Artificial Quantum Intelligence
- Introduction
AI has rapidly evolved from expert systems to large language models (LLMs) like GPT, achieving remarkable success. However, LLMs still lack self-awareness, reasoning, and adaptability, while classical computing limits their scalability. Quantum computing, with its exponential power, parallelism, and unique properties, offers a path to redefine AI capabilities.
At Jožef Stefan Institute (JSI), cutting-edge research bridges quantum computing and AI, fostering the development of Artificial Quantum Intelligence (AQI). This program focuses on quantum-enhanced LLMs and their path to superintelligence (SAI), leveraging quantum principles to advance AI beyond human cognitive limits. - Objectives
2.1 Path to Superintelligence (SAI): Develop AI architectures integrating quantum learning, reinforcement learning, and cognitive modeling to push AGI toward SAI.
2.2 Quantum AI Foundations: Investigate quantum algorithms for AI optimization. Study quantum entanglement and superposition for faster learning and parallel reasoning.
2.3 Quantum LLM Development: Develop quantum-enhanced neural networks for LLM training.
Improve reasoning, decision-making, and problem-solving through quantum-enhanced architectures. - Research Phases
Phase 1: AI and Quantum Computing Integration (Year 1): Study LLMs, quantum machine learning (QML), and hybrid quantum-classical models. Develop quantum-inspired deep learning for natural language processing (NLP). Build initial quantum AI models, testing fundamental quantum-enhanced computations.
Phase 2: Quantum Large Language Models (Year 2): Train the first quantum-assisted transformer model, focusing on memory efficiency and scalability. Implement quantum optimization and error correction techniques. Experiment with quantum generative AI, enhancing creativity and adaptability in LLMs.
Phase 3: Towards Superintelligence (Year 3): Integrate self-learning mechanisms into quantum LLMs. Develop a quantum cognitive agent capable of adaptive, independent problem-solving.
Address safety, control, and alignment challenges of quantum superintelligence. - Expected Impact
Quantum-enhanced LLMs: Faster, more intelligent AI models with improved adaptability and reasoning. Pathway to Superintelligence: AI capable of autonomous learning and decision-making, approaching true SAI. Breakthroughs in computing: Leveraging quantum mechanics for unprecedented AI performance. Scalability and efficiency: Orders-of-magnitude improvements in speed, memory, and energy consumption. - Conclusion
This three-year program offers an unparalleled opportunity for young researchers to work at the cutting edge of AI and quantum computing. By developing quantum-enhanced LLMs, we aim to bridge the gap between AI and superintelligence, creating systems that not only process data but also reason, adapt, and drive scientific and humanity progress.
Note: The program will adapt to the SOTA progress.