activity
20202025
most citedReinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving

83 citations · 130 across the 5 of their papers we have counts for

collaborators

5 papers

cond-mat.str-el2025

New perspectives on Density-Matrix Embedding Theory

Alicia Negre, Fabian Faulstich, Raehyun Kim +3

Quantum embedding methods enable the study of large, strongly correlated quantum systems by (usually self-consistent) decomposition into computationally manageable subproblems, in…

cond-mat.supr-con2023★ 20 cited

Ab initio quantum many-body description of superconducting trends in the cuprates

Zhi-Hao Cui, Junjie Yang, Johannes Tölle +9

Using a systematic ab initio quantum many-body approach that goes beyond low-energy models, we directly compute the superconducting pairing order and estimate the pairing gap of se…

cond-mat.str-el2022★ 23 cited

Interacting models for twisted bilayer graphene: a quantum chemistry approach

Fabian M. Faulstich, Kevin D. Stubbs, Qinyi Zhu +7

The nature of correlated states in twisted bilayer graphene (TBG) at the magic angle has received intense attention in recent years. We present a numerical study of an interacting…

physics.comp-ph2021★ 4 cited

On the pure state -representability of density matrix embedding theory

Fabian M. Faulstich, Raehyun Kim, Zhi-Hao Cui +3

Density matrix embedding theory (DMET) formally requires the matching of density matrix blocks obtained from high-level and low-level theories, but this is sometimes not achievable…

quant-ph2020★ 83 cited

Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving

Jiahao Yao, Lin Lin, Marin Bukov

The quantum alternating operator ansatz (QAOA) is a prominent example of variational quantum algorithms. We propose a generalized QAOA called CD-QAOA, which is inspired by the coun…