Error-Controlled Quantum Chemical Exploration of Reaction Networks

Yuzuru Kurosaki

Nathan Wiebe, Markus Reiher, Krysta M. Svore +2 more

Jincheng Zhang

This work explores the application of tensor network methods, specifically Matrix Product States (MPS) and Tensor Train (TT), to simulate complex chemical reactions on quantum simulators. The core claim is that tensor network approaches offer an accurate method to capture entanglement effects and predict reaction dynamics, overcoming limitations of traditional classical approaches. The mechanism involves mapping a chemical system into a suitable tensor network representation, followed by leveraging quantum hardware to efficiently perform tensor contractions and simulate quantum evolution. We demonstrate a scalable approach to chemical reaction modeling by combining tensor networks with the capabilities of quantum simulation. This research focuses on the theoretical framework and potential advantages of this combined methodology, aiming to provide a foundation for future experimental investigations. The presented methods offer a path toward simulating reactions with increased fidelity and complexity, relevant for diverse fields such as materials science, drug discovery, and fundamental chemical research. The key contribution lies in the efficient representation and manipulation of complex quantum states within the constraints of available quantum hardware.

Alain C. Vaucher

Jincheng Zhang

Quantum information has emerged as a transformative tool across diverse fields, and its potential within chemical reaction rate prediction is increasingly attracting attention. This paper explores the integration of quantum information and geometric modeling to enhance the prediction of reaction rates, utilizing geometric information for optimization. We present a novel framework that leverages quantum algorithms to represent chemical reaction dynamics as geometric functions, enabling the optimization of reaction conditions based on these geometric representations. The core mechanism focuses on translating complex reaction kinetics into a simplified geometric model, allowing for efficient optimization through geometric analysis. The study demonstrates the effectiveness of this approach in predicting reaction rates, offering a promising pathway towards improved reaction control and understanding.

Kang-Kuen Ni

Advances in quantum manipulation of molecules bring unique opportunities, including the use of molecules to search for new physics, harnessing molecular resources for quantum engineering, and exploring chemical reactions in the ultra-low temperature regime. In this talk, I will focus on the latter two topics. First, I will introduce our effort on building single ultracold molecules with full internal and motional state control in optical tweezers for future quantum simulators and computers. This work allows us to go beyond the usual paradigm of chemical reactions that proceed via stochastic encounters between reactants, to a single, controlled reaction of exactly two atoms. Second, I will present our work giving a detailed microscopic picture of molecules transforming from one species to another. We develop full quantum state mapping of chemical reaction product-pairs from single events, which we use to precisely benchmark statistical theory.