About This Research
Understanding how quantum computing could optimize home energy storage through hands-on experimentation and analysis
Site Evolution
This website launched in July 2025 as a broad exploration of energy systems, renewable technologies, and sustainability. After months of research, I realized I wanted to go deeper on one specific question rather than covering everything at a surface level.
On January 18, 2025, I pivoted the site to document this focused research project: using quantum computing to optimize residential solar + battery storage.
The Problem I'm Solving
Imagine you have solar panels on your roof and a battery in your garage. Every day, you face thousands of tiny decisions:
- •Should I use my solar power now or save it for later?
- •Should I charge my battery when electricity is cheap at night?
- •When should I sell excess power back to the grid?
- •How do I maximize savings while keeping the lights on?
This seems simple, but it's actually a complex optimization problem with over 10 billion possible solutions for just one day. Traditional computers can solve it, but quantum computers might do it better - especially when scaling to neighborhood or city-wide systems.
Why Quantum Computing?
Quantum computers work fundamentally differently than regular computers. Instead of processing information as 1s and 0s, they use quantum bits (qubits) that can be both 1 and 0 simultaneously - a property called superposition.
For optimization problems like battery scheduling, this means quantum computers can explore many possible solutions at once, potentially finding better answers faster.
But here's the catch: We're still in the early days of quantum computing. My project explores when and if quantum approaches actually help.
My Approach
Phase 1: Classical Baseline
First, I'm building a "normal" computer program in Python that:
- •Takes real solar generation data from government APIs
- •Uses actual electricity pricing data
- •Calculates the best battery charge/discharge schedule
- •Establishes a baseline for comparison
Why start here? I need to understand the problem deeply with familiar tools before jumping to quantum.
Phase 2: Quantum Implementation
Next, I'll learn quantum computing through IBM's free Qiskit platform and rebuild the same optimizer using:
- •QAOA (Quantum Approximate Optimization Algorithm) - designed for these exact problems
- •Real quantum hardware (IBM's free 100+ qubit processors)
- •Same data as Phase 1 for fair comparison
The learning curve: I'm documenting everything I learn about quantum computing along the way.
Phase 3: Analysis & Comparison
Finally, I'll compare both approaches:
- •Solution quality: Which finds better schedules?
- •Speed: Which runs faster?
- •Scalability: What happens with more complex scenarios?
- •Practicality: When would you actually use quantum?
Why This Project?
I chose this project because it hits several goals:
1. Practical Application
Real problem affecting millions of homes
2. Learning Quantum
Hands-on experience with cutting-edge technology
3. Accessible Data
Free APIs from NREL and EIA
4. Manageable Scope
Can complete in one semester
5. Future Relevant
Energy optimization is increasingly important
Tools & Technologies
Data Sources (All Free)
- •NREL Developer Network - Solar resource data
- •EIA Open Data - Electricity pricing
- •Real-world household consumption patterns
Classical Computing
- •Python for programming
- •NumPy/SciPy for optimization
- •Matplotlib for visualization
Quantum Computing
- •IBM Qiskit (free access)
- •IBM Quantum Platform (10 free minutes/month)
- •QAOA for optimization
Documentation
- •This website for progress updates
- •GitHub for code and data
- •Jupyter notebooks for analysis
About Me
Austin Amissah
Student at The Lawrenceville School, Class of 2027
I launched this site in July 2025 to explore energy systems broadly. Over time, I became fascinated by the optimization challenges in renewable energy and how quantum computing might help solve them. This led to the pivot: focusing on one specific research question I can actually answer.
I'm passionate about the intersection of technology and real-world problems. While I'm still learning quantum computing (this is my first deep dive!), I'm excited to document the journey and share what I discover.
This project combines my interests in:
- •Energy systems and sustainability
- •Computer science and optimization
- •Emerging technologies like quantum computing
- •Making complex topics accessible
Open Source Commitment
All my code, data, and findings will be freely available once the implementation is complete and tested. If you're a student interested in quantum computing or energy optimization, this will be a starting point for your own exploration.
Why open source? Science works best when shared. Plus, I'll learn more from feedback!
Questions I Hope to Answer
By the end of this project, I want to know:
1. How hard is it for a high school student to actually use quantum computers?
2. Does quantum optimization work for real-world energy problems?
3. What are the current limitations of quantum hardware?
4. When might quantum computing become practical for home energy?
5. What did I learn that I couldn't have learned from textbooks alone?
Let's Connect
This is a learning journey, and I'm documenting both successes and failures. If you have questions, suggestions, or want to collaborate on similar research, please reach out!