Questions tagged [machine-learning]

For questions about how quantum computing could improve or affect machine learning i.e. quantum machine learning. Questions about classical machine learning belong on another site, such as Stack Overflow, Cross Validated or Artificial Intelligence SE.

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Advantage of quantum machine learning over machine learning [closed]

Can they learn from fewer data samples or are they able to deal with higher levels of noise?
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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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'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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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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57 views

Prove the parameter shift rule in case of generator is Pauli matrix

I'm studying the parameter shift rule and got stuck when doing an example with Pauli operators in https://arxiv.org/abs/1803.00745. With $f=e^{-i\mu\frac{\sigma_i}{2}}$, $\partial_\mu f=\frac{(-i\...
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Normalize and encode real data in a way that encode collinear vectors with different values

Now, I am working on a quantum supervised learning problem and I have a problem with amplitude encoding. Before being encoded, a vector $(a_1, a_2,\dots,a_n)$ must be normalized in such a way that $\...
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1answer
210 views

Differentiable Programming of Quantum Computers

Recently, I have tried PennyLane and TensorFlow Quantum. These platforms are said to provide differentiable programming of quantum computers but I can't understand it clearly though. I have searched ...
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1answer
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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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Quantum State Tomography from IQ plane data

Background: I am given to understand that the steps of Quantum State Tomography (QST) are as follows for a single qubit: The qubit is in the state $\psi=a_0|0\rangle+a_1|1\rangle$ with density matrix ...
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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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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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1answer
52 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 ...
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1answer
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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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How to get the solution and prediction of nuclear ridge regression? [closed]

\begin{equation} \label{eqs.1} \tilde{y}=\sum_{m=1}^{M}\alpha_{m}\kappa(x^{(m)},\tilde{x}) (1) \end{equation} where $\kappa(x^{(m)},\tilde{x})$defines the similarity between data and can be chosen ...
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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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Is qiskit documentation about determining $|0\rangle$ and $|1\rangle$ incorrect?

I'm using pi-pulse pulses on qubit Armonk for determining 0 and 1 by Machine Learning. But when I run the code from https://qiskit.org/textbook/ch-quantum-hardware/calibrating-qubits-pulse.html. I get ...
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50 views

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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Random Circuit Generation in Cirq

I am trying to improve an optimization method as describe in this paper: enter link description here For that I need to create many random equivalent circuits. In my experiment I have 6 beam splitters ...
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2answers
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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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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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2answers
77 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 ...
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1answer
327 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. ...
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Calculating Dot Product of Two States

I've been reading Peter Wittek's Quantum Machine Learning. In chapter 10.2 of this book, the author explains how we can calculate the dot product of two states: To evaluate the dot product of two ...
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1answer
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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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158 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 ...
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113 views

How to learn parameters in a quantum circuit, given an interference pattern?

Using cirq, I have the following quantum circuit, with three parameters: phi, alpha and beta: ...
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Quantum Boltzmann machine: How do you sample from the Boltzmann distribution on a quantum computer?

I am reading through the following article https://arxiv.org/abs/1601.02036. Eq. (22) describes one of the terms of the gradient of the log-likelihood cost function, which can be estimated using ...
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1answer
81 views

Quantum Circuit Optimization with Machine Learning [closed]

I read some paper about Quantum Circuit Optimization but I am on a low level. And have some experience in ML. But what I don't understand is it possible that ML can help to optimize Quantum Circuits ...
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1answer
160 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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380 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 ...
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1answer
184 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 ...
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3answers
114 views

What are the differences between the IBM machines?

I'm quite new to this field, and have started sending jobs to IBM's quantum computers. I have access to around 11 locations. I can see that these have different numbers of qubits within them, and then ...
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190 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 ...
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1answer
116 views

Quantum-Assisted Neural Network Training (Is my design reasonable?)

I'm a college student with a slight interest in quantum mechanics. I think I have a decent understanding of the Copenhagen and Many Worlds interpretations of quantum mechanics, and was considering how ...
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1answer
512 views

Quantum Circuit To Compute Any Inner Product

I'm currently reading the paper Classification with Quantum Neural Networks on Near Term Processors It shows a method to determine the following quantity: Where U is a unitary operator acting on $|z,...
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1answer
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What's the relationship between output of qubit measurements and classification of data in Quantum Machine Learning?

I'm training a model in Q# which has more than 2 features. I have trouble understanding the following things: How is the data classified based on the qubit states? For example: If I have only 2 ...
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How do you decide which rotations to use in a Quantum Machine Learning model?

I am trying to design a model using Q#'s machine learning library that takes in two features (real numbers from 0 to 1) and classifies as 0 or 1. So how do I decide which Rotations and what seeds to ...
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1answer
96 views

How is back-propagation done in “Transfer learning in hybrid classical-quantum neural networks”

Just read this paper from Xanadu on Quantum Transfer Learning and a couple of things are unclear to me regarding the optimisation step. How is back-propagation done through the classical weights ...
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2answers
285 views

Q# Error: No namespace with the name “Microsoft.Quantum.MachineLearning” exists

I'm having trouble getting the namespace Microsoft.Quantum.MachineLearning. Here is an example Q# code: ...
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1answer
103 views

How many samples are required to estimate the probabilities of a state?

Suppose that we have a quantum state of the form: $$|\psi\rangle = \sqrt{p}|0\rangle + \sqrt{1-p}|1\rangle$$ In order to get an estimate of the probability of reading $|0\rangle$ or $|1\rangle$, we ...
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Is there a “parameterized initialization” that I can apply to a QuantumRegister to re-use a circuit?

I'm working on a QuantumCircuit which measures the fidelity of one point (my "test vector") and two other points (my "data set", containing of states phi_1 and <...
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1answer
138 views

How to turn off multiprocessing in TensorFlow Quantum

Some background: I'm currently running the same training algorithm with a classical neural network and a quantum circuit, respectively. The NN is implemented in Keras with a TensorFlow backend, the ...
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1answer
74 views

IBM Q Experience - Can it be used draw out ML inferences? [closed]

Are there Quantum-enhanced Machine Learning algorithms that can be implemented via Qiskit in IBM Q Experience and obtain valuable inferences faster than their classical counterparts from datasets of ...
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1answer
83 views

Initial assumption of the unitary that allows us to estimate the label function

You can find the paper here , in which they describe the architecture of a QNN that can be used to learn binary functions and correctly classify unseen data. They say that for each binary label ...
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1answer
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Question About Measuring an Operator For Quantum Neural Network Paper

I'm currently reading the paper: https://arxiv.org/pdf/1802.06002.pdf I'm a little bit stuck on the step of how to determine the following quantity: Where U is a unitary operator acting on $|z,1\...
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1answer
164 views

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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1answer
69 views

Quantum NN vs Quantum-Inspired NN

I can't find the true difference between Quantum Neural Network (QNN) and Quantum-Inspired Neural Network (QINN). I have multiple guesses: QINN and QNN are absolutely the same thing (all QNNs are ...
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1answer
165 views

New Hybrid-HHL algorithm vs VQLS

A team of researchers has realized hybrid quantum algorithm for solving a linear system of equations with exponential speedup that utilizes quantum phase estimation, the algorithm demonstrates quantum ...
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1answer
76 views

Will NISQ based algorithms be useful in fault-tolerant Quantum computers?

As a data scientist, I want to use the cutting edge algorithms of machine learning to build my models, I am interested in quantum machine learning, the recent research in QML is about variational ...