Quantum Computing and Theoretical Chemistry (QCTC) Lab.

Department of Chemistry, Yonsei University
Department of Quantum Information, Yonsei University
Department of Computational Science and Engineering, Yonsei University

Joonsuk Huh's Research Group

We develop efficient quantum algorithms for quantum chemistry, quantum many-body physics, and computational biology.

Quantum Algorithms

In 1981, Richard Feynman suggested the concept of quantum computation to simulate quantum mechanical systems. His proposal has motivated many researchers to understand the computational power of quantum computers, resulting in a monumental quantum algorithm for factoring known as Shor's algorithm. While Shor's algorithm is for a mathematical problem, its exponential speedup has encouraged enormous efforts to find "quantum advantage" in solving problems in chemistry and physics.

We develop quantum algorithms that can outperform classical algorithms in quantum chemistry and many-body physics, e.g., estimating ground state energies and simulating the dynamics of quantum systems. While we mainly focus on spin models and fermionic systems, we also study bosonic systems and explore hybrid oscillator-qubit quantum processors.

Quantum-Classical Hybrid Algorithms

Quantum-classical hybrid algorithms take advantage of both quantum and classical computation. As current quantum hardware is noisy and not sufficiently large, we seek the utility of quantum computation through hybrid approaches. We aim to develop efficient quantum-classical hybrid algorithms for estimating the ground state energy of many-body systems and solving combinatorial optimization problems.

Bioinformatics and Drug Design on Quantum Computers

Bioinformatics

Many problems in bioinformatics are hard combinatorial problems, so classical algorithms become intractable for large biological data. Quantum algorithms such as quantum annealing and the quantum approximate optimization algorithm have shown theoretically and empirically promising results for such problems. Our group applies quantum algorithms to challenging biological problems such as multiple sequence alignment, codon optimization, and small-molecule design.

Drug Discovery & Drug Design

Novel drug discovery requires a tremendous amount of resources, but drug approval rates have been gradually decreasing. We use quantum algorithms to accelerate the process of drug design by targeting one of the bottlenecks in drug development: molecular docking.

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