In a new study supported by Q-NEXT, scientists from the Gagliardi Group at the University of Chicago, in collaboration with Argonne National Laboratory, and the University of Wisconsin–Madison, have introduced a hybrid quantum-classical algorithm called the LAS-USCCSD method. This innovative algorithm is designed to efficiently solve complex quantum chemistry problems.
The algorithm begins with classical computations, breaking a molecule into smaller components to calculate its basic structure and energy levels. It then identifies the key parameters—those that control interaction energy between different parts of the molecule. By applying the variational quantum eigensolver, the algorithm focuses on these important parameters, significantly improving the efficiency of the quantum computation by reducing the data the quantum computer needs to process.
Compared to its predecessor, the LAS-UCCSD method, the new LAS-USCCSD algorithm is more efficient. It requires fewer parameters to achieve accurate results, which simplifies and accelerates calculations. This reduction in complexity makes the method more practical for near-term quantum computers.
The research team successfully tested the algorithm on molecules such as (H₂)₂, (H₂)₄, trans-butadiene, and a complex bimetallic molecule. The LAS-USCCSD method reduced the number of required parameters by up to 10 times, making quantum computations more feasible on today’s quantum computing platforms.
Special recognition goes to Gagliardi Group members Abhishek Mitra, Ruhee D’Cunha, Qiaohong (Joanna) Wang, and Matthew Hermes for their contributions to this groundbreaking work!
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