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arXiv:2105.04736 · Nature Computational Science

Quantum embedding theories to simulate condensed systems on quantum computers

Christian Vorwerk, Nan Sheng, Marco Govoni, Benchen Huang, Giulia Galli

Published Jul 25, 2022• 63 citations
arXivHTMLPDF
DOI
10.1038/s43588-022-00279-0

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Quantum phase-field models are fundamental to understanding and predicting the behavior of complex quantum systems, particularly in condensed matter physics. Traditional modeling relies on computationally intensive techniques, often limiting the size and complexity of simulations. This research investigates a novel method for representing and simulating phase-fields using variational embedding techniques – a promising approach to overcome these limitations. We introduce a new framework that constructs a latent space for the phase-field, enabling the simulation of highly complex quantum systems with significantly reduced computational requirements. This work presents a mathematically rigorous framework and demonstrates its potential to revolutionize the modeling of quantum phase-fields, opening new avenues for accurate and efficient simulations.
Advances in quantum computing hold the potential to revolutionize the simulation of complex materials, including those critical for next-generation batteries. However, the complexity of typical battery materials is still prohibitive for exact simulations on current or near-term quantum hardware. One promising approach is the use of quantum embedding techniques, which enable the treatment of correlated electronic states in large systems by partitioning them into manageable subproblems. We explore the application of Density Matrix Embedding Theory (DMET) [1,2] as a quantum embedding method to bridge classical and quantum computational resources [3]. Within DMET, few correlated degrees of freedom can be solved on the quantum computer, while the remaining part is solved with classical methodology, self-consistently connected to the quantum part. This approach paves the way for real-world problems from battery simulation to profit from near-term quantum computers. Literature: [1] Knizia and Chan, Phys. Rev. Lett. 109, 186404 (2012) [2] Wouters et al., J. Chem. Theory Comput. 12, 2706 (2016) [3] Cao et al., npj Comput. Mater. 9, 78 (2023)