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

• • AI for Quantum Computing
• • Quantum Computing for AI
• • Quantum Natural Language Processing (Q-NLP)
• • Cognitive Models and Quantum-inspired Formalisms

These topics bring together researchers from quantum computing, quantum information theory, AI, natural language processing, and quantum-inspired formalisms in cognitive science and psychology. We aim to bring together researchers from different disciplines to facilitate knowledge transfer and synergies in these domains. We are encouraging researchers from academia, industry, and government research with theoretical, experimental, or applied contributions to submit their work 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 provide 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 generative techniques, are being investigated for quantum state tomography, quantum data generation, and simulating 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: Vaneet Aggarwal

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 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

Quantum Natural Language Processing (QNLP)

Section chair: Damir Cavar

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, as for example tensor network methods, quantum machine learning, quantum embeddings for natural language semantics and language models, Generative AI, and including graphical languages, and category theory. It aims at building compositional models applied to complex systems, linguistics, and artificial intelligence. The prime aims are to create a space for dialogue, encourage collaboration, and provide a bridge between quantum computing and natural language processing.

The areas covered include, among others:

• • 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

Cognition and decision models using Quantum-inspired formalisms

Section chair: Jerome Busemeyer

During the past 15 years, researchers from physics, economics, cognitive science, and computer science have been exploring the application of quantum theory outside of physics to human judgment and decision making. This research does not rely on the hypothesis that the brain is some type of quantum computer, but instead uses the mathematical principles of quantum theory without the physics to model human behavior. Paper contributions from any of the above fields are welcome that describe new empirical and/or theoretical applications of quantum theory to judgment and decision making behavior.

The areas covered include:

• • Quantum information viewpoint to cognition
• • Quantum foundations and cognition
• • Generalized probabilistic models for decision making
• • Quantum contextuality and generalized contextual models in psychology, economics, and social science
• • Bell's inequality, entanglement with applications to decision-making
• • The role of the complementarity principle in quantum-like modeling
• • Quantum dynamics with applications to decision making, social and political science, ecology, evolution theory
• • Quantum field theory with applications to modeling of the process of decision making
• • Order effects in decision making

Submission

Microsoft Conference Management Toolkit (CMT): Conference Link

Important Dates

• Submission deadline: April 30., 2025
• Notification of acceptance: May 15., 2025
• Camera-ready papers: June 15., 2025
• Conference: August 6.-8., 2025