Quantum-centric simulation of hydrogen abstraction by sample-based quantum diagonalization and entanglement forging

• URL: http://arxiv.org/abs/2508.08229v2
• Date: Tue, 12 Aug 2025 19:37:42 GMT
• Title: Quantum-centric simulation of hydrogen abstraction by sample-based quantum diagonalization and entanglement forging
• Authors: Tyler Smith, Tanvi P. Gujarati, Mario Motta, Ben Link, Ieva Liepuoniute, Triet Friedhoff, Hiromichi Nishimura, Nam Nguyen, Kristen S. Williams, Javier Robledo Moreno, Caleb Johnson, Kevin J. Sung, Abdullah Ash Saki, Marna Kagele,
• Abstract summary: An important application is the computation of radical chain reactions.<n>We compute the activation energy and reaction energy for hydrogen abstraction from 2,2-diphenyldipropane.<n> Calculations are performed using a superconducting quantum processor of the IBM Heron family and classical computing resources.
• Score: 1.8314070876706192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The simulation of electronic systems is an anticipated application for quantum-centric computers, i.e. heterogeneous architectures where classical and quantum processing units operate in concert. An important application is the computation of radical chain reactions, including those responsible for the photodegradation of composite materials used in aerospace engineering. Here, we compute the activation energy and reaction energy for hydrogen abstraction from 2,2-diphenyldipropane, used as a minimal model for a step in a radical chain reaction. Calculations are performed using a superconducting quantum processor of the IBM Heron family and classical computing resources. To this end, we combine a qubit-reduction technique called entanglement forging (EF) with sample based quantum diagonalization (SQD), a method that projects the Schr\"{o}dinger equation into a subspace of configurations sampled from a quantum device. In conventional quantum simulations, a qubit represents a spin-orbital. In contrast, EF maps a qubit to a spatial orbital, reducing the required number of qubits by half. We provide a complete derivation and a detailed description of the combined EF and SQD approach, and we assess its accuracy across active spaces of varying sizes.

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