Deep Learning
Universal model for Quantum Deep Learning and
Quantum State Preparation
- Built To Order
- Production Ready
Renowned AI pioneer Geoffrey Hinton—the father of Deep Learning—has acknowledged the limitations of the current deep learning paradigm. According to him:
“We have to do away with backpropagation. That’s not how our brain works. We have to throw everything away and start all over again. All this won’t lead to AGI.”
He proposed the Forward-Forward Algorithm as an alternative, and has been iterating on it ever since.
At Automatski, we went beyond backpropagation decades ago.
Our deep learning platform is built on pure forward passes and adaptive local adjustments. No backtracking. No backward gradient computation.
- No GPUs needed for training or inference
- 99% lower energy consumption
- 95% lower compute cost
- 100x larger models trainable on commodity hardware
This is not theoretical. This is production-ready and built-to-order for customers building next-gen cognitive systems. Beat that, OpenAI, Microsoft, Google, Nvidia, Intel, AMD! 🙂
We’ve developed the most universal model for Quantum Deep Learning and Quantum State Preparation to date.
At its core is a Universal Ansatz Circuit, guided by system-level Simulator/Computer Instructions—optimally designed to prepare quantum states and perform learning tasks efficiently.
Key Capabilities:
- Polynomial (and in many cases Exponential) Quantum Advantage
- Universality makes it compatible with any quantum hardware platform
- Directly usable in Quantum AI, chemistry, cryptography, optimization, and simulation problems
Unlike other quantum deep learning approaches, which struggle to scale or deliver speedups, our architecture is both practical and scalable.