Mastering Algorithmic Mechanism Design on Quantum Computing Networks: Resilient Solutions for Cybersecurity
This tutorial presents a novel integration of algorithmic mechanism design (AMD) with quantum network architectures to address critical vulnerabilities in classical cybersecurity frameworks exposed by modern cyber warfare tactics. The approach uniquely combines inverse game theory principles with quantum network optimization techniques, specifically focusing on Blind Quantum Computing (BQC) protocols implemented through Measurement-Based Quantum Computing (MBQC) architectures and quantum Generative Adversarial Networks (qGANs). By developing a quantum-enhanced mechanism design framework for adversarial multi-agent systems, the work bridges critical gaps in trust minimization for distributed AI infrastructures through quantum-secured coordination protocols. The session structure follows three cohesive phases:
- Strategic Modeling: Application of inverse game theory to multi-agent quantum environments, analyzing Nash equilibria for optimal qubit allocation and error-correction tradeoffs while establishing security-efficiency Pareto frontiers for BQC protocols
- Adaptive Optimization: Demonstration of AMD-driven parameter tuning in MBQC systems using metaheuristic algorithms, validated through adversarial scenario simulations and qGAN-based quantum randomness certification
- Implementation Benchmarking: Cross-platform evaluation of AMD-optimized BQC implementations with accountability models for multi-agent quantum systems, including resource auditing protocols and fidelity verification metrics
From Theory to Practice: Applying Quantum Key Distribution and Post-Quantum Cryptography for 6G Networks
The tutorial proposal focuses on the convergence of quantum threats in the domain of 6G networks. It aims to provide an in-depth study of this convergence, starting with background information on quantum attacks, post-quantum cryptography, and quantum key distribution. It will then explore its execution to 6G networks and their quantum-based threats. The tutorial will include a step-by-step demonstration of two of the demos to illustrate the practical implementation of these concepts. The tutorial is designed for participants with no prerequisite knowledge and aims to introduce them to the application of post-quantum cryptography and quantum key distribution to protect the 6G networks. As this topic is gaining significance and relevance in the telecommunications industry, the tutorial offers attendees the opportunity to learn about cutting-edge security issues for 6G networks and their specific applications from the cybersecurity perspective.
Pauli Propagation: A Framework For Simulating Quantum Systems
In this tutorial, we present the framework of Pauli propagation for simulating quantum systems and quantum circuits. This new framework is particularly suited for quickly estimating expectation values and surrogating parametrized quantum circuits, thus making it a natural candidate for emulating quantum machine learning algorithms and enabling hybrid quantum-classical approaches at scale. Pauli propagation has been shown to efficiently simulate generic noisy and noise-free quantum circuits, as well as variational quantum algorithms that do not suffer from barren plateaus. Beyond its direct applications, the framework offers an alternative perspective on quantum computations and what makes them hard to simulate classically. We will first cover the foundations of Pauli propagation, including common gates and approximations. Everything will be accompanied by pedagogical code examples using the "PauliPropagation.jl" library. Then, we will move on to our theoretical results proving polynomial runtime for classical simulations of certain families of quantum circuits, including a case study on simulating quantum convolutional neural networks in practice. Finally, we will discuss a possible future of Pauli propagation, from its algorithmic manifestations to how it can assist quantum computers at scale.
Quantum Reinforcement Learning: From Foundations to Emerging Applications
Quantum Reinforcement Learning (QRL) sits at the frontier where quantum computing and adaptive decision-making converge, offering the potential to fundamentally reshape sequential decision-making in complex environments. This tutorial provides a structured and accessible introduction to QRL, covering theoretical foundations, algorithmic frameworks, and emerging real-world applications. Participants will learn key concepts such as variational quantum policies, quantum-enhanced exploration, QRL with recurrent policies and distributed/multi-agent quantum reinforcement learning. The tutorial also presents practical use cases of QRL in communication networks, financial modeling, and autonomous systems. Through both conceptual lectures and hands-on demonstrations, attendees will gain actionable insights into building quantum-enhanced learning systems.
Quantum Annealing for Constrained Optimization
We will present in this tutorial how constrained optimization problems and constraint satisfaction problems can be modeled in QUBO and solved by quantum annealing, with the help of several examples. After introducing the basic concepts of quantum annealing, QUBO, CSPs and constrained optimization problems, we will first present several encoding schemes for integer variables and basic constraints in QUBO, with various concrete examples. Then we will detail how to encode more complex constraints such as linear equations and non-linear constraints, for instance the “permutation/all-different” constraint. We will describe QUBO models for well-known constraint problems such as N-queens, Magic Square, Costas Arrays, as well as for constrained optimization problems such as the traveling Salesman Problem (TSP) and the Quadratic Assignment Problem (QAP).