Scope and Topics
This conference focuses on three aspects of Quantum Computing and Quantum-inspired formalisms:

• • AI for Quantum Computing
• • Quantum Computing for AI
• • Quantum Natural Language Processing (Q-NLP)

These topics bring together researchers from quantum computing, quantum information theory, AI, natural language processing, and quantum-inspired formalisms in cognitive science and psychology, as well as applied disciplines that utilize quantum computing and quantum AI solutions, such as Medical Research, Chemistry, Physics, Biology, or Cybersecurity. We aim to bring together researchers from different disciplines to facilitate knowledge transfer and synergies in these domains. We encourage researchers from academia, industry, and government research to submit their theoretical, experimental, or applied contributions to this conference.

AI for Quantum Computing

Section chair: Samuel Yen-Chi Chen

Recent advancements in quantum computing and artificial intelligence (AI)/machine learning (ML) have intensified interest in the integration of these two fields. Reinforcement learning (RL), a machine learning paradigm where agents optimize decision-making through trial and error, has demonstrated remarkable success in complex sequential decision-making tasks and is being explored for quantum computing applications such as quantum control, error correction, noise mitigation, quantum architecture search, and quantum circuit optimization. Beyond RL, evolutionary algorithms offer powerful search and optimization techniques for designing efficient quantum circuits, tuning variational quantum algorithms, and improving quantum error-correction strategies. Additionally, generative AI models, including deep generative networks and quantum-inspired techniques, are being investigated for quantum state tomography, quantum data generation, and the simulation of quantum systems. Multi-agent learning extends AI-driven quantum advancements by enabling cooperative strategies for quantum resource allocation, distributed quantum computing, and collective optimization of quantum tasks. The interdisciplinary nature of AI and quantum computing fosters collaborations among researchers from computer science, AI/ML, and quantum information science, spanning both industry and academia. This session will explore cutting-edge research and applications that leverage AI methodologies to address key challenges in quantum computing, accelerating the realization of practical quantum advantage.
Topics of Interest include (but are not limited to):

• • Reinforcement Learning for Quantum Computing
     Quantum reinforcement learning algorithms
     RL for quantum control, quantum error correction, quantum architecture search, quantum circuit optimization
     RL-driven quantum compilation and noise mitigation
  
• • Evolutionary Algorithms in Quantum Computing
     Quantum architecture search using evolutionary optimization
     Variational quantum algorithm tuning with genetic algorithms
     Evolutionary strategies for quantum error correction
• • Generative AI for Quantum Applications
     Generative models for quantum state tomography
     Quantum-inspired generative AI for quantum data generation
     Variational autoencoders, GANs or Diffusion Models for quantum system simulation or quantum circuit synthesis
• • Multi-Agent Learning for Quantum Systems
     Distributed quantum computing with AI-driven coordination
     Cooperative quantum resource allocation using multi-agent learning
     Collective optimization strategies for quantum networks
• • AI for Quantum Circuit and Architecture Optimization
     AI-assisted quantum circuit design and optimization
     Machine learning-driven quantum compiler improvements
     Neural network-based approaches for quantum gate synthesis or pulse optimization

Quantum Computing for AI

Section chair: Damir Cavar

Quantum computing and artificial intelligence (AI) are two cutting-edge fields that are increasingly intersecting. This focus area addresses some current research topics in AI using quantum computers for Computer Vision, Cybersecurity, or fundamental research and simulation in Chemistry and Physics.

The areas covered include, among others:

• • Quantum Optimization
• • Quantum Computer Vision
• • Quantum-enhanced AI for Cybersecurity
• • Quantum Machine Learning
     Quantum-Native Machine Learning
• • Complex-valued neural networks with phase-aware activations
     Tensor-network models that map cleanly to qubit layouts
• • Trustworthy Quantum Machine Learning
• • Quantum Optimization Algorithms
• • Hybrid Quantum-Classical Computing
• • Quantum Architecture Design and Synthesis
• • Quantum Circuit Optimization using AI
• • Applications of Quantum AI
• • Quantum Machine Learning Operations
• • Distributed Quantum Machine Learning
• • Post-Quantum AI Security
• • Quantum Foundation Models

Quantum Natural Language Processing (QNLP)

Section chair: Bob Coecke

This topic aims at bringing together academic and industry-based research on natural language processing using quantum computing approaches and quantum-inspired methods. It covers a broad array of topics in NLP, such as tensor network methods, quantum machine learning, quantum embeddings for natural language semantics and language models, Generative AI, and graphical languages and category theory. It aims to build compositional models for complex systems, linguistics, and artificial intelligence. The primary aims are to create a space for dialogue, encourage collaboration, and serve as a bridge between quantum computing and natural language processing.

The areas covered include, among others:

• • Categorical, compositional, diagrammatic QNLP
• • Pregroup grammars and category theory in QNLP
• • Linguistic structure and quantum circuits in QNLP
• • Meaning using quantum states, entanglement, and density matrices
• • Quantum states and computation of natural language meaning (e.g., embeddings)
• • Parsing and generation of natural language using quantum computing
• • Applied domains like document classification, sentiment analysis, anaphora resolution, or emotion detection
• • Large Language Models (LLMs) and quantum computing
• • Quantum transformer architectures, Quantum Selective State Space Models for language generation

Submission

The submission of papers uses the OpenReview site. The following link will take you to the OpenReview website. If you have an OpenReview account, log in and proceed with the submission. If you do not have an OpenReview account, use the link on the login page to create one. We ask authors and reviewers to create an OpenReview Profile at least two weeks in advance of the paper submission deadlines. As a registered author proceed with the submission here:

OpenReview QNLPAI 2026: Conference Link

Student Poster Session

Submission: Authors should submit a 1-page abstract as specified above for Abstracts submissions, and in addition, a draft poster in PDF format.

Important Dates - Main Conference

• Submission deadline: June 7, 2026 (End of Day, Anywhere on Earth (AoE))
• Notification of acceptance: June 21, 2026
• Camera-ready papers: June 30, 2026
• Conference: August 14-16, 2026
• Submission site: OpenReview QNLPAI2026

Important Dates - Student Poster Session

• Submission deadline: June 7, 2026 (End of Day, Anywhere on Earth (AoE))
• Notification of acceptance: July 21, 2026
• Submission site: OpenReview QNLPAI2026