Scaling active spaces in simulations of surface reactions through sample-based quantum diagonalization

Abstract

Quantum-chemical simulations are essential for predicting energies of chemical reactions. Accurately solving the many-body Schrödinger equation for reagent and product states of most relevant chemical process is, however, unfeasible. Quantum computing offers a pathway for predicting energies of correlated electronic systems with localized interactions. Here, we apply a quantum embedding approach for investigating oxygen reduction reactions at the electrode surface in Lithium batteries, a representative example of energetic analysis in localized chemical reactions. We employ an Active Space Selection method based on Density Difference Analysis for identifying the orbitals involved in the reaction. Leveraging the Local Unitary Cluster Jastrow ansatz for state preparation, the active-space orbitals are then processed on a quantum computer. As quantum algorithms, we use Sample-based Quantum Diagonalization, SQD, and its extended version, Ext-SQD, which integrates electronic excitations into the quantum-selected electronic configuration subspace. The largest configurations are represented by quantum circuits mapped onto 80 qubits of an IBM Heron R2 quantum processing unit. For up to 12 orbitals, we are able to benchmark the quantum-computed reaction energies against results obtained with Complete Active Space Configuration Interaction. For benchmarking results in active spaces as large as 32 orbitals, we resort to Heat-Bath Configuration Interaction and Coupled Cluster Singles and Doubles calculations, respectively. At 27 orbitals, the Ext-SQD results exhibit prediction accuracy improvements with regard to the standard, quantum-chemical reference methods that remain computationally feasible at that scale. The results indicate the potential of sample-based quantum diagonalization for performing high-accuracy reaction modeling in chemistry and materials science.

Figures (7)

• Figure 1:
• Figure 2: Active Space selection Geometry optimization step (a). Density Difference Analysis step (b). Evaluation of each orbital's contribution to the density difference. Construction of active space using the Natural Orbitals (c), where orbitals for the product are shown. Orbitals are sorted based on their contribution to the density difference. The active space grows from the HONO-LUNO level "inside out".
• Figure 3: Ground state and surface reaction energies obtained with standard, quantum-chemical simulation methods. Ground state energies as a function of active space size. The curves on the left-hand side represent the reactant, the curves on the right-hand side the product. Energy reaction can be obtained by calculating the difference.
• Figure 4: Circuit-to-chip mapping. Green qubits represent $\alpha$ spin orbitals, red qubits represent $\beta$ spin orbitals, and blue qubits act as ancillae.
• Figure 5: Histograms of the top-10 electron configurations sampled by the quantum processing unit for the (14e,20o) reactant active space. Filled (unfilled) circles represent occupied (unoccupied) orbitals. (a) Histogram for the non-truncated, one-layer LUCJ ansatz. (b) Histogram for the truncated, two-layer LUCJ ansatz. The truncated variant exhibits a distribution of electronic configurations that is not dominated by the reference Hartree-Fock state.