Quantum AI is the use of quantum computing for computation of machine learning algorithms. Thanks to computational advantages of quantum computing, QuantumAI can help achieve results that are not possible to achieve with classical computers. Quantum mechanics is a universal model based on different principles than those observed in daily life. A quantum model of data is needed to process data with quantum computing. Hybrid quantum-classical models are also necessary in quantum computing for error correction and correct functioning of the quantum computer. QAI is a tokenized computing security protocol with the ability to learn and create a Quantum resistant network through the Cardano Blockchain with it's artificial intelligence computing system. QuantumAI will be the future of decentralized quantum resistant protocols and will work hard to prevent AI threats and secure data.
What are the critical milestones for QuantumAI?
Although quantum AI is an immature technology, there are improvements in quantum computing which increase the potential of quantum AI. However, the quantum AI industry needs critical milestones in order to become a more mature technology. These milestones can be summarized as:
- Less error-prone and more powerful quantum computing systems.
- Widely adopted open-source modeling and training frameworks.
- Substantial and skilled developer ecosystem.
- Compelling AI applications for which quantum computing that outperforms classical computing.
These critical steps would enable QuantumAI for further developments.