Richard Ho
Research Mentor(s): Alauddin Ahmed
Mentor Department: University of Michigan
Authors:
Session: Session 4 (1:00pm – 1:50pm)
Presentation Type: Poster 41
Abstract
Accurately modeling large molecular systems in computational chemistry is challenging due to the exponential growth of electron interactions, making classical simulations increasingly difficult. Quantum computing offers a promising alternative by leveraging quantum-mechanical principles to simulate molecular properties more efficiently. A recent milestone in quantum error correction, demonstrated by Google’s Willow quantum processor, significantly reduced errors, marking a step toward practical quantum advantage. This advancement suggests the potential for quantum systems to outperform classical supercomputers in specific computational tasks [1]. While current quantum hardware still faces limitations, such as error rates and scalability challenges, quantum algorithms running on quantum simulators provide a valuable testing ground for near-term quantum computing applications. Therefore, to explore the feasibility of quantum algorithms in computational chemistry, this study implements the Variational Quantum Eigensolver (VQE) using PennyLane, an open-source quantum computing framework. VQE, a hybrid quantum-classical algorithm, approximates the ground-state energy of molecules by iteratively optimizing a quantum circuit. This case study applies VQE to the hydrogen (H2) molecule and progressively increases molecular complexity to assess its computational performance. The study evaluates how molecule size affects the algorithm’s accuracy, convergence speed, and scalability on simulated quantum hardware. The findings provide insights into the current capabilities and limitations of quantum algorithms for chemistry applications, highlighting key challenges that must be addressed for achieving practical quantum advantage in the field. Reference: [1] Google Quantum AI and Collaborators. (2025). Demonstrating quantum advantage in error-corrected computation. Nature, 638, 920–926. https://doi.org/10.1038/s41586-024-08449-y.
