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 ...
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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 ...
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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 ...
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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 ...
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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)...
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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 ...
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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.
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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, ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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: ...
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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 ...
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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:
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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 ...
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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 ...
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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 ...
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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 ...
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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{\...
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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 ...
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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 ...
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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$...
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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 ...
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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 ...
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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 ...
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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 ...
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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(\...
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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 ...
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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:
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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 ...
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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 (...
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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 ...
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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 ...
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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 ...
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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
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Inference on real hardware using a pre-trained quantum model on a simulator
Being Quantum Computers with more than 5-7 qubits quite expensive (especially IBM's) I was wondering if it makes sense to pre-train a quantum machine learning model on a noisy simulator, store the ...
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Quantum Machine Learning in NISQ era
I know that quantum algorithms can be useful for machine learning ("ML") methods, and vice versa. For example if we use QAOA we can use for the optimization part different types of ML ...
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What are QML algorithms using less than 8 qubits and provide a quantum advantage?
So this is more of a soft question.
I've been trying to find some quantum machine learning algorithms can both be run with less than 8 qubits and provide a quantum advantage to classical machine ...
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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 ...
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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 ...
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'Geometric difference' in Google's 'Power of data in quantum machine learning' paper
Has anyone ever tried to implement the geometric difference metric introduced in the Google's power of data paper? It is defined in Eq. 5.
My implementation of the metric is as follows.
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