Efficient quantum state preparation of multivariate functions using tensor networks

• URL: http://arxiv.org/abs/2511.15674v1
• Date: Wed, 19 Nov 2025 18:18:27 GMT
• Title: Efficient quantum state preparation of multivariate functions using tensor networks
• Authors: Marco Ballarin, Juan José García-Ripoll, David Hayes, Michael Lubasch,
• Abstract summary: We introduce tensor network algorithms that are efficient with regard to dimensionality and take gate errors into account.<n>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.
• Score: 1.0499611180329804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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