3.quantum-monte-carlo

Randomized algorithms

Contents

1. Simulated annealing and quantum annealing (Julia: Simulated annealing and Julia: Quantum annealing)
2. Quantum Monte Carlo and variational quantum Monte Carlo
   1. Restricted Boltzmann Model (RBM) (Python)
3. Quantum Random Walk

Reading

• Book: Quantum Monte Carlo Methods¹
• Thesis: Implementation of the Variational Monte Carlo method for the Hubbard model²

Researchers in the field

• Fakher Assaad - Computational solid state physics
• Anders Sandvik - Quantum many-body physics, Monte Carlo, spin models, strongly-correlated electrons
• Zi-Yang Meng - Condense matter physics
• You-Jin Deng - Computational Statistical Physics

Projects

• Reproduce: Classical signature of quantum annealing.³⁴⁵.
• Reproduce: Solving the quantum many-body problem with artificial neural networks²⁶.

References

Footnotes

1. James, Naoki Kawashima, and Philipp Werner., 2016, Quantum Monte Carlo Methods. Cambridge University Press ↩

2. Rüger, R., Goethe-universität, J.W., 2013. Implementation of the Variational Monte Carlo method for the Hubbard model. http://work.robertrueger.de/docs/mscthesis.pdf ↩ ↩²

3. Stella, L., Santoro, G.E., Tosatti, E., 2005. Optimization by quantum annealing: Lessons from simple cases. Phys. Rev. B 72, 014303. https://doi.org/10.1103/PhysRevB.72.014303 ↩

4. Wang, L., Rønnow, T.F., Boixo, S., Isakov, S.V., Wang, Z., Wecker, D., Lidar, D.A., Martinis, J.M., Troyer, M., 2013. Comment on: “Classical signature of quantum annealing.” https://doi.org/10.48550/arXiv.1305.5837 ↩

5. Boixo, S., Rønnow, T.F., Isakov, S.V., Wang, Z., Wecker, D., Lidar, D.A., Martinis, J.M., Troyer, M., 2014. Evidence for quantum annealing with more than one hundred qubits. Nature Phys 10, 218–224. https://doi.org/10.1038/nphys2900 ↩

6. Carleo, G., Troyer, M., 2017. Solving the quantum many-body problem with artificial neural networks. Science 355, 602–606. https://doi.org/10.1126/science.aag2302 ↩