Benchmarking Adaptive Variational Quantum Eigensolvers
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Quantum Computing Inst.; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
By design, the variational quantum eigensolver (VQE) strives to recover the lowest-energy eigenvalue of a given Hamiltonian by preparing quantum states guided by the variational principle. In practice, the prepared quantum state is indirectly assessed by the value of the associated energy. Novel adaptive derivative-assembled pseudo-trotter (ADAPT) ansatz approaches and recent formal advances now establish a clear connection between the theory of quantum chemistry and the quantum state ansatz used to solve the electronic structure problem. Here we benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves for a few selected diatomic molecules, namely H2, NaH, and KH. Using numerical simulation, we find both methods provide good estimates of the energy and ground state, but only ADAPT-VQE proves to be robust to particularities in optimization methods. Another relevant finding is that gradient-based optimization is overall more economical and delivers superior performance than analogous simulations carried out with gradient-free optimizers. The results also identify small errors in the prepared state fidelity which show an increasing trend with molecular size.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division; USDOE Office of Science (SC), Basic Energy Sciences (BES)
- Grant/Contract Number:
- AC05-00OR22725; 3ERKCG12
- OSTI ID:
- 1731007
- Alternate ID(s):
- OSTI ID: 1771891
- Journal Information:
- Frontiers in Chemistry, Vol. 8, Issue n/a; ISSN 2296-2646
- Publisher:
- Frontiers Research FoundationCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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