EAGER-QAC-QSA: Variational quantum algorithms for transcorrelated electronic-structure Hamiltonians

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Miyake of the Center for Quantum Information and Control at the University of New Mexico is supported by an award from the Chemical Theory, Models, and Computational Methods program in the Division of Chemistry, to study new designs of simulation and algorithms for quantum computing. Quantum computers are promising to advance computational power and provide unmatched advantages in code breaking and scientific simulations in fields such as quantum chemistry and materials science. Quantum computing has progressed rapidly in recent years, and the hardware development is nearing a level of sophistication which could allow a proof-of-principle demonstration of quantum advantages. Miyake’s research team develops methods of variational optimizations to solve the energetic configurations of electrons, as they are of fundamental importance in physics, chemistry, and material science. The project contributes to the knowledge base of quantum information science and its cross-fertilization with different research fields such as quantum chemistry and material science. It also advances the objectives of two of 10 Big Ideas for Future NSF Investments: “The Quantum Leap: Leading the Next Quantum Revolution” and “Growing Convergent Research at NSF”. In order to inspire and train undergraduate and graduate students from the departments of chemistry and material science, a tutorial session on quantum computation and chemistry is arranged in conjunction with the Southwest Quantum Information and Technology workshop.The electronic-structure Hamiltonians, made of the kinetic energies and Coulomb interactions of electrons, are fundamental in physics, quantum chemistry, and material science. It is highly desirable to understand how a quantum computer could provide practical speed-up for the “strongly-correlated” problems major classical algorithms currently struggle. In the near term, hybrid quantum-classical approaches, such as variational optimizations, are particularly promising, as they utilize a quantum computer only for essential parts of computation and utilize fully classical side-computers to boost the overall computational performance. A major challenge in the variational methods is how to set variational parameters in an effective way and satisfy physical constraints at the same time. The so-called Jastrow form to characterize dynamical correlation is empirically successful to fulfill the Coulomb cusp condition originated from electrons’ coalescence. However, it is not straightforward to encode as quantum ansatz states as it breaks unitarity. To this end, by modifying the electronic-structure Hamiltonians under a similarity transformation of the Jastrow term, Miyake’s research team develops and analyzes variational quantum algorithms based on this so-called transcorrelated Hamiltonians.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.