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Hückel molecular orbital theory on a quantum computer: A scalable system-agnostic variational implementation with compact encoding.
Singh, Harshdeep; Majumder, Sonjoy; Mishra, Sabyashachi.
Afiliação
  • Singh H; Center of Computational and Data Sciences, Indian Institute of Technology, Kharagpur, India.
  • Majumder S; Department of Physics, Indian Institute of Technology, Kharagpur, India.
  • Mishra S; Department of Chemistry, Indian Institute of Technology, Kharagpur, India.
J Chem Phys ; 160(19)2024 May 21.
Article em En | MEDLINE | ID: mdl-38767256
ABSTRACT
Hückel molecular orbital (HMO) theory provides a semi-empirical treatment of the electronic structure in conjugated π-electronic systems. A scalable system-agnostic execution of HMO theory on a quantum computer is reported here based on a variational quantum deflation (VQD) algorithm for excited state quantum simulation. A compact encoding scheme is proposed here that provides an exponential advantage over the direct mapping and allows for quantum simulation of the HMO model for systems with up to 2n conjugated centers with n qubits. The transformation of the Hückel Hamiltonian to qubit space is achieved by two different strategies an iterative refinement transformation and the Frobenius-inner-product-based transformation. These methods are tested on a series of linear, cyclic, and hetero-nuclear conjugated π-electronic systems. The molecular orbital energy levels and wavefunctions from the quantum simulation are in excellent agreement with the exact classical results. However, the higher excited states of large systems are found to suffer from error accumulation in the VQD simulation. This is mitigated by formulating a variant of VQD that exploits the symmetry of the Hamiltonian. This strategy has been successfully demonstrated for the quantum simulation of C60 fullerene containing 680 Pauli strings encoded on six qubits. The methods developed in this work are easily adaptable to similar problems of different complexity in other fields of research.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article