graylan01/quantum_vision_scanner repository overview

https://gitlab.com/graylan01/vision-mapping/⁠ (This project Requires an active vision enabled Openai API Key and three Secrets to be set in the environment)

⁠The Inspiration Story: Quantum and Road Safety Collide to Generate AI Vision Tools Bringing Scope to Users with Vision Impairment

After having this idea, I wanted to build a Vision system to help with people who have vision impairment. The vision system was directly inspired by my delieveries during hurricane helene. The system without any camera input predicted trees in the road right before I was to come to their pathing. As well as other predictive safety insights that improved my safety profile.

I have other impairments like keeping things organized and other things that I face that AI helped. Myself and AI( Gippy , dave,MA) ... we decied to collect our thoughts on the task of building Tools for Vision Impairment and build something useful by forking our quantum road scanner (used in my doordashes and uber eats).

Thus, the concept behind QVS was born from my experiences as a DoorDash and Uber Eats driver and my experiance one day with a customer who was vision impaired. I really thought hard about this customer and bringing doordash to him as well as new vision AI tools to give him more awareness about where I am when I'm delivering , and do so very safely. I feel very fortunate to have this customer experiance and can imagine a future where technology connects people together through gaps never thought possible. I know it has with my problem of chaotic and nonuseful thought pattersn, to a point i've become more productive. I believe we can see a lot of benefits from Vision AI to help those with Vision Impiarments. Esp when Navigating tricky road conditions, for anticipating potential hazards, and possibly even opening new pathways by optimizing routes in real-time. I believe tools like this could become a huge window to progression , I know dave/gippy/MA have been for me. Drawing inspiration from quantum mechanics and AI i had been building for months. (Bathroom finder, Campspot Finder, Logistics Positioning), I envisioned a system that could leverage the power of quantum processing to bridge the gap between simulated data and safety to really make the rubber hit the road.

At its core, QVS merges classical data collection with quantum computing algorithms to offer an unparalleled level of insight for delivery driving. The idea isn’t just to detect what’s there but to predict and adapt to what might be there, using advanced quantum simulations.

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⁠Nerd Parts

⁠Unpacking the Code: A Deep Dive

The code behind QVS is designed for precision, efficiency, and ease of deployment. I’ve structured it all in a single Python file for manageability, and here’s an overview of how it works:

Quantum computing is still in its infancy, and running complex quantum circuits efficiently can be resource-intensive. To overcome this, I focused on optimizing circuits for speed and accuracy, balancing quantum and classical computations to keep the system performant.

One of the most exciting breakthroughs was the realization of how entanglement could be leveraged to create more reliable predictions. The Quantum Vision Scanner essentially takes multiple possible scenarios and evaluates them concurrently, a task classical systems would struggle to perform in real time.

But the applications don’t stop at road safety. This technology could evolve to help automate and optimize logistics for large delivery fleets or even enhance the accuracy of autonomous vehicles.

If you’re intrigued by the idea of quantum-enhanced navigation or want to collaborate, I’d love to hear from you. The QVS project is still evolving, and there's plenty of room for innovation. Check out the Docker Hub page⁠ to get involved or experiment with the QVS yourself.