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Found 9 papers, 1 papers with code
A unifying primary framework for quantum graph neural networks from quantum graph states
We show that they can be used either as a parameterized quantum circuits to represent neural networks or as an underlying structure to construct graph neural networks on quantum computers.
Federated learning with distributed fixed design quantum chips and quantum channels
This can provide efficiency over the models where the parameter vector is sent via classical or quantum channels and local gradients are obtained through the obtained values of these parameters.
A Simple Quantum Blockmodeling with Qubits and Permutations
In general, through performing this task, row and column permutations affect the fitness value in optimization: For an $N\times N$ matrix, it requires $O(N)$ computations to find (or update) the fitness value of a candidate solution.
Dimension reduction and redundancy removal through successive Schmidt decompositions
We show that data with distributions such as uniform, Poisson, exponential, or similar to these distributions can be approximated by using only a few terms which can be easily mapped onto quantum circuits.
On the explainability of quantum neural networks based on variational quantum circuits
Ridge functions are used to describe and study the lower bound of the approximation done by the neural networks which can be written as a linear combination of activation functions.
A walk through of time series analysis on quantum computers
Since some time series data can be also considered as continuous functions, we can expect quantum machine learning models to do many data analysis tasks successfully on time series data.
A Simple Quantum Neural Net with a Periodic Activation Function
In this paper, we propose a simple neural net that requires only $O(nlog_2k)$ number of qubits and $O(nk)$ quantum gates: Here, $n$ is the number of input parameters, and $k$ is the number of weights applied to these parameters in the proposed neural net.
A Generalized Circuit for the Hamiltonian Dynamics Through the Truncated Series
In this paper, we present a method for the Hamiltonian simulation in the context of eigenvalue estimation problems which improves earlier results dealing with Hamiltonian simulation through the truncated Taylor series.
A Quantum Implementation Model for Artificial Neural Networks
In quantum computing, the phase estimation algorithm is known to provide speed-ups over the conventional algorithms for the eigenvalue-related problems.
