Non-iterative disentangled unitary coupled-cluster based on lie-algebraic structure
Year: 2025
Type: article
Abstract: Due to their non-iterative nature, fixed unitary coupled cluster (UCC) ansätze are attractive for performing quantum chemistry variational quantum eigensolver (VQE) computations as they avoid pre-circuit measurements on a quantum computer. However, achieving chemical accuracy for strongly correlated systems with UCC requires further inclusion of higher-order fermionic excitations beyond triples increasing circuit depth. We introduce k -non-iterative disentangled unitary coupled cluster (NI-DUCC), a fixed and non-iterative disentangled UCC compact ansatz, based on specific ‘ k ’ sets of ‘qubit’ excitations, eliminating the needs for fermionic-type excitations. These elements scale linearly ( O ( n ) ) by leveraging Lie algebraic structures, with n being the number of qubits. The key excitations are screened through specific selection criteria, including the enforcement of all symmetries, to ensure the construction of a robust set of generators. NI-DUCC employs ‘ k ’ products of the exponential of O ( n ) - anti-Hermitian Pauli operators, where each single Pauli string has a length p . This results in a fewer two-qubit CNOT gates circuit scaling, O ( k n p ) , suitable for hardware implementations. Tested on LiH, H 6 and BeH 2 , NI-DUCC-VQE achieves both chemical accuracy and rapid convergence even for molecules deviating significantly from equilibrium. It is hardware-efficient, reaching the exact full configuration interaction energy solution at specific layers, while reducing significantly the VQE optimization steps. NI-DUCC-VQE effectively addresses the gradient measurement bottleneck of ADAPT-VQE-like iterative algorithms, yet the classical computational cost of constructing the O ( n ) set of excitations increases exponentially with the number of qubits. We provide a first implementation for constructing the generators’ set, able to handle up to 20 qubits and discuss the efficiency perspectives.
Source: Quantum Science and Technology
Authors: Mohammad Haidar, Olivier Adjoua, Siwar Badreddine, Alberto Peruzzo, Jean-Philip Piquemal
Institutions: Qubit Pharmaceuticals, Laboratoire de Chimie Théorique
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,895
OpenAlex ID: W4402028417