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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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Implementing stochastic gradient descent on hybrid quantum-classical optimization

I am working on a project in which I need to simulate the paper https://arxiv.org/abs/1910.01155. So I am a complete beginner to qiskit but I read its documentation so I know some stuff. So basically ...
Kutubkhan Bhatiya's user avatar
4 votes
1 answer
59 views

Is there any machine learning method for finding quantum error correction codes?

To define a quantum error correction code, first one needs to model noise, such as Pauli noise, dephasing noise, etc. Then according to the noise, look for the code space, stabilizer, and logical ...
mingo's user avatar
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Qiskit VQC - how does VQC associate the measurement results with labels?

I'm working through the example here, and am struggling to see at what stage it is specified how the measurement results/ or expectations collected from the circuit are used to decide which label to ...
John's user avatar
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Implementation of identity block initialisation strategy for mitigating barren plateaus

I have been trying to implement this paper on identity block initialisation strategy for barren plateau mitigation but I don't really understand how one would apply it to a parameterised circuit with ...
Moto's user avatar
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3 votes
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Gradient-free optimization in Qiskit without using pre-defined classes

Basically I want to build a gradient-free optimizer that classifies a very simple dataset (e.g. the sklearn make_moons) using scipy.optimize (Nelder-Mead or Powell ...
Kian's user avatar
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Quantum data as input for Quantum Neural Net

I'm new to quantum machine learning, and I wanted to know how quantum data is processed in a quantum neural net. For example, if I am training a QNN to classify entangled circuits from non-entangled ...
beginnerCoder7's user avatar
2 votes
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27 views

Quantum Convolutional Neural Network not producing gradients

I am trying to bulid a quantum convolutional neural network for image classification with Pennylane and Keras but the model isn't training and I keep getting the warning: WARNING:tensorflow:Gradients ...
Umm's user avatar
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Resources on quantum machine learning for beginners

I am a first year PhD Physics student, working on quantum information theory. I am planning to learn machine learning and in particular quantum machine learning. I do not have any prior exposure to ...
Anindita Sarkar's user avatar
3 votes
1 answer
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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
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Turn expectation values back into classical data

How are expectation values turned back into classical data for evaluation? I have a circuit performing a regression task that returns the expectation value of the Pauli Z operator. I would like to ...
camaya's user avatar
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Transform QLSTM model output expectation values back into classical data

What is the typical procedure for transforming a model's expvals back into the classical data format? I'm new to QML and need some insight. I have some expvals that were the output of model i.e. ...
camaya's user avatar
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Qnode model gradient of inputs (not parameters!) question

I am trying to use qml to do physics informed quantum machine learning within Tensorflow. I know with TF, to get derivatives of the network's inputs (df/dx, for example), you can use with tf....
Corey's user avatar
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1 answer
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Quantum neural networks and quantum kernels deal with nonlinearities

I'm trying to understand quantum neural networks from reading Alchieri et al.'s review paper. The following paragraph describes the differences between classical and quantum neural networks: Also, ...
Medulla Oblongata's user avatar
2 votes
1 answer
76 views

Optimizing a parametrized Quantum Circuit in batches does not decrease the cost function while unbatched optimization does

I want to optimize a variational quantum circuit to maximize the Hilbert-Schmidt Distance between the different classes of the UCI breast cancer data set. When I choose to use batched optimization, ...
jo87casi's user avatar
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3 votes
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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
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quantum algorithm for multilevel/hierarchical dataset

The Radon dataset is a well-known hierarchical/multilevel dataset. It contains Radon samples from houses in counties across the United States. The goal of the model is to estimate the (log) Radon ...
inq's user avatar
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Effect of error correction gates in QCNN

In Iris Cong et. al. (2019) they propose a Quantum Convolutional Neural Network that utilizes mid-circuit measurements to control an error-correcting ansatz $V_j$. This is the equivalent of a pooling ...
Federico Tiblias's user avatar
1 vote
1 answer
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How to save a hybrid Tensorflow and Pennylane model?

I implemented a hybrid model with Keras and Pennylane that looks like this: The quantum layer is basically a quantum circuit converted to a keras layer with the ...
Ryan Wang's user avatar
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1 vote
1 answer
134 views

Error loading saved hybrid quantum (pennylane + tensorflow keras) model: Unknown layer: 'KerasLayer'

I'm creating a hybrid model consisting of classical convolutional layers and a quantum output using Tensorflow. I can save the model in either .h5 or .keras format, but when I load them with the code <...
Ryan Wang's user avatar
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1 vote
1 answer
81 views

What's the case when parameter-shift rule does not hold?

When the parameterized unitary is of the form $e^{-i\theta V}$, where $V$ is a Hermitian operator of the unitary, we can use parameter shift rule to calculate the gradient. In this paper, it says: &...
peachnuts's user avatar
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Could kernels also be used for Reinforcement Learning?

In this paper Kernel-Based Reinforcement Learning (2002), a classical kernel-based method was demonstrated for Reinforcement Learning , which indicates that classical research in this direction is ...
BootBootBoot's user avatar
1 vote
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Tensorflow_quantum hybrid models tf-quantum

I am trying to QCNN for MNIST classification equivalent to that built in. I’m having problems trying to pass my quantum circuit built with cirq as a Keras layer. Here’s what I have: ...
Kieran McDowall's user avatar
4 votes
1 answer
154 views

Why is the quantum kernel $\kappa(x,x')=|\langle\phi(x)|\phi(x')\rangle|^2$ defined with a square?

I've always wondered why the quantum kernel method \begin{equation}\label{QKM1} \kappa (x,x')=|\langle \phi(x) |\phi(x') \rangle {{|}^{2}} \end{equation} must be a square. After reading “Supervised ...
Ren-Xin Zhao's user avatar
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2 answers
546 views

Cannot import tensorflow_quantum module in Colab

I was trying to install the tensor flow quantum module using the one given in their official website but it is showing these errors while installing. ...
Siddharth Sethi's user avatar
1 vote
1 answer
75 views

Pennylane variational classifier demo - need for padding

In the variational classifier demo from Pennylane, the data loading is performed with ...
Sarvapriya Tripathi's user avatar
1 vote
1 answer
135 views

How to determine which embedding method to use for QML?

So there are a lot of feature mapping techniques out there for Quantum Machine Learning, but I'm not sure which one to use for my next VQC. Can anyone explain when and why to use each of the following?...
Ryan Wang's user avatar
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1 vote
0 answers
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The possibility of an image classifier using quantum computer architecture?

Consider an exhaustive database of all contour images that can ever be created on a 16x16 grid. Out of the $2^{256}$ unique possibilities, could a quantum computer classify all the resulting images ...
LithiumPoisoning's user avatar
3 votes
0 answers
27 views

Question when deriving quantum differential privacy?

I met some problems when trying to derive proposition 4 in the paper Gentle measurement of quantum states and differential privacy. I know that intuitively, if we act on a single register of ρ, and ...
Zehong Fan's user avatar
1 vote
0 answers
48 views

Quanvolutional NN vs Quantum Convolution NN

There are 2 primary approaches for Image recognition using Quantum Neural Networks: 1. Quanvolutional one and 2. Quantum Convolutional Neural Networks. The primary difference is that in 1 we don't ...
Chan's user avatar
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1 vote
0 answers
65 views

Compatibility of TensorFlow Quantum on Windows

I created a Jupyter file around two years ago that relies on the following packages: However, it seems that Google has discontinued support for TensorFlow Quantum on Windows. This poses a problem as ...
ricklondon's user avatar
2 votes
1 answer
218 views

Comparative analysis of TensorFlow-Quantum, Pennylane, and Qiskit, to implement quantum CNNs

I am currently working on a project wherein I have to implement a Quantum Convolutional Neural Network. The best options for NISQ devices are hybrid algorithms, so I will try integrating quantum ...
Chan's user avatar
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1 vote
1 answer
82 views

Image classification using quantum variational circuit?

Image classification using variational quantum circuit is described in here. 3 image clusters having clearly separable 3 feature coordinates have been chosen to be: There are classical clustering ...
James's user avatar
  • 491
2 votes
1 answer
97 views

How the circuit covers the Hilbert Space

I am refreshing my functional analysis knowledge to learn quantum machine learning and I am getting confused on Hilbert spaces. What does it mean for a "circuit to cover the Hilbert Space" I ...
epsilonolispe's user avatar
0 votes
1 answer
408 views

How to assign and manipulate Qiskit's TwoLocal ParameterVector?

Below is my code in python where i just show Qiskit's TwoLocal variational circuit model. ...
Luccas Marim's user avatar
2 votes
0 answers
87 views

RealAmplitudes ansatz

Does someone know why RealAmplitudes ansatz is made like this ? I can't find any research paper on it. Why does it use 4 Ry Gate for one qubit ?
Duen's user avatar
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1 vote
1 answer
425 views

What are the advantages of angle embedding over amplitude embedding?

With $n$ qubits, $2^n$ features can be encoded with amplitude embedding, while only $n$ with angle embedding. So, is there any reason to use angle embedding?
salcc's user avatar
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1 vote
1 answer
120 views

Are there some cases where Grover's algorithm was used to improve machine learning performance?

Grover algorithm showing quantum advantage, are there some cases where it was used to improve Machine Learing performance ?
Duen's user avatar
  • 436
5 votes
2 answers
250 views

Simultaneous measurements and Bell basis measurements to estimate $\lvert\text{Tr}(\sigma \rho)\rvert^2$ in Huang et al. paper

Theorem 2 of this paper says if one is able to prepare $\rho^{\otimes k}$ then it is possible to predict expectation values of all $n$-qubit Pauli observables using $O(n)$ number of copies of $\rho$. ...
user8183310's user avatar
1 vote
0 answers
41 views

Quantum Generative Adversarial Network does not converge

I have built a quantum generative adversarial network model, in which the generator and the discriminator, both are quantum based model. The parametrized quantum circuit/ansatz of these two models are ...
Shuhul Handoo's user avatar
1 vote
0 answers
91 views

I am optimising a variational quantum circuit to learn a distribution $p(x)$, but it doesn't converge over a training set $\mathcal{X}$?

I am training a variational quantum circuit to learn distributions: given data $s(\vec{\lambda})$, what is the probability distribution for the parameterisation $\vec{\lambda}$, i.e. the posterior ...
JoJo's user avatar
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2 votes
1 answer
212 views

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 ...
MeltedStatementRecognizing's user avatar
1 vote
0 answers
193 views

Error in Implementing Quantum SVM

I am getting Type error of invalid parameter in base sampler module. From what I know sampler is constructor and doesn't take any parameter values, on the qisit.org tutorial of svm they have even said ...
Piyushgalav's user avatar
3 votes
1 answer
79 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
2 votes
1 answer
342 views

Which backend does the QSVM run on?

I'm trying to create a QSVM classifier model. I wanted to know which backend the fit() method uses to train the model. I checked on the IBM portal to see if there were any jobs being created, but ...
Amey Meher's user avatar
4 votes
1 answer
121 views

Graph Limits in Quantum Computing

Lovasz's book Large networks and Graph Limits mentions that their study of graph limits is motivatived applications to quantum computers, statistical physics, and models for the internet. They don't ...
user22511's user avatar
1 vote
2 answers
598 views

How to convert classical machine learning dataset to quantum dataset?

I'm looking for a way to convert images dataset to quantum dataset format to apply some quantum machine learning algorithms. Is it possible? I have read about that and I found it is possible by using ...
ahmad alomari's user avatar
2 votes
0 answers
99 views

How can we compute the gradient in a Quantum RNN?

I was looking into implementing a quantum recurrent neural network (QRNN) for a project, but I have some doubts about the computation of the gradient. There are a few papers that have implemented a ...
Alex Li's user avatar
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3 votes
1 answer
141 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
2 votes
1 answer
218 views

Hilbert space vs RKHS

I believe in quantum machine learning, it is interesting to talk about RKHS(reproducing kernel Hilbert space) and Hilbert space where a quantum state lives in. How do we think of these two spaces? Are ...
Sam's user avatar
  • 437
2 votes
4 answers
166 views

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 ...
mpro's user avatar
  • 517