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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Hybrid Classical-Quantum network dataset error [closed]

I am new to Qiskit and am developing a quantum-classical network modeling. This Qiskit tutorial on the subject yields the following - ...
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Recommended resources for starter in quantum computing and its eventual applications on machine learning [duplicate]

A growing interest in Quantum Computing -particularly in its eventual applications on Machine Learning- has taken over me and I would like to follow that path. I have strong Maths (Calculus, ...
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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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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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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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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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How do we implement QKNN algorithm on our dataset

What could be the steps to implement a distance based classifier (eg Qknn) on our dataset using quantum computing? I read a paper on "Distance based classifier using Iris dataset". I wanted ...
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What are the libararies for Machine Learning on NISQ Chip? And What are the roadmaps?

Nowadays quantum learning is hiring. And we can see mainly two different area. One of them is variational algorithms part. And the other one is classical learning for quantum systems like NISQ. (Some ...
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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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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's the point of the half coefficient in the max-cut cost Hamiltonian

Below is the cost Hamiltonian for an unweighted max-cut problem, I don't understand what the point of the half coefficient is. Why couldn't we omit it? $C_\alpha = \frac{1}{2}\left(1-\sigma_{z}^j\...
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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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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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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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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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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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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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What is a “repeat until success quantum circuit” in quantum neural networks?

I am working now on a quantum neural network project and want a deep explanation on the Repeat Until Success circuit. What I know about this circuit is that it allows a nonlinear activation function ...
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Usage of Tensorflow/Keras to train Qiskit circuits

In order to explore whether it is possible to train a Qiskit Quantum circuit with tensorflow I built a small toy model. The purpose of this toy model is to find via tensorflow the correct angle to get ...
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106 views

Comparing QSVM & Classic SVM on BigData. Quantum Supremacy

I work on comparing QSVM and Classic SVM (SKlearnSVM) with using Qiskit. I have to show quantum supremacy at 400000-500000 samples but I don't get good results. I have problem with long time training ...
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130 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 ...
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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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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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Qiskit QSVM - Alphas and Support Vectors [closed]

I just started using Qiskit and I have implemented the QSVM example. However, I having trouble with the return. The qsvm.predict() results are reasonable but when ...
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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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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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Why is Farhi and Neven's architecture described in “Classification with Quantum Neural Network on near term processors” called a Neural Network?

In regards to "Classification with Quantum Neural Networks on near term processors" (which you can find here) , there are still a few things that do not make entirely sense to me. First of all, why ...
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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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Regarding quantum support vector machine using qiskit [duplicate]

I would like to ask, how can I add my own .csv data file to run a quantum support vector machine using qiskit ? I don't want to use already existing datasets in sklearn, scikit-learn library.
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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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Methods for encoding non-linear probabilities as data

I am working on translating a computing method I developed to model complex non-linear systems with classical computational methods into a form that is natural to quantum computing. The technique ...
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927 views

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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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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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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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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658 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 ...
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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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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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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 ...