Quantum-selected configuration interaction with time-evolved state

Abstract

Quantum-selected configuration interaction (QSCI) utilizes an input quantum state on a quantum device to select important bases (electron configurations in quantum chemistry) that define a subspace in which to diagonalize a target Hamiltonian, i.e., perform selected configuration interaction, on classical computers. Previous proposals for preparing a good input state, which is crucial for the quality of QSCI, based on optimization of quantum circuits may suffer from optimization difficulty and require many runs of the quantum device. Here, we propose using a time-evolved state by the target Hamiltonian (for some initial state) as an input of QSCI. Our proposal is based on the intuition that the time evolution by the Hamiltonian creates electron excitations of various orders when applied to the initial state. We numerically investigate the accuracy of the energy obtained by the proposed method for quantum chemistry Hamiltonians describing electronic states of small molecules. Numerical results reveal that our method can yield sufficiently accurate ground-state energies for the investigated molecules. Systematic analysis when increasing the number of qubits in a hydrogen chain shows that the subspace size required for sufficiently accurate results is reasonable at system sizes that cannot be solved by naive classical diagonalization. Our proposal provides a systematic and optimization-free method to prepare the input state of QSCI and could contribute to practical applications of quantum computers in quantum chemistry calculations.

19 pages, 8 figures, 4 tables

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Cited by in corpus (7)

• Extending Quantum Computing through Subspace, Embedding and Classical Molecular Dynamics Techniques
• Rovibrational energy levels of H${}_2$O by quantum computing
• Velocity Verlet-based optimization for variational quantum eigensolvers
• Towards Compact Wavefunctions from Quantum-Selected Configuration Interaction
• Enhancing quantum computations with the synergy of auxiliary field quantum Monte Carlo and computational basis tomography
• Evaluating Sample-Based Krylov Quantum Diagonalization for Heisenberg Models with Applications to Materials Science
• Resource-efficient Quantum Algorithms for Selected Hamiltonian Subspace Diagonalization