1 citations · 1 across the 2 of their papers we have counts for
8 papers
Sample-based quantum diagonalization as parallel fragment solver for the localized active space self-consistent field method
Qiaohong Wang, Mario Motta, Ruhee D'Cunha +7
Accurately and efficiently describing strongly correlated electronic systems is a central challenge in quantum computational chemistry, with classical and quantum computers. The lo…
Adiabatic state preparation from general initial states
Bryce Fuller, Mario Motta, Stuart M. Harwood +4
A variety of quantum computing algorithms exist for the preparation of approximate Hamiltonian ground states. A natural and important question is how these ground-state approximati…
Sample-based Quantum Diagonalization Methods for Modeling the Photochemistry of Diazirine and Diazo Compounds
Saurabh Shivpuje, Tanvi P. Gujarati, Richard Van +6
Diazirines and diazo compounds are widely employed as photoreactive precursors for generating carbenes, key intermediates in chemical biology and materials science. However, comput…
Quantum-centric simulation of hydrogen abstraction by sample-based quantum diagonalization and entanglement forging
Tyler Smith, Tanvi P. Gujarati, Mario Motta +11
The simulation of electronic systems is an anticipated application for quantum-centric computers, i.e. heterogeneous architectures where classical and quantum processing units oper…
Enhanced Prediction of CAR T-Cell Cytotoxicity with Quantum-Kernel Methods
Filippo Utro, Meltem Tolunay, Kahn Rhrissorrakrai +6
Chimeric antigen receptor (CAR) T-cells are T-cells engineered to recognize and kill specific tumor cells. Through their extracellular domains, CAR T-cells bind tumor cell antigens…
A Heuristic Quantum-Classical Algorithm for Modeling Substitutionally Disordered Binary Crystalline Materials
Tanvi P. Gujarati, Tyler Takeshita, Andreas Hintennach +1
Improving the efficiency and accuracy of energy calculations has been of significant and continued interest in the area of materials informatics, a field that applies machine learn…