Introduction

Artificial intelligence has achieved remarkable progress, yet fundamental questions remain about representation, learning, reasoning, generalization, causality, computation, and intelligent agency. Most existing approaches are grounded in classical mathematics, probability, logic, and computation. Quantum information and computation provide distinct mathematical and computational frameworks that may open new ways to formulate and study these problems.

The Quantum Foundations of Intelligence (QFI) workshop explores quantum information and computation not merely as technologies for accelerating existing machine learning workloads, but as sources of new mathematical and algorithmic principles for intelligence. We are particularly interested in how concepts from quantum theory, including non-commutativity, contextuality, superposition, entanglement, measurement, quantum probability, and quantum processes, can inspire new theories, representations, algorithms, and principles of computation and intelligence. Contributions may be theoretical, mathematical, computational, or empirical, including approaches implemented entirely on classical hardware.

The workshop brings together AI and machine learning with quantum computing, quantum information, theoretical computer science, mathematics, logic, information theory, cognitive science, and philosophy of computation. We seek work that develops new quantum and quantum-inspired representations and learning paradigms; establishes connections between quantum computation and core problems in AI and computer science; explores quantum generative, cognitive, and multimodal models; identifies quantum computational advantages, expressivity, and fundamental limitations; or develops quantum approaches to reasoning, causality, decision-making, and intelligent agency.

Research may address language and multimodal intelligence, embodied and physical AI, autonomous systems, scientific discovery, cognitive modeling, and brain–computer interfaces, provided that the central contribution is grounded in quantum computational or informational principles.

Core Focus Tracks

Quantum Foundations and Representations
Quantum probability, Hilbert-space representations, non-commutative structures, contextuality, entanglement, compositionality, and mathematical frameworks for intelligence.
Quantum Learning and Reasoning
Quantum learning theory, sample and query complexity, quantum inference, quantum logic, quantum kernels and models, generalization, reasoning, and neural-symbolic approaches.
Quantum Information, Computation, and Complexity
Quantum algorithms, information processing, communication, search, computational complexity, simulation, quantum advantage, and fundamental computational limits.
Quantum Causality and Decision-Making
Quantum causal models, quantum processes, interventions, indefinite causal order, counterfactual reasoning, decision theory, planning, and intelligent agency.
Quantum Generative and Cognitive Models
Quantum generative models, tensor-network approaches, language and multimodal representations, quantum-probabilistic models of cognition, perception, memory, and scientific discovery.
Expressivity, Advantage, and Limits of Quantum Intelligence
Formal classical–quantum learning separations, expressivity and universality, trainability and barren plateaus, quantum-inspired classical benchmarks, classical simulation and dequantization, and fundamental limits of quantum approaches to intelligence.

Important Dates

See Important Dates for all paper and abstract submission deadlines.

Author and Submission Guidelines

Please follow Author and Submission Guidelines to submit your work.

If you are interested in serving on the workshop program committee or paper reviewing, please apply online.