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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Is this Quantum Neural Network overfitting?

In the accuracy graphs (attached the graph images below) shown in this code (Binary Classification for Fraud Detection): validation loss is greater than training loss training accuracy is greater ...
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1 answer
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Tuning hyperparameters of QSVM

While implementing QSVM algorithm and I am facing some problems. I followed this tutorial: https://qiskit.org/documentation/stable/0.24/tutorials/machine_learning/01_qsvm_classification.html While ...
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How linear combination of unitaries gradient work (Qiskit, PennyLane)?

I'm trying to implement linear combination of unitaries(LCU) gradient from Qiskit Gradient Framework but on PennyLane. First, i looked through the source code in Qiskit. In Qiskit LCU gradient if we ...
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How can I change the default mapping function in ZZ feature map?

I am working with QSVM and have been reading a few things about this. I tried QSVM from the qiskit documentation as well. I also went through the link Quantum circuit for the ZZ feature map which ...
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Implementing Logistic Regression on IBMQ Hardware

I'm trying to implement a logistic regression inference circuit on NISQ hardware by following some of the proposed approaches below, [1] Hai-Ling Liu et al.Quantum algorithm for logistic regression. ...
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Hardware efficient ansatz versus Alternating Layered Ansatz

I'm reading a paper Cost function dependent barren plateaus in shallow parametrized quantum circuits. There they introduce what is called Alternating Layered Ansatz (ALA). It is said that: The ...
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Quantum GAN implementation

Can anyone provide a good link to understand how to implement qgan using pytorch in qiskit. Trying to understand this ( https://qiskit.org/documentation/machine-learning/tutorials/...
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1 answer
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Kernel ridge regression with qiskit's FeatureMap shows nonlinear patterns outside [0,1] range

I'm implementing a kernel ridge regressor using qiskit's FeatureMap and QuantumKernel to compute the alpha parameters of the solution. If I try to fit my model with non-normalized features I obtain ...
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What are "unbounded loss functions" and "unbounded operators"?

I am reading this paper: Quantum Generative Training Using Rényi Divergences. In it, the authors mention the following multiple times: "...an unbounded loss function can circumvent the existing ...
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How could I choose cost function in Qiskit TwoLayerQNN?

So here is the problem, I've found that in a TwoLayerQNN, the backward gradient is only to minimize the expectation of observable I've chosen. But I'm not going to minimize the predict of the input, ...
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1 answer
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Is quantum machine learning faster than classical machine learning (at the moment)?

We all know that quantum computing is rapidly developing and somehow being used in the AI field. However, it seems like there's no specific comparison between quantum machine learning (could be deep ...
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Silicon and Germanium semiconductors mixture in quantum computing

Can Silicon and Germanium semiconductors mixture (chemical reaction) with some other chemical elements (if required) assist in creating new and existing robust electronic components? Si + Ge + ? + ? = ...
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Quantum machine learning: only a boost to computation or something more? [closed]

I'm new in the area and I'm quite curious. What are the main advantages of quantum machine learning over "classical" machine learning (from sklearn library, for example)? Is just only a ...
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How does the formula for the parameter shift rule change if we measure in the $X$ basis?

I'm studying the parameter shift rule and got stuck when improving an example with Pauli operators in https://arxiv.org/abs/1803.00745. This paper shows up $\partial_\mu f=\frac{1}{2}(f(\mu+\frac{\pi}{...
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1 answer
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Is noise the only reason that makes the results of Quantum Support Vector Machine (QSVM) and Classical SVM differ, when we use a large dataset?

When I used and tested out the Quantum Support Vector Machine for just 120 samples of a large dataset (Training - 100 and Testing - 20). Its kernel classification results were quite close to the ...
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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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5 votes
3 answers
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Software tools to train quantum circuits with parameters

I want to play with the following problem. Given a unitary $U$ with parameters $\theta$ I want to optimize these parameters to make $U$ match some target unitary $V$, i.e. to find $\operatorname{...
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2 answers
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Are all computational resources reducible to the time resource?

It's well known that in most (if not all?) computations you can trade time and space resources. An extreme example might be creating an infinitely large lookup table of all composites produced from ...
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Quantum Boltzmann Machine and Gibbs state

I was looking into Quantum Boltzmann Machine and ran into Gibbs State as a part of training. Since new to this field, it would be good to have good intuitions why Gibbs state is related. Is Gibbs ...
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2 votes
1 answer
135 views

how can we save a model using qiskit_machine_learning?

How can we save a trained model using Qiskit Machine Learning library? I've built a VQC classification model and once finished, I'd like to save the different models to be loaded later. There is a <...
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1 vote
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Circuit state preparation using amplitude encoding

I am following an example of preparing an input state using amplitude encoding from this book. How to calculate $\beta_1^1$ using given formula above? In my understanding, $\beta_1^1 = 2\arcsin(\frac{\...
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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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2 votes
1 answer
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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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6 votes
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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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2 votes
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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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3 votes
2 answers
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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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4 votes
1 answer
323 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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4 votes
1 answer
134 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 ...
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4 votes
1 answer
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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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5 votes
1 answer
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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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6 votes
1 answer
261 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 ...
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2 votes
2 answers
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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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4 votes
1 answer
86 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 ...
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1 vote
0 answers
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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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6 votes
1 answer
144 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 ...
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2 votes
0 answers
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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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1 vote
0 answers
80 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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1 vote
2 answers
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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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2 votes
2 answers
164 views

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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5 votes
2 answers
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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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9 votes
1 answer
539 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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3 votes
0 answers
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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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5 votes
1 answer
110 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 ...
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5 votes
4 answers
296 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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7 votes
3 answers
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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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3 votes
0 answers
61 views

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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2 votes
1 answer
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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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4 votes
1 answer
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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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9 votes
2 answers
691 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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