State Preparation in Quantum Algorithms for Fragment-Based Quantum Chemistry

D’Cunha, Ruhee; Otten, Matthew; Hermes, Matthew R.; Gagliardi, Laura; Gray, Stephen K.

doi:10.1021/acs.jctc.3c01283

Title: State Preparation in Quantum Algorithms for Fragment-Based Quantum Chemistry

Journal Article · 11 April 2024 · Journal of Chemical Theory and Computation

DOI: https://doi.org/10.1021/acs.jctc.3c01283 · OSTI ID:2511235

^[1]; Otten, Matthew ^[2];

^[1];

^[3]; Gray, Stephen K. ^[4]

Univ. of Chicago, IL (United States)
HRL Laboratories LLC, Malibu, California 90265, United States
Univ. of Chicago, IL (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States). Center for Nanoscale Materials (CNM)

State preparation for quantum algorithms is crucial for achieving high accuracy in quantum chemistry and competing with classical algorithms. The localized active space–unitary coupled cluster (LAS–UCC) algorithm iteratively loads a fragment-based multireference wave function onto a quantum computer. Here, in this study, we compare two state preparation methods, quantum phase estimation (QPE) and direct initialization (DI), for each fragment. We test the two state preparation methods on three systems, ranging from a model system, a set of interacting hydrogen molecules, to more realistic chemical problems, like the C–C double bond breaking in transbutadiene and the spin ladder in a bimetallic system. We analyze the impact of QPE parameters, such as the number of ancilla qubits and Trotter steps, on the prepared state. We find a trade-off between the methods, where DI requires fewer resources for smaller fragments, while QPE is more efficient for larger fragments. Our resource estimates highlight the benefits of system fragmentation in state preparation for subsequent quantum chemical calculations. These findings have broad applications for preparing multireference quantum chemical wave functions on quantum circuits that can be used for realistic chemical applications.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: Defense Advanced Research Projects Agency (DARPA); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 2511235

Journal Information:: Journal of Chemical Theory and Computation, Journal Name: Journal of Chemical Theory and Computation Journal Issue: 8 Vol. 20; ISSN 1549-9618

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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