Exploring Near-Term Quantum Algorithms for Chemical Reaction Studies and AI-driven Algorithm Advancements

Abstract

Quantum Computing presents a paradigm shift in computational chemistry, offering unprecedented potential for tackling complex chemical problems. In this presentation, I will delve into recent developments made in leveraging quantum computing for chemistry, particularly focusing on the recent applications of quantum algorithms for electronic structure and how machine learning methods can expedite quantum algorithm development. In the first half of my talk, I will highlight a notable study from IBM Quantum, wherein a Diels-Alder cycloaddition reaction serves as a testbed for near-term quantum algorithms and hardware. My discussion will center on the assessment of the near-term algorithms in computing activation barriers, exemplified by the interaction between cyclopentadiene and ethylene to form a transition state. I will describe a protocol that combines the use of two quantum algorithms: one called Entanglement Forging, which allows us to reduce the number of qubits required for quantum chemistry simulations, and another called Quantum Subspace Expansion, which improves the accuracy of the quantum simulation. These quantum techniques combined with classical processing using second-order perturbation theory allow us to account for both static and dynamic electron correlation in studying chemical reactions. In the second half of my talk, I will discuss the synergistic potential of combining artificial intelligence (AI) and quantum computing, exploring how the intersection of generative AI and machine learning expedites ground-state searches within quantum chemistry, illustrated with recent examples.