qlbm documentation

The long-term goal of qlbm is to be a quantum computational fluid dynamics (QCFD) solver for fault-tolerant quantum computers running on a heterogeneous quantum-classical high-performance computer (QHPC). Currently, the primary aim of qlbm is to accelerate and improve the research surrounding Quantum Lattice Boltzmann Methods (QLBMs). On this website, you can find the Internal Documentation of the source code components that make up qlbm. A preprint describing qlbm in detail is available on arXiv [1].

qlbm is made up of 4 main modules. Together, the Base Classes, Collisionless Circuits, and Space-Time Circuits module handle the parameterized creation of quantum circuits that compose QBMs. The Lattices and Geometry module parses external information into quantum registers and provides uniform interfaces for underlying algorithms. The Infrastructure module integrates the quantum components with Tket, Qiskit, and Qulacs transpilers and runners. The Other Tools module contains miscellaneous utilities.

qlbm currently supports two algorithms:

1. The Collisionless QLBM (CQLBM) first described in [2] and later expanded in [3].

2. Space-Time QLBM (STQLBM) described in [4].

Internal Documentation

Detailed documentation of qlbm.

Internal Documentation

Tutorials

Hands-on examples.

Tutorials

Code documentation

• Internal Documentation
  • Base Classes
  • Collisionless Circuits
  • Space-Time Circuits
  • Lattices and Geometry
  • Infrastructure
  • Other Tools
• Tutorials
  • Visualizing Collisionless circuits
  • Visualizing Space-Time circuits
  • Visualizing Geometry
  • Flowfield Visualization

References

[1]

Călin A. Georgescu, Merel A. Schalkers, and Matthias Möller. Qlbm – a quantum lattice boltzmann software framework. arXiv preprint arXiv:2411.19439, 2024.

[2]

Merel A. Schalkers and Matthias Möller. Efficient and fail-safe quantum algorithm for the transport equation. Journal of Computational Physics, 502:112816, 2024.

[3]

Merel A. Schalkers and Matthias Möller. Momentum exchange method for quantum boltzmann methods. Computers & Fluids, 285:106453, 2024.

[4]

Merel A. Schalkers and Matthias Möller. On the importance of data encoding in quantum boltzmann methods. Quantum Information Processing, 23(1):20, 2024.