From Feynman’s Vision to Quantum AI: Bridging QM, MD, and Machine Learning for Next-Generation Discovery

Once the great American physicist Richard Feynman said, “Nature isn’t classical… and if you want to make a simulation of nature, you’d better make it quantum mechanical.” The "Quantum Man" said it right. Quantum mechanics is a huge, versatile scope of applied physics. It can bring together molecular dynamics and AI in a single line as well. Just for an example, in modern drug discovery, Quantum Mechanics (QM) and Molecular Dynamics (MD) combined help the investigator to orchestrate the medicine precisely. It can prevent a virus from spreading by analyzing a mixture with a specific protein chain. This modified methodology is known as QM/MM. Here, the quantum part behaves like a microscopic simulator to ensure the electronic mix-up where the drug meets the protein. On the other hand, molecular dynamics deal with the physical constant movement of the thousands of surrounding atoms. For more accuracy, the modern-day wizard, AI, comes to play its role. Many quantum programming languages have been launched, such as IBM's Qiskit, Google's Cirq, and Microsoft's Q#. These languages are able to build QML (Quantum Machine Learning) models, which help to create quantum circuits to analyze molecular interactions. Companies like Volkswagen and DHL are using QML to control the traffic flow. IBM is trying to implement QML in Li-S battery for the best way to store energy. Variational Quantum Eigensolver (VQE) algorithms can predict the energy level of ground-state molecules. Quantum AI is the next powerful uprising tool; it will help researchers accelerate material discovery. By predicting properties and interactions, such as conductivity and stability, faster than ever before, quantum computing will guide us from material simulation to physical-world application at a next-generation level. Quantum computing is more than technology.

References: 

[1] Feynman (1982). Simulating Physics with Computers. International Journal of Theoretical Physics. 

[2] IBM Quantum. Qiskit Documentation. 

https://qiskit.org

[3] Google AI Quantum. Cirq Documentation. 

https://quantumai.google/cirq

[4] Microsoft Quantum. Q# Documentation. 

https://learn.microsoft.com/quantum

[5] Variational Quantum Eigensolver – Peruzzo et al. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications. 

[6] Quantum Mechanics/Molecular Mechanics – Senn, H. M., & Thiel, W. (2009). QM/MM Methods for Biomolecular Systems. Angewandte Chemie. 

[7] Quantum Machine Learning – Biamonte et al. (2017). Quantum Machine Learning. Nature.

Written By,

Mahmud Hasin Azwad

3rd year, 2nd semester

BSc in Mechanical Engineering, AUST