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Related Experiment Video

Updated: Aug 14, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

Machine-learning assisted scheduling optimization and its application in quantum chemical calculations.

Yingjin Ma¹, ZhiYing Li¹, Xin Chen²

¹Computer Network Information Center, Chinese Academy of Sciences, Beijing, China.

Journal of Computational Chemistry

|January 17, 2023

Summary

This summary is machine-generated.

This study introduces a bi-level optimization framework for efficient computational resource use in scientific calculations. Integrating machine learning and load-balancing algorithms accelerates quantum chemical computations on high-performance computing clusters.

Keywords:

distributed computing fragmentation approach high throughput computing interaction energy calculations load-balancing

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Area of Science:

Computational Chemistry
High-Performance Computing
Scientific Computing

Background:

Efficient utilization of computational resources is vital for timely and cost-effective scientific calculations.
Optimizing computational sequences is key to enhancing performance in complex simulations.

Purpose of the Study:

To propose a bi-level optimization framework for computational sequences.
To develop a computational and scheduling engine (ParaEngine) for optimized quantum chemical (QC) calculations.
To integrate machine learning (ML)-assisted static and dynamic load-balancing algorithms.

Main Methods:

Development of a bi-level optimization framework.
Implementation of the ParaEngine for invoking optimized QC calculations.
Integration of ML-assisted static load-balancing and dynamic load-balancing algorithms.

Main Results:

Demonstrated improved computational resource usage rates for high-throughput and large-scale fragmentation QC calculations.
Achieved faster completion of computational tasks using high-performance computing (HPC) clusters.
Validated the framework with benchmark calculations on drug, solvent, protein, and viral systems.

Conclusions:

The proposed framework effectively optimizes computational sequences for scientific calculations.
Integration with HPC clusters significantly enhances the efficiency and speed of QC computations.
The approach offers a promising solution for improving timeliness and economic efficiency in scientific computing.