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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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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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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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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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 "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 ...
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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 ...
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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 ...
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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 ?
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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:
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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 ...
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Calculating the Inner Product using Quantum Phase Estimation
I'm following the method laid out in https://arxiv.org/abs/2011.03429 (Page 23 Equations 13-23) to calculate the inner product of two amplitude embedded vectors using Quantum Phase Estimation. I'm ...
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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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Does anyone know how to use TF Quantum with real hardware data?
I'm currently trying to embed a tensorflow model for denoising measurements as a tensorflow quantum model, and at some point I'd like for this to be able to run on hardware. After reading through all ...
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Is there any QML algorithm that performs better than a classical one in practice?
As of today, is there any QML algorithm that performs better than a classical one in practice? I have been looking for quantum neural networks, quantum kernels and quantum CNN, but none so far have ...
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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 ...
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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 ...
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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:
...
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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 ...
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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 ...
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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 ...
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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?
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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 ...
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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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How to implement purification of the density matrix into a pure state of enlarged system in the quantum PCA algorithm?
I am trying to implement the quantum PCA algorithm to reduce the number of features (over 700 features) for a machine learning model. The original algorithm is proposed in this paper. I found a ...
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Implementation of Quantum PAC learning classifier
I am working on a project related to Boosting of Quantum Classifiers of PAC format. And I am a little confused about how do we go about implementing a PAC classifier. The basic idea is that we have to ...
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How to train a Quantum Neural network for regression model in supervised learning
We want to train a parameterised circuit(which is our neural network - from this paper. Now our final circuit looks a little like
Let's say there are n training cases. So I have n |gt> vectors ...
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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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Quantum Machine Learning: how to get effective time of training/scoring
I am trying to examine the potential of Quantum Machine learning in terms of performance and time compared to classical algorithms. I am using both Qiskit's QSVM and scikit's SVM with Qiskit Quantum ...
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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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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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motivation behind using PQCs in QML and Variational Algorithms
Parameterized quantum circuits (PQCs) are a key component in many quantum machine learning (QML) and variational quantum algorithms, such as the Variational Quantum Eigensolver (VQE) and quantum ...
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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 ...
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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 :
...
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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 ...
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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 ...
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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/...
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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 ...
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