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 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
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How to run pegasosQSVC in backend using qiskit ibm runtime session?

I am trying to run pegasosQSVC on quantum backend using a qiskit-ibm-runtime session, following is the code I am using : ...
khalil mehdi's user avatar
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How to run PegasosQSVC in quantum backend or simulator?

I am making my classification project in quantum machine learning, currently trying to run PegasosQSVC in backend! The code runs normally in my environment however when I connect to backend using <...
khalil mehdi'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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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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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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Outputing classification probability from the Qiskit VQC

I am new to Qiskit. For the last few days, I have been trying to train my first VQC to do some classification task. Now the VQC is successfully running but it could only output a label whenever I ask ...
Zhelun Li's user avatar
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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 answer
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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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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: &...
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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
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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
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2 answers
478 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
3 votes
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58 views

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 ...
Quantum Brilliance's user avatar
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175 views

Qiskit Variational Quantum Regressor - Qiskit VQR

I am new to quantum machine learning and I am trying to build a VQR with Qiskit. The input and target data to my model both have shape (32,4), where 32 is the number of samples and 4 is the number of ...
Jean-Gabriel Chenard's user avatar
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How to go from a classical LSTM cell to a quantum LSTM cell where the neural network parts in the LSTM cell's gates are replaced by quantum circuits?

The input data I have is a tensor with shape (num_samples, num_timesteps, num_features). A single datapoint in my problem case is a feature vector of dim = 4 which conceptually corresponds to an ...
Jean-Gabriel Chenard's user avatar
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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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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
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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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The necessity of a qRAM-based state preparation in qSVT-type problems

I've been reading a couple of papers regarding qPCA and its dequantisation. In the course of my reading, it appears to me that one of the ingredients that makes this dequantisation meaningful is the ...
Song of Physics's user avatar
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Qiskit_machine_learning QNNEstimator 'Estimator job failed' when using CU-Gates

I'm trying to construct a QNN using controlled arbitrary unitary gates. While some simple code versions work perfectly for controlled single rotations, the moment I add CU gate with parameters, the ...
MaxM's user avatar
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1 answer
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Getting High cost function in code implementation of VQLS pennylane tutorial

I am currently trying to implement the tutorial in pennylane https://pennylane.ai/qml/demos/tutorial_vqls.html for very complex example in 3 Qubit and cost function is very high in spite of adding ...
Nithin Reddy Govindugari's user avatar
2 votes
1 answer
145 views

Detailed references on Quantum Principal Component Analysis

I am looking for review articles and other online resources on qPCA based on Lloyd's original proposal. I have found the original source to be slightly hand-wavy with the details so I am having ...
Song of Physics's user avatar
2 votes
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74 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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Most promising QML algorithms in the NISQ era

This question was asked in the previous years, but how is 2023 state of the art Quantum Machine Learning ? Things seem to go fast in this area, for instance I saw Thales used 4 qubits for quantum ...
Duen's user avatar
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How to take gradient of the `tfq.layers.State` output?

I am using the following code for building a quantum circuit as a custom tf.keras.layers.Layer: ...
Shuhul Handoo's user avatar
5 votes
1 answer
305 views

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 ...
Song of Physics's user avatar
1 vote
0 answers
79 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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1 answer
207 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
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1 answer
426 views

What do arbitrary encoding circuits provide?

I'm about to implement my first VQC for image classification and I'm trying to figure out which data encoding method could fit better the problem. From what I've understood, there are three main ...
Paul's user avatar
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1 answer
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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 (...
Bill's user avatar
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1 vote
2 answers
487 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
3 votes
1 answer
133 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
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1 answer
211 views

Pennylane: Pennylane can not train the parameters of the problem

I asked this question in the Pennylane forum, but there was no reply for a long time, the link is: https://discuss.pennylane.ai/t/why-does-the-embedding-metric-learning-case-not-work/2211?u=rx1 The ...
Ren-Xin Zhao's user avatar
2 votes
1 answer
208 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
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2 votes
4 answers
152 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
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3 votes
2 answers
316 views

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 ...
JERMY's user avatar
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1 vote
1 answer
741 views

How to create a quantum circuit with 800+ features using PennyLane

I am new to Quantum ML, and I am currently using PennyLane to do the QML activity. As per this article, total number of features is equal to the total number of qubits. (In the example, they have ...
Shikhar's user avatar
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2 votes
0 answers
54 views

A path towards building quantum Computing graduation project for undergraduates

I need help. I'm a computer science student with a Data science major. I have a final graduation project this year. With that, I want to create a project in the Quantum computing field. I'm already ...
Hamza Kamel Ahmed's user avatar
6 votes
1 answer
277 views

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 ...
mpro's user avatar
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0 answers
59 views

Quantum Kernel Method: If the input is the QK provided by the variable qiskit, is it still true?

The puzzle is from Case 1: https://qiskit.org/documentation/machine-learning/tutorials/03_quantum_kernel.html Case 2: https://qiskit.org/documentation/machine-learning/tutorials/...
Ren-Xin Zhao's user avatar
2 votes
1 answer
232 views

What are the practical advantages of quantum GANs with respect to classical ones?

I read some papers on Quantum GANs, for instance this one and this one. I also noticed all the main quantum computing frameworks have a tutorial on quantum GANs, e.g. qiskit. However I don't really ...
mpro's user avatar
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What is the relationship between the number of shots and the performance of a quantum agent?

What is the relationship between number of shots and the performance of quantum agent in Quantum neural network? and what is the limit of number of shoots in QASM simulator?
Getahun Fikadu's user avatar
2 votes
2 answers
851 views

How well different featuremap encode the data?

Recently, I was doing research on QML. Qiskit gave detailed steps on how to encode data into quantum states, but I was confused about one point: there are different feature mapping methods under ...
Mistico013's user avatar
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75 views

Quantum Kernel Machine Learning loss function and the plot of kernel?

When I was doing quantum machine learning, after building the quantum kernel, I drew the graph of the loss function changing with the iteration function and the graph of the quantum kernel, but I ...
Mistico013's user avatar
1 vote
0 answers
82 views

keras agents fails in DQNAgent using PQC during clonation for target

I have some issues using keras-rl2 with tensorflow_quantum and VQC (using identical architecture as https://www.tensorflow.org/quantum/tutorials/quantum_reinforcement_learning) After the creation of ...
Eva Andres's user avatar
2 votes
1 answer
285 views

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 ...
user14924's user avatar
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1 vote
1 answer
1k views

Hybrid Quantum LSTM in Qiskit

I read this article on a Hybrid Quantum LSTM in Pennylane and I'm trying to replicate it in Qiskit. Nevertheless it doesn't seem to work very well. Here's my code ...
mpro's user avatar
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3 votes
1 answer
107 views

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 ...
userflux9674's user avatar