Ab initio nuclear structure via quantum adiabatic algorithm

Abstract

Background: Solving nuclear many-body problems with an ab initio approach is widely recognized as a computationally challenging problem. Quantum computers offer a promising path to address this challenge.There are urgent needs to develop quantum algorithms for this purpose.Objective: In this work, we explore the application of the quantum algorithm of adiabatic state preparation with quantum phase estimation in ab initio nuclear structure theory. We focus on solving the low-lying spectra (including both the ground and excited states) of simple nuclear systems.Ideas: The efficiency of this algorithm is hindered by the emergence of small energy gaps (level crossings) during the adiabatic evolution. In order to improve the efficiency, we introduce techniques to avoid level crossings: 1) by suitable design of the reference Hamiltonian; 2) by insertions of perturbation terms to modify the adiabatic path.Results: We illustrate this algorithm by solving the deuteron ground state energy and the spectrum of the deuteron bounded in a harmonic oscillator trap implementing the IBM Qiskit quantum simulator. The quantum results agree well the classical results obtained by matrix diagonalization.Outlook: With our improvements to the efficiency, this algorithm provides a promising tool for investigating the low-lying spectra of complex nuclei on future quantum computers.

Standard model physics and the digital quantum revolution: thoughts about the interface

“…[189,409]). This method has been applied to lattice field theory simulations [159,160] and applications to the nuclear many-body problem have recently started to be developed [409].…”

“…The scale for the evolution time T is set by the smallest energy gap ΔE between the instantaneous ground-state and first excited state throughout the adiabatic path. Several strategies have been proposed to optimize the efficiency of this scheme, ranging from explicit coupling to an external 'bath' of degrees of freedom [437][438][439], gap amplification techniques [440], and specially designed adiabatic paths that leverage previous knowledge of the excitation spectrum to avoid closing gaps during the adiabatic evolution (e.g., references [221,441]). This method has been applied to lattice field theory simulations [191,192] and applications to the nuclear many-body problem have recently started to be developed [441].…”

Bootstrapping the deuteron

“…Inspired by the huge success of artificial intelligence (AI), new variational methods based on artificial neural networks (ANNs) are put forward for few-nucleon systems, incorporating the latest advances in AI into conventional variational methods [3][4][5]. Also, lots of interests are stimulated in developing new theoretical methods on quantum devices, encouraged by the public accessibility of quantum computing clouds via the internet [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. These achievements enrich our tools to study nuclear systems.…”