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Quantum Simulation

Efficient quantum state preparation of multivariate functions using tensor networks

arXiv

Authors: Marco Ballarin, Juan José García-Ripoll, David Hayes, Michael Lubasch

Year

2025

Paper ID

16915

Status

Preprint

Abstract Read

~2 min

Abstract Words

103

Citations

N/A

Abstract

For the preparation of high-dimensional functions on quantum computers, we introduce tensor network algorithms that are efficient with regard to dimensionality, optimize circuits composed of hardware-native gates and take gate errors into account during the optimization. To avoid the notorious barren plateau problem of vanishing gradients in the circuit optimization, we smoothly transform the circuit from an easy-to-prepare initial function into the desired target function. We show that paradigmatic multivariate functions can be accurately prepared such as, by numerical simulations, a 17-dimensional Gaussian encoded in the state of 102 qubits and, through experiments, a 9-dimensional Gaussian realized using 54 qubits on Quantinuum's H2 quantum processor.

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For the preparation of high-dimensional functions on quantum computers, we introduce tensor network algorithms that are efficient with regard to dimensionality, optimize...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2511.15674 [2] arXiv https://arxiv.org/abs/2511.15674 [3] Publisher https://arxiv.org/abs/2511.15674

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