85 citations · 87 across the 5 of their papers we have counts for
5 papers
The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry
Eliu Huerta, Xiaoyun Wang, Geetika Gupta +7
This Comment emerges from TPC26 (https://tpc26.org), a conference convening leaders from academia, national laboratories, and industry who are reshaping materials science discovery…
Multireference Embedding and Fragmentation Methods for Classical and Quantum Computers: from Model Systems to Realistic Applications
Shreya Verma, Abhishek Mitra, Qiaohong Wang +8
One of the primary challenges in quantum chemistry is the accurate modeling of strong electron correlation. While multireference methods effectively capture such correlation, their…
Non-unitary Variational Quantum Eigensolver with the Localized Active Space Method and Cost Mitigation
Qiaohong Wang, Ruhee D'Cunha, Abhishek Mitra +4
Accurately describing strongly correlated systems with affordable quantum resources remains a central challenge for quantum chemistry applications on near and intermediate-term qua…
The Localized Active Space Method with Unitary Selective Coupled Cluster
Abhishek Mitra, Ruhee D'Cunha, Qiaohong Wang +5
We introduce a hybrid quantum-classical algorithm, the localized active space unitary selective coupled cluster singles and doubles (LAS-USCCSD) method. Derived from the localized…
Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
Yuri Alexeev, Maximilian Amsler, Paul Baity +124
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of e…