arXiv:2602.18364v2 Announce Type: replace-cross Abstract: Maximum likelihood prediction (MLP) is a core task at the heart of modern large language models. Here, we study a quantum version of this task for a simplified data model consisting of independent and identically distributed samples, as a first step. The quantum maximum likelihood predictor is obtained by embedding of empirical probability distributions into quantum states and performing a minimization of quantum relative entropy over a given class of states. We provide an interpretation of this predictor in terms of quantum reverse inf
The paper was published on arXiv, signaling a new academic development in the field of quantum machine learning, building on current large language model research.
This research explores fundamental quantum approaches to core AI tasks, potentially laying groundwork for future quantum AI advancements that could surpass classical capabilities.
Current understanding of maximum likelihood prediction is extended into the quantum domain, suggesting new computational paradigms for AI systems.
- · Quantum computing researchers
- · Advanced AI research labs
- · Quantum hardware developers
- · Classical compute-bound AI models (eventually)
Exploration of quantum algorithms for fundamental AI tasks like maximum likelihood prediction.
Potential for quantum computers to achieve superior performance in specific AI applications when such hardware matures.
Reconceptualization of AI architectures to leverage quantum mechanics, leading to entirely new classes of intelligent systems.
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Read at arXiv cs.LG