Sample-based Quantum Diagonalization Methods for Modeling the Photochemistry of Diazirine and Diazo Compounds

TL;DR

The study addresses the challenge of accurately modeling photochemical reaction pathways of diazirine and diazo compounds, which require large active spaces and reliable treatment of excited states and conical intersections. It introduces a hybrid quantum–classical workflow leveraging Sample-based Quantum Diagonalization (SQD) and Ext-SQD, coupled with classical geometry optimization and active-space selection, to estimate ground- and excited-state energies. The approach achieves chemical-accurate agreement with CASCI/SCI benchmarks for both aliphatic ($12$, $10$) and aryl ($30$, $30$) active spaces, demonstrating scalability to large quantum-resource demands and providing insights into carbene formation pathways. This work validates the potential of quantum-centric methods to model electronically complex photochemical transformations, with implications for medicinal chemistry and photoaffinity labeling.

Abstract

Diazirines and diazo compounds are widely employed as photoreactive precursors for generating carbenes, key intermediates in chemical biology and materials science. However, computationally modeling their reaction pathways remains challenging due to a need for large active spaces and the requirement to accurately capture excited-state surfaces along with transition states and conical intersections. In this work, we utilize a hybrid quantum-classical workflow for investigating carbene formation in representative diazirine-diazomethane systems. Our approach leverages Sample-based Quantum Diagonalization (SQD) and its extended variant (Ext-SQD) for ground and excited-state analysis, combined with classical tools for geometry optimization, active-space selection, and diagnostic evaluation. Quantum computations were carried out on superconducting quantum processors, and results for both aliphatic and aryl-substituted diazirine-diazomethane pairs were benchmarked against established classical methods, including DFT, CCSD, CASCI, and SCI. SQD achieves accuracy surpassing the chemical accuracy threshold for nearly all stationary points on the potential energy surface of parent diazirine relative to the CASCI(12,10) reference, and remains close to chemical accuracy for phenyl-substituted diazirine in a (30,30) active space, with an average deviation of 1.1 kcal/mol relative to the SCI benchmark. SQD closely follows CASCI and SCI trends, showing consistent agreement. The findings demonstrate the promise of quantum computing frameworks in modeling photochemical transformations of electronically complex and pharmacologically relevant molecules.

Figures (3)

• Figure 1: (a) Schematic representation of carbene formation upon photolysis of DZ, DM, PhDZ, and PhDM. The diagram provides a simplified overview of the photochemical pathways, highlighting carbene generation from diazirine–diazo pairs and the isomerization of diazirines to diazo compounds. (b) Overview of the hybrid quantum–classical workflow employed to compute energies using SQD and extended SQD methodologies.
• Figure 2: Relative energies (in kcal/mol) of stationary points along the reaction coordinate for the DZ – DM interconversion. PES were computed with DFT, CCSD, CASCI(12,10) and SQD(12,10) on ibm_kingston. Points span both S$_0$: ground and S$_1$: excited states. Geometries at representative points along the reaction path are illustrated in the plot for reference. In the molecular model, atoms are color-coded as follows: carbon in gray, hydrogen in white, and nitrogen in blue. The ibm_kingston markers are shown slightly shifted vertically for clarity; in fact, they overlap very closely with CASCI values. $^{*}$Note that the value of the conical intersection, CI1, was computed using the CCSD2 implementation in ORCA.
• Figure 3: Computed ground-state energy profile for phenyldiazirine (PhDZ) - phenyldiazomethane (PhDM) system along the reaction coordinate, comparing results from quantum simulations and classical methods. Geometries at representative points along the reaction path are illustrated in the plot for reference. In the molecular model, atoms are color-coded as follows: carbon in gray, hydrogen in white, and nitrogen in blue.