ADAPT-QSCI: Adaptive Construction of an Input State for Quantum-Selected Configuration Interaction

Abstract

We present a quantum-classical hybrid algorithm for calculating the ground state and its energy of the quantum many-body Hamiltonian by proposing an adaptive construction of a quantum state for the quantum-selected configuration interaction (QSCI) method. QSCI allows us to select important electronic configurations in the system to perform CI calculation (subspace diagonalization of the Hamiltonian) by sampling measurement for a proper input quantum state on a quantum computer, but how we prepare a desirable input state has remained a challenge. We propose an adaptive construction of the input state for QSCI in which we run QSCI repeatedly to grow the input state iteratively. We numerically illustrate that our method, dubbed ADAPT-QSCI, can yield accurate ground-state energies for small molecules, including a noisy situation for eight qubits where error rates of two-qubit gates and the measurement are both as large as 1%. ADAPT-QSCI serves as a promising method to take advantage of current noisy quantum devices and pushes forward its application to quantum chemistry.

14 pages

References in corpus (5)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Heat-bath Configuration Interaction: An efficient selected CI algorithm inspired by heat-bath sampling
• Semistochastic Heat-bath Configuration Interaction method: selected configuration interaction with semistochastic perturbation theory
• Adaptive multiconfigurational wave functions
• Fermionic Adaptive Sampling Theory for Variational Quantum Eigensolvers

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• Critical Limitations in Quantum-Selected Configuration Interaction Methods
• Subspace-Based Local Compilation of Variational Quantum Circuits for Large-Scale Quantum Many-Body Simulation
• Quantum computation of a quasiparticle band structure with the quantum-selected configuration interaction
• Rovibrational energy levels of H${}_2$O by quantum computing
• Enhancing quantum computations with the synergy of auxiliary field quantum Monte Carlo and computational basis tomography
• Qubit frugal entanglement determination with the deep multi-scale entanglement renormalization ansatz
• Exploring fixed points and eigenstates of quantum systems with reinforcement learning
• Resource-efficient Quantum Algorithms for Selected Hamiltonian Subspace Diagonalization
• Towards Compact Wavefunctions from Quantum-Selected Configuration Interaction