Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Quantum algorithms for simple fluid flows

Julia Ingelmann

Published Jan 1, 2024• 0 citations
Publisher

Abstract

This repository contains Python scripts implementing quantum algorithms for simulating simple fluid flows.
DOI
10.5281/zenodo.14216585

Related papers

Anderson Model HEOM Implementation - Code and Data

Théodore GOUMAÏ VEDEKOÏ, Jean-Pierre Tchapet Njafa, S. G. Nana Engo

Complete computational implementation for the manuscript “Comparative Study of the Anderson Model inWeak and Strong Interaction Regimes: Implementations in Julia (HierarchicalEOM.jl) and Python (QuTiP)”. This repository contains: Python implementation using QuTiP Julia implementation using HierarchicalEOM.jl All Jupyter notebooks reproducing manuscript figures Temperature study scripts Complete documentation and installation guides All simulations are fully reproducible using the provided code.

•Jan 11, 2026•0 citations

Towards simulating fluid flows with quantum computing

Sachin S. Bharadwaj, Katepalli R. Sreenivasan

arXiv:2409.09736•Sadhana•Mar 19, 2025•20 citations

wsantmartins/2025RydPenning: Quantum simulation with Rydberg ions in a Penning trap

Wilson S. Martins

Quantum simulation with Rydberg ions in a Penning trap Authors: Wilson S. Martins, Markus Hennrich, Ferdinand Schmidt-Kaler, and Igor Lesanovsky This repository contains the numerical simulations used to generate the figures presented in the main text of the manuscript. All scripts are written in Python (.py) and generate output figures in the user's local directory. The most important folders in this repository are: https://github.com/matrices Contains the operators expressed in the fine-structure basis, truncated at orbital angular momentum l={S,P,…,H}. This includes, for example: The total Hamiltonian The dipole operator These matrices are used throughout the simulations. @figures Contains the post-processed figures in .pdf and .svg format used in the manuscript. The simulation scripts save pre-processed figures in this folder, which are subsequently edited for final presentation.

•Dec 8, 2025•0 citations

A Review of Quantum Machine Learning and Quantum-inspired Applied Methods to Computational Fluid Dynamics

Cesar A. Amaral, Vinícius L. Oliveira, Juan P. L. C. Salazar +1 more

arXiv:2510.14099•Brazilian Journal of Physics•Dec 12, 2025•4 citations

Quantum-Ready and Quantum-Inspired Computing for Fluid Dynamics

Peyman Givi

Bulletin of the American Physical Society•Nov 22, 2021•0 citations

HLS Implementation of Quantum Shor’s Algorithm Using Matrix Pruning

Juhi Wani, Tarun Kumar Allamsetty, Rushikesh Gherde +1 more

Shor’s integer factoring algorithm served as a significant breakthrough in the field of quantum computing owing to its potential applications in modern cryptography. Simulating quantum algorithms in a way that offers a close imitation of quantum computers is required to provide some insight viably. This paper proposes an FPGA implementation of the Shor’s algorithm by employing High Level Synthesis (HLS). A detailed method of implementation including design of data-flow graphs, pruning matrices using Python scripts, writing a HLS compatible C++ code, and subsequent hardware synthesis is presented for an 8-qubit system. An estimated maximum frequency of 180 MHz has been achieved in this design.

International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)•Apr 21, 2022•2 citations
Complete computational implementation for the manuscript “Comparative Study of the Anderson Model inWeak and Strong Interaction Regimes: Implementations in Julia (HierarchicalEOM.jl) and Python (QuTiP)”. This repository contains: Python implementation using QuTiP Julia implementation using HierarchicalEOM.jl All Jupyter notebooks reproducing manuscript figures Temperature study scripts Complete documentation and installation guides All simulations are fully reproducible using the provided code.
Quantum simulation with Rydberg ions in a Penning trap Authors: Wilson S. Martins, Markus Hennrich, Ferdinand Schmidt-Kaler, and Igor Lesanovsky This repository contains the numerical simulations used to generate the figures presented in the main text of the manuscript. All scripts are written in Python (.py) and generate output figures in the user's local directory. The most important folders in this repository are: https://github.com/matrices Contains the operators expressed in the fine-structure basis, truncated at orbital angular momentum l={S,P,…,H}. This includes, for example: The total Hamiltonian The dipole operator These matrices are used throughout the simulations. @figures Contains the post-processed figures in .pdf and .svg format used in the manuscript. The simulation scripts save pre-processed figures in this folder, which are subsequently edited for final presentation.
Shor’s integer factoring algorithm served as a significant breakthrough in the field of quantum computing owing to its potential applications in modern cryptography. Simulating quantum algorithms in a way that offers a close imitation of quantum computers is required to provide some insight viably. This paper proposes an FPGA implementation of the Shor’s algorithm by employing High Level Synthesis (HLS). A detailed method of implementation including design of data-flow graphs, pruning matrices using Python scripts, writing a HLS compatible C++ code, and subsequent hardware synthesis is presented for an 8-qubit system. An estimated maximum frequency of 180 MHz has been achieved in this design.