A Gell-Mann & Low Theorem Perspective on Quantum Computing: New Paradigm for Designing Quantum Algorithm

TL;DR

This paper introduces a novel quantum algorithm framework based on the Gell-Mann & Low theorem, enabling systematic reconstruction of the Dyson series for the Fermi-Hubbard model, offering a new perspective for variational quantum computing.

Contribution

It presents the Variational Interaction-Picture S-matrix Ansatz (VIPSA), integrating the Gell-Mann & Low theorem into quantum algorithms to reconstruct the Dyson series without Trotter expansion.

Findings

Successfully reconstructs Dyson series on a quantum computer

Demonstrates stable convergence in simulations

Offers a new conceptual approach for quantum algorithm design

Abstract

The Gell-Mann & Low theorem is a cornerstone of Quantum Field Theory (QFT) and condensed matter physics, and many-body perturbation theory is a foundational tool for treating interactions. However, their integration into quantum algorithms remains a largely unexplored area of research, with current quantum simulation algorithms predominantly operating in the Schr\"odinger picture, leaving the potential of the interaction picture largely untapped. Our Variational Interaction-Picture S-matrix Ansatz (VIPSA) now fills this gap, specifically in the context of the Fermi-Hubbard model -- a canonical paradigm in condensed matter physics which is intricately connected to phenomena such as high-temperature superconductivity and Mott insulator transitions. This work offers a new conceptual perspective for variational quantum computing based upon the Gell-Mann & Low theorem. We achieve this by employing an innovative mathematical technique to explicitly unfold the normalized S-matrix, thereby enabling the systematic reconstruction of the Dyson series on a quantum computer, order by order. This method stands in contrast to the conventional reliance on Trotter expansion for adiabatic time evolution, marking a conceptual shift towards more sophisticated quantum algorithmic design. We leverage the strengths of the recently developed ADAPT-VQE algorithm, tailoring it to reconstruct perturbative terms effectively. Our simulations indicate that this method not only successfully recovers the Dyson series but also exhibits robust and stable convergence. We believe that our approach shows great promise in generalizing to more complex scenarios without increasing algorithmic complexity.