Applications of Cluster Perturbation Theory Using Quantum Monte Carlo Data

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	Similar Articles Articles by Fei Lin Articles by Erik S. S?rensen Articles by Catherine Kallin Articles by A. John Berlinsky

20th International Symposium on High-Performance Computing in an Advanced Collaborative Environment (HPCS'06)

St. John's, Newfoundland

May 14-May 17

ISBN: 0-7695-2582-2

	ASCII Text	x
	Fei Lin, Erik S. S?rensen, Catherine Kallin, A. John Berlinsky, "Applications of Cluster Perturbation Theory Using Quantum Monte Carlo Data," High Performance Computing Systems and Applications, Annual International Symposium on, pp. 27, 20th International Symposium on High-Performance Computing in an Advanced Collaborative Environment (HPCS'06), 2006.

	BibTex	x
	@article{ 10.1109/HPCS.2006.12, author = {Fei Lin and Erik S. S?rensen and Catherine Kallin and A. John Berlinsky}, title = {Applications of Cluster Perturbation Theory Using Quantum Monte Carlo Data}, journal ={High Performance Computing Systems and Applications, Annual International Symposium on}, volume = {0}, year = {2006}, issn = {1550-5243}, pages = {27}, doi = {http://doi.ieeecomputersociety.org/10.1109/HPCS.2006.12}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - High Performance Computing Systems and Applications, Annual International Symposium on TI - Applications of Cluster Perturbation Theory Using Quantum Monte Carlo Data SN - 1550-5243 SP EP A1 - Fei Lin, A1 - Erik S. S?rensen, A1 - Catherine Kallin, A1 - A. John Berlinsky, PY - 2006 KW - null VL - 0 JA - High Performance Computing Systems and Applications, Annual International Symposium on ER -

Fei Lin, McMaster University, Canada

Erik S. S?rensen, McMaster University, Canada

Catherine Kallin, McMaster University, Canada

A. John Berlinsky, McMaster University, Canada

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/HPCS.2006.12

We study cluster perturbation theory [Phys. Rev. Lett. 84, 522 (2000)] when auxiliary field quantum Monte Carlo method is used for solving the cluster hamiltonian. As a case study, we calculate the spectral functions of the Hubbard model in one and two dimensions and compare our results for the spectral functions to results obtained using exact diagonalization to solve the cluster hamiltonian. The main advantage of using quantum Monte Carlo results as a starting point is that the initial cluster size can be taken to be considerably larger and hence potentially capture more of the relevant physics. The drawback is that quantum Monte Carlo methods yield results at imaginary times with stochastic errors.

Citation:

Fei Lin, Erik S. S?rensen, Catherine Kallin, A. John Berlinsky, "Applications of Cluster Perturbation Theory Using Quantum Monte Carlo Data," hpcs, pp.27, 20th International Symposium on High-Performance Computing in an Advanced Collaborative Environment (HPCS'06), 2006

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