Questions tagged [quantum-neural-network]

A machine learning model or algorithm that combines concepts from quantum computing and artificial neural networks encompassing a variety of ideas, ranging from quantum computers emulating the exact computations of neural nets, to general trainable quantum circuits that bear only little resemblence with the multi-layer perceptron structure.

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Why does the parameter circuit include both the positive and negative shift terms?

In the derivation of the parameter shift rules in the original paper, the Hermitian generator $G$ of any unitary $U(\theta)=\exp[-i\theta G]$ satisfies $$ U(\pm\frac{\pi}{4r})=\frac{1}{\sqrt{2}}(I\mp\...
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How do I calculate the amount of qubits required for Image Classification with a Quantum Convolutional Neural Network?

I'm relatively new to the topic of QCNNs and I wanted to understand how the number of qubits is selected in the encoded quantum layer. Like is it based on the image we want to encode? What is the ...
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Does this quantum algorithm to check for a permuting function make sense? [closed]

Even "Deutsch's algorithm" seems too difficult. Maybe I found an algorithm that is more appropriate for people without knowledge. Less to explain. Easy to understand. Or do you have ...
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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 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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2 votes
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Adding trainable weights to feature inputs for a CircuitQNN?

Currently I'm trying to get together a QNN that can be trained to classify the normalized (-1, 1) IRIS Dataset on all 3 classes. For this I am using Qiskit's ...
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What is the input for the interpret of Quantum Variational Regressors?

I'm trying to implement my first variational regressor using qiskit. I would like to understand how the interpret of a CircuitQNN works. I need to define an interpret that works on the amplitudes of a ...
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Data encoding in the quantum perceptron model

In this paper, this figure shows the perceptron model used for quantum neural network. When realizing the inner product between weight vector and input vector, it defines a unitary transformation $U_W$...
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How to upload our dataset using pytorch when it is not present in torchvision?

I am trying to upload my dataset(SWELL-KW) instead of MNIST in "Hybrid quantum-classical Neural Networks with PyTorch and Qiskit" provided by IBM qiskit but it says ...
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6 votes
2 answers
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Understanding the definition of quantum neural network of Abbas et al. 2020

My Question based on this Paper https://arxiv.org/pdf/2011.00027.pdf "Power of Quantum Neural Networks" - Section 2. So I know that there are different ways to implement Neural Networks into ...
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Computing expectation value of product of observables in PennyLane

In PennyLane, the following circuit returns the expectation value of the PauliZ observable on qubit (wire) 1: ...
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Label function for a QNN designed to classify bit strings

The paper can be found https://arxiv.org/pdf/1802.06002.pdf here. They say that for each binary label function $l(z)$ where $l(z)=−1$ or $l(z)=1$, there exists a unitary $U$ such that, for all input ...
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3 votes
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Does TensorFlow Quantum tfq.convert_to_tensor work on custom gates?

I'm trying to use Cirq with TensorFlow Quantum to simulate a variational quantum classifier. There's a tutorial on the TFQ website on building a quantum neural network to classify a simplified version ...
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2 votes
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What is a "repeat until success quantum circuit" in quantum neural networks?

I am working now on a quantum neural network project and want a deep explanation on the Repeat Until Success circuit. What I know about this circuit is that it allows a nonlinear activation function ...
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