Nobel Prize-Winning AI Breakthrough Paves the Way for Quantum Chemistry

The Nobel Prize in Chemistry was awarded to three leaders in AI for predicting protein structures, while a Korean research team made strides in quantum computing, estimating molecular properties with unprecedented accuracy and fewer resources, promising advancements in drug development and material sciences.

The Nobel Prize in Chemistry was just awarded to Professor David Baker from the University of Washington, Google DeepMind CEO Hassabis, and Principal Investigator John Jumper. Their groundbreaking work uses AI to predict protein structures, unlocking new possibilities for drug discovery and the creation of advanced materials. As AI and data science continue to revolutionize research, quantum computing is emerging as another transformative force in these fields.

Advancements in Quantum Computing

At the Korea Institute of Science and Technology (KIST), Dr. Hyang-Tag Lim’s research team has made significant strides in quantum computing. They developed an algorithm capable of estimating interatomic bond distances and ground state energies with chemical accuracy, all while using fewer resources than traditional methods. Remarkably, their approach achieves this precision without relying on quantum error mitigation techniques, setting a new standard for efficient quantum calculations.

Overcoming Quantum Computing Challenges

Quantum computers have the disadvantage of rapidly increasing errors as the computational space grows at the current level. To overcome this, the Variational Quantum Eigensolver (VQE) method, which combines the advantages of classical and quantum computers, has emerged. VQE is a hybrid algorithm designed to use a Quantum Processing Unit (QPU) and a Classical Processing Unit (CPU) together to perform faster computations. Global research teams, including IBM and Google, are investigating it in a variety of quantum systems, including superconducting and trapped-ion systems. However, qubit-based VQE is currently only implemented up to 2 qubits in photonic systems and 12 qubits in superconducting systems and is challenged by error issues that make it difficult to scale when more qubits and complex computations are required.

Breakthroughs With Qudits

Instead of qubits, the team utilized a higher-dimensional form of quantum information called a qudit. A qudit is a quantum unit that can have multiple states, including 0, 1, and 2, in addition to the 0 and 1 that a traditional qubit can represent, which is advantageous for complex quantum computations. In this study, a qudit was implemented by the orbital angular momentum state of a single-photon, and dimensional expansion was possible by adjusting the phase of a photon through holographic images. This allowed for high-dimensional calculations without complex quantum gates, reducing errors.

Impact and Future Applications

The team used the method to perform quantum chemistry calculations with VQE to estimate the bond length between hydrogen molecules in four dimensions and lithium hydride (LiH) molecules in 16 dimensions, the first time 16-dimensional calculations have been realized in photonic systems. While conventional VQEs from IBM, Google, and others require error mitigation techniques for chemical accuracy, the KIST team’s VQE achieved chemical accuracy without any error mitigation techniques. This demonstrates how high accuracy can be achieved with fewer resources, showing the potential for widespread application in industries where molecular properties are important. It is also expected to be useful in solving complex problems such as climate modeling.

“By securing qudit-based quantum computing technology that can achieve chemical accuracy with fewer resources, we expect it to be used in various practical fields, such as developing new drugs and improving battery performance,” said Dr. Hyang-Tag Lim of KIST.

Reference: “Qudit-based variational quantum eigensolver using photonic orbital angular momentum states” by Byungjoo Kim, Kang-Min Hu, Myung-Hyun Sohn, Yosep Kim, Yong-Su Kim, Seung-Woo Lee and Hyang-Tag Lim, 23 October 2024, Science Advances.
DOI: 10.1126/sciadv.ado3472

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