@article{@misc{knudsen2020solving,       title={Solving Differential Equations via Continuous-Variable Quantum Computers},        author={Martin Knudsen and Christian B. Mendl},       year={2020},       eprint={2012.12220},       archivePrefix={arXiv},       primaryClass={quant-ph} },
	author = {Knudsen, Martin and  Mendl, Christian},
	title = {Neural Network Continuous-Variable Quantum Computing},

        journal = {arXiv},
	year = {2020},


        month = {Dec},





        language = {en},

        abstract = {We explore how a continuous-variable (CV) quantum computer could solve a classic differential equation, making use of its innate capability to represent real numbers in qumodes. Specifically, we construct variational CV quantum circuits [Killoran et al., Phys.~Rev.~Research 1, 033063 (2019)] to approximate the solution of one-dimensional ordinary differential equations (ODEs), with input encoding based on displacement gates and output via measurement averages. Our simulations and parameter optimization using the PennyLane / Strawberry Fields framework demonstrate good convergence for both linear and non-linear ODEs.},


        note = {Archive-ID: 2020_12_22_SolvingDifferentialEquationsviaContinuous-VariableQuantumComputers},
	
        url = {https://arxiv.org/abs/2012.12220},
}