Molecular Quantum Computations on a Protein

TL;DR

This work demonstrates a scalable quantum-centric workflow for biomolecular electronic structure by integrating a fragment-based Embedded Wave Function (EWF) embedding scheme with both quantum (SQD/FCI) and classical (MP2/CCSD) solvers. By expanding the DMET framework with MP2-derived bath expansions and leveraging a LUCJ-based quantum ansatz, the approach enables CI-level treatment of hundreds of atoms in Trp-cage, validated against classical benchmarks. The results show that quantum-enhanced fragment CI can approximate high-level correlation while remaining computationally tractable, marking a key step toward quantum-classical workflows for proteins. The study highlights modularity and scalability, suggesting future pathways to fault-tolerant quantum methods (e.g., QPE) within embedding schemes for even larger biomolecular systems.

Abstract

This work presents the implementation of a fragment-based, quantum-centric supercomputing workflow for computing molecular electronic structure using quantum hardware. The workflow is applied to predict the relative energies of two conformers of the 300-atom Trp-cage miniprotein. The methodology employs wave function-based embedding (EWF) as the underlying fragmentation framework, in which all atoms in the system are explicitly included in the CI treatment. CI calculations for individual fragments are performed using either sample-based quantum diagonalization (SQD) for challenging fragments or full configuration interaction (FCI) for trivial fragments. To assess the accuracy of SQD for fragment CI calculations, EWF-(FCI,SQD) results are compared against EWF-MP2 and EWF-CCSD benchmarks. Overall, the results demonstrate that large-scale electronic configuration interaction (CI) simulations of protein systems containing hundreds or even thousands of atoms can be realized through the combined use of quantum and classical computing resources.

Figures (10)

• Figure 1: Overview of the EWF-based quantum-centric workflow for Trp-Cage conformer energetics. The protocol includes Hartree–Fock reference generation, automated fragmentation and bath construction, bath orbital expansion, and high-level cluster calculations using either quantum (SQD/FCI) or classical (MP2/CCSD) solvers. Fragment energies are reconstructed to obtain total conformer energies.
• Figure 2: Schematic overview of the classical pre-processing and EWF workflow. The diagram illustrates (A) conformer definition and mean-field problem setup, (B) initial EWF fragment and DMET bath construction from the Hartree--Fock reference, and (C) MP2-level interacting bath expansion via bath natural orbitals.
• Figure 3: Quantum--classical workflow for post--Hartree--Fock EWF cluster calculations. The diagram summarizes solver selection logic, quantum circuit execution, and classical post-processing steps used in EWF-(SQD,FCI) calculations.
• Figure 4: Number of EWF clusters with a given size (defined as the number of MOs) for the folded (blue) and unfolded (green) conformers. The black dashed line separates EWF clusters studied with FCI (left) and SQD (right).
• Figure 5: Exploded schematic of the LUCJ sampling workflow used in SQD calculations. LUCJ circuits are initialized from Hartree--Fock and CCSD amplitudes via compressed double factorization, instantiated and transpiled for a target QPU backend, executed with error-suppression protocols, and iteratively optimized through classical post-processing of measurement outcomes.