can put in different inputs and get the same output, right? So does that not make QML non reversible, since ...
Shantanu's user avatar
  • 173
5 votes
1 answer
418 views

Applications of Quantum Principal Component Analysis

I have been reading Seth Lloyd's paper on Quantum Principal Component Analysis and while there is a short discussion that points to possible applications, I am having a hard time seeing the advantage ...
Song of Physics's user avatar
5 votes
1 answer
254 views

Are there quantum algorithms demonstrating speedup computing classical neural networks (in 2021)?

It seems like there are a number of different speed-ups for different machine learning algorithms: But has anyone created an algorithm showing speed-up for neural networks? A similar question was ...
Steven Sagona's user avatar
5 votes
1 answer
744 views

Quantum PCA State Preparation

In Quantum Algorithm Implementations for Beginners is an example of the Quantum PCA with an given 2 x 2 covariance matrix $\sum$. The steps for state preparation are given in the paper. The steps are: ...
rexrayne's user avatar
4 votes
2 answers
1k views

Computing expectation value of product of observables in PennyLane

In PennyLane, the following circuit returns the expectation value of the PauliZ observable on qubit (wire) 1: ...
ryanhill1's user avatar
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4 votes
1 answer
3k views

How is data encoded in a quantum neural network?

I am a newbie to quantum machine learning. I am trying to build a quantum neural network (QNN). What I studied so far about QNN is that input would be qubits and hidden layer parameter can be set ...
Muhammad Kashif's user avatar
4 votes
1 answer
237 views

SWAP Test as a Projective Measurement [closed]

In a much cited paper by Lloyd et al Quantum Algorithm for Supervised and Unsupervised Machine Learning, they proposed a rather cute quantum algorithm to evaluate the distance between an input feature ...
X...'s user avatar
  • 155
4 votes
1 answer
273 views

Does TensorFlow Quantum tfq.convert_to_tensor work on custom gates?

I'm trying to use Cirq with TensorFlow Quantum to simulate a variational quantum classifier. There's a tutorial on the TFQ website on building a quantum neural network to classify a simplified version ...
ryanhill1's user avatar
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4 votes
1 answer
901 views

HHL algorithm -- controlled-by-eigenvalues rotations

All the references in this question refer to Quantum algorithm for solving linear systems of equations (Harrow, Hassidim & Lloyd, 2009). The question I have is about the step where they apply ...
Adrien Suau's user avatar
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4 votes
1 answer
190 views

Objective function of Quantum GAN in the paper "Quantum generative adversarial networks"

In this paper about Quantum GANs, the authors do not explain clearly how do they have the equation $$\newcommand{\tr}{\operatorname{Tr}}\newcommand{\Pr}{\operatorname{Pr}} V(\vec{\theta}_D, \vec{\...
Monad's user avatar
  • 375
4 votes
1 answer
297 views

Why is sampling from probability distributions generated by specific quantum circuits classically intractable?

I was reading a paper by Benedetti et al. titled Parameterized quantum circuits as machine learning models. Its authors state the following: We also know that sampling from the probability ...
karolyzz's user avatar
  • 289
4 votes
1 answer
125 views

Understanding the Quantum Hebbian algorithm

I've been reading the paper from Lloyd and al. on Quantum Hopfield Networks, but I don't understand the quantum Hebbian algorithm (page 3). I am trying to understand the mathematical development on ...
Skyris's user avatar
  • 137
4 votes
1 answer
163 views

Data encoding in the quantum perceptron model

In this paper, this figure shows the perceptron model used for quantum neural network. When realizing the inner product between weight vector and input vector, it defines a unitary transformation $U_W$...
peachnuts's user avatar
  • 1,393
4 votes
1 answer
842 views

What is the advantage of QSVM over the classical SVM?

I am mainly talking about QSVM from Qiskit (https://qiskit.org/documentation/stubs/qiskit.aqua.algorithms.QSVM.html#qiskit.aqua.algorithms.QSVM) versus a classical SVM. Is it just a time complexity ...
Rob James's user avatar
  • 355
4 votes
1 answer
115 views

How can I experimentally quantify the "speed up" of a quantum-enhanced machine learning?

I have essentially developed a Quantum Support Vector Machine to classify some data I have successfully. I wanted to know if it is possible to quantify the speed-up and time difference between this ...
Rob James's user avatar
  • 355
4 votes
1 answer
324 views

When and how do we use basis embedding?

It is suggested in various sources that a possible approach to representing classical data as a quantum state is simply to take the binary sequence $x$ and turn it to $|x\rangle$ (i.e., "basis ...
Haim's user avatar
  • 257
4 votes
1 answer
895 views

Calculating the quantum euclidean distance between vectors

I am trying to get the distance using the swap test circuit. , With the help of the codes I shared, I can only estimate the distance between two vectors. Can it calculate the distances of many vectors ...
T.Pablo's user avatar
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4 votes
0 answers
94 views

Reconstructing classical data from quantum feature maps

In the paper [Supervised learning with quantum enhanced feature spaces (Nature, arxiv) by Havlicek et al., a feature map is defined by $$| \Phi(\bar{x})\rangle=U_{\Phi(\bar x)}H^{\otimes n} U_{\Phi(\...
Haim's user avatar
  • 257
4 votes
0 answers
256 views

Comparison of matrix inversion algorithms

Since 2009, many matrix inversion algorithms have appeared. Is there somewhere a table, or recently released overview, comparing the speed of matrix inversion algorithms, like done in this table taken ...
Iskander's user avatar
3 votes
2 answers
247 views

Sympy suddently does not work together with TFQ

I work with tensorflow-quantum and use sympy for parameter updating. Suddenly, without (manually) updating or changing anything this error comes up: ...
eli44's user avatar
  • 165
3 votes
1 answer
105 views

Why are 3, rather than 2 gates used in quantum variational circuits?

In the hello many worlds tensorflow tutorial and in the lockwood paper (2020) I have seen that often in QVC the following combination of gates is used: $R_z(\theta), R_y(\theta), R_x(\theta)$ I am ...
eli44's user avatar
  • 165
3 votes
2 answers
462 views

Method and Meaning of Quantum Encoding in Quantum Machine Learning

I'm now studying quantum machine learning. While studying papers about quantum machine learning, I have a question about quantum embedding. To my knowledge, some general embedding algorithms, such as ...
JERMY's user avatar
  • 111
3 votes
1 answer
38 views

Is QST a inherently supervised or unsupervised problem in Machine Learning?

I am studying how to apply neural networks to the problem of Quantum State Tomography and I got confused when it comes to decide if this is a supervised or unsupervised learning problem. At first, I ...
Dimitri's user avatar
  • 85
3 votes
1 answer
2k views

How does the ZZ Feature Map influence the measurement?

I've been look at this Notebook from qiskit and trying to understand whats happening, but can't quite figure it out. From my understanding, rotations around the Z ...
Ricardo's user avatar
  • 189
3 votes
2 answers
989 views

How well different featuremap encode the data?

Recently, I was doing research on QML. Qiskit gave detailed steps on how to encode data into quantum states, but I was confused about one point: there are different feature mapping methods under ...
Mistico013's user avatar
3 votes
1 answer
80 views

Data input limitations (size) for QML

I have done quite a few Google/paper searches but did not found an answer. I would like to test the possibility of speeding up/ improving the accuracy of an existing unsupervised machine learning (...
Bill's user avatar
  • 31
3 votes
1 answer
146 views

Advantage of density matrix over vector to form quantum kernel

In Maria Schuld, Supervised quantum machine learning models are kernel methods, Section III.A, on page 6, the third paragraph from the bottom states While from a quantum physics perspective it seems ...
Hans's user avatar
  • 217
3 votes
1 answer
113 views

Game formulation of Quantum GAN

Quantum Generative Adversarial Network (QuGAN) generates a desired quantum state via a minimax game between generator and discriminator (equivalently, it's optimizing a trace distance between ...
userflux9674's user avatar
3 votes
0 answers
33 views

Why is "reducing Hamiltonian energy" also optimizing a Quantum Machine Learning model?

From what I observed, most hybrid qml architectures surround the ideas of Hamiltonian states, and it seems like our goal to optimize a circuit is to keep energy states as low as possible. But why is ...
Ryan Wang's user avatar
  • 267
3 votes
0 answers
60 views

What ways are there to use parallelisation in quantum machine learning?

In classical machine learning, GPUs have been used successfully to parallelise the training as well as the inference process. Does quantum machine learning have the same potential to operate with ...
Quantum Brilliance's user avatar
3 votes
1 answer
163 views

Detailed references on Quantum Principal Component Analysis

I am looking for review articles and other online resources on qPCA based on Lloyd's original proposal. I have found the original source to be slightly hand-wavy with the details so I am having ...
Song of Physics's user avatar
2 votes
2 answers
332 views

Quantum SVM with large feature set

I am trying to practice QSVM from the following tutorial Introduction into Quantum Support Vector Machines The author has used 2 feature_dimension with 2 component PCA ...
Protima Rani Paul's user avatar