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Scalable Quantum-Classical DFT Embedding for NISQ Molecular Simulation

Namrata Manglani, Samrit Kumar Maity, Ranjit Thapa, Sanjay Wandhekar

TL;DR

Problem: capturing electronic correlation on NISQ devices is challenging due to computational cost. Approach: a quantum DFT embedding (QDFT) framework confines correlation to a fixed six-orbital active space embedded in a DFT bath, using range-separated DFT and a VQE solver with adaptive damping, with mu optimized per system. Findings: across H2O, CO2, benzene, pyridine, and Naphthalene, about sixty to sixty-eight percent of CCSD correlation energy is recovered relative to the DFT baseline, converging in two embedding iterations and requiring roughly ten qubits after tapering. Significance: provides a scalable, hardware-ready framework that decouples quantum cost from system size and guides near-term quantum simulations of medium-sized molecules.

Abstract

Scalable quantum-classical embedding is essential for chemically meaningful simulations on near-term NISQ hardware. Using QDFT, we show systematic recovery of correlation energy relative to the DFT baseline, benchmarked against CCSD in a fixed six-orbital active space across molecules ranging from water to naphthalene. By varying the number of embedded electrons from 2 to 8, aromatic systems saturate near 63-64 percent, while linear molecules such as carbon dioxide reach 68 percent. All systems converge within two embedding iterations under relaxed self-consistency thresholds, highlighting the robustness of the approach. A (4e,6o) active space recovers approximately 60 percent correlation using 10 qubits, providing practical guidelines for NISQ-era simulations.

Scalable Quantum-Classical DFT Embedding for NISQ Molecular Simulation

TL;DR

Problem: capturing electronic correlation on NISQ devices is challenging due to computational cost. Approach: a quantum DFT embedding (QDFT) framework confines correlation to a fixed six-orbital active space embedded in a DFT bath, using range-separated DFT and a VQE solver with adaptive damping, with mu optimized per system. Findings: across H2O, CO2, benzene, pyridine, and Naphthalene, about sixty to sixty-eight percent of CCSD correlation energy is recovered relative to the DFT baseline, converging in two embedding iterations and requiring roughly ten qubits after tapering. Significance: provides a scalable, hardware-ready framework that decouples quantum cost from system size and guides near-term quantum simulations of medium-sized molecules.

Abstract

Scalable quantum-classical embedding is essential for chemically meaningful simulations on near-term NISQ hardware. Using QDFT, we show systematic recovery of correlation energy relative to the DFT baseline, benchmarked against CCSD in a fixed six-orbital active space across molecules ranging from water to naphthalene. By varying the number of embedded electrons from 2 to 8, aromatic systems saturate near 63-64 percent, while linear molecules such as carbon dioxide reach 68 percent. All systems converge within two embedding iterations under relaxed self-consistency thresholds, highlighting the robustness of the approach. A (4e,6o) active space recovers approximately 60 percent correlation using 10 qubits, providing practical guidelines for NISQ-era simulations.
Paper Structure (12 sections, 1 equation, 2 figures, 2 tables)

This paper contains 12 sections, 1 equation, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Computational Methods
  3. Quantum DFT Embedding Framework
  4. Range-Separation Parameter Optimization
  5. Active Space and Quantum Solver Details
  6. Molecular Systems and Reference Data
  7. Results and Discussion
  8. Baseline Energies
  9. Correlation Recovery and Active-Space Scaling
  10. Active-Space Convergence Trends
  11. Discussion
  12. Conclusions

Figures (2)

  • Figure 1: Representation of molecular structure from MolView
  • Figure 2: Correlation recovery versus active-space size. The dashed line indicates 60% recovery. Linear CO$_2$ continues to scale, while aromatic systems saturate at modest active spaces.