Quantum-centric computation of molecular excited states with extended sample-based quantum diagonalization

Abstract

The simulation of molecular electronic structure is an important application of quantum devices. Recently, it has been shown that quantum devices can be effectively combined with classical supercomputing centers in the context of the sample-based quantum diagonalization (SQD) algorithm. This allowed the largest electronic structure quantum simulation to date (77 qubits) and opened near-term devices to practical use cases in chemistry toward the hundred-qubit mark. However, the description of many important physical and chemical properties of those systems, such as photo-absorption/-emission, requires a treatment that goes beyond the ground state alone. In this work, we extend the SQD algorithm to determine low-lying molecular excited states. The extended-SQD method improves over the original SQD method in accuracy, at the cost of an additional computational step. It also improves over quantum subspace expansion based on single and double electronic excitations, a widespread approach to excited states on pre-fault-tolerant quantum devices, in both accuracy and efficiency. We employ the extended SQD method to compute the first singlet (S${}_1$) and triplet (T${}_1$) excited states of the nitrogen molecule with a correlation-consistent basis set, and the ground- and excited-state properties of the [2Fe-2S] cluster.

15 pages, 7 figures, comments are welcome!

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• Extending Quantum Computing through Subspace, Embedding and Classical Molecular Dynamics Techniques
• Quantum computation of a quasiparticle band structure with the quantum-selected configuration interaction
• Efficient Quantum Simulation of Non-Adiabatic Molecular Dynamics with Precise Electronic Structure
• Resource-efficient Quantum Algorithms for Selected Hamiltonian Subspace Diagonalization
• Towards Compact Wavefunctions from Quantum-Selected Configuration Interaction
• Enhancing quantum computations with the synergy of auxiliary field quantum Monte Carlo and computational basis tomography
• Exploring fixed points and eigenstates of quantum systems with reinforcement learning
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