AtomoraCQ

What Inspired the Project AtomoraCQ

I grew up in Gurgaon, where every winter the air quality reaches hazardous levels. Schools shut down, outdoor activities stop, and the skyline disappears behind a grey haze. While studying engineering and computational sciences, I realized that long-term environmental challenges are deeply connected to material science. Cleaner batteries, better carbon capture systems, and low-emission industrial processes all depend on discovering advanced materials. However, material discovery often takes 5–15 years due to expensive experimentation and computational limitations. That gap between urgency and innovation inspired AtomoraCQ.

The Idea

AtomoraCQ is an engineering acceleration platform that combines quantum algorithms and machine learning to fast-track the discovery of climate-resilient materials. The goal is not to replace laboratory research, but to dramatically reduce the search space by predicting which materials are worth testing. By integrating hybrid quantum-classical workflows, the platform enables smarter screening for : 1.Sustainable energy materials 2.Carbon capture compounds 3.Climate-resilient infrastructure materials

How I Built It

• AtomoraCQ was built using: 1.Python for system integration 2.Qiskit for quantum circuit simulation 3.Variational Quantum Eigensolver (VQE) for molecular energy estimation 4.Scikit-learn / PyTorch for machine learning ranking models 5.Scientific datasets for benchmarking and validation

• The architecture is hybrid: 1.Quantum simulations estimate molecular properties. 2.A machine learning layer learns performance patterns. 3.A ranking engine filters and prioritizes promising materials.

This allows early-stage screening without exhaustive brute-force simulation.

What I Learned

Through building AtomoraCQ, I learned: 1.The practical limitations of current quantum hardware 2.The importance of hybrid architectures over purely quantum approaches 3.How domain-specific AI can outperform generic ML tools 4.The value of designing technology with scalability in mind 5.Most importantly, I learned that innovation at the intersection of disciplines—quantum computing, AI, and environmental engineering—creates exponential potential.

Challenges Faced

1. Quantum Hardware Limitations Current devices are noisy and limited in scale. Solution: Designed the system to run on simulators and remain hardware-agnostic.

2. Data Availability High-quality materials datasets are complex and inconsistent. Solution: Focused on benchmark datasets and structured preprocessing.

3. Balancing Vision with Feasibility It was crucial to ensure the project was technically credible, not just ambitious.

Impact Vision

AtomoraCQ aims to reduce material discovery timelines from years to months by providing an intelligent computational screening layer. By accelerating sustainable material innovation, the platform contributes to: 1.Faster clean energy development 2.More efficient carbon capture 3.Engineering solutions for climate resilience

AtomoraCQ represents not just a technical project, but a long-term vision to accelerate environmental engineering through computational innovation.

Built With

• jupyter-notebook
• nisq-compatible
• numpy
• pandas
• python
• pytorch
• qiskit
• quantum
• quantumsimulation
• scikit-learn
• scipy
• sdg-13
• vqe