Program

Abstracts:

Daniel Egger "Multi-objective optimization and the quantum combinatorial optimization stack"

Current noisy quantum computers typically tackle optimization problems by first mapping the problem to an Ising Hamiltonian and then sampling candidate solutions using the approximate quantum optimization algorithm. I will discuss recent advances in quantum multi-objective optimization (MOO) and the software stack to execute combinatorial optimization on quantum hardware. MOO problems are challenging classically since one must find a collection of solutions known as the Pareto front. Here, quantum computers can help to quickly generate good solutions that explore the Pareto front. Furthermore, when implementing optimization on quantum computers the software stack presents multiple opportunities to optimize the workflows. Along with MOO, I will discuss topics such as problem modelling, QAOA parameter finding, circuit construction and more. I will also highlight some open-source tools to carry out these tasks.

Adam Glos "Harnessing quantum computer universality for efficient QAOA circuits design"

Many state-of-the-art quantum optimization algorithms like Quantum Approximate Optimization Algorithm (QAOA) require representing the original problem as a binary optimization problem, which is then converted into an equivalent cost Hamiltonian suitable for the quantum device. Implementing each term of the cost Hamiltonian separately often results in high redundancy, significantly increasing the resources required. Instead, I’ll present how to design classical programs for computing the objective function and certifying the constraints, and later compile them to quantum circuits, eliminating the reliance on the explicit binary optimization problem representation. This results in a new variant of the QAOA, which we name the Program-based QAOA (Prog-QAOA). This idea is exploited for optimization tasks like the Travelling Salesman Problem and Max-K-Cut and yields circuits that are near-optimal with respect to all relevant cost measures, e.g., number of qubits, gates, and circuit depth.

Daniel Scherer "Learning to Compile - AI Methods for Quantum Software Optimization"

Artificial intelligence is emerging as a powerful enabler for advancing quantum software across both near-term and fault-tolerant quantum computing. This talk explores how reinforcement learning and neural architectures can address two core challenges in quantum compilation: unitary synthesis and circuit optimization.

I present learning-based methods that frame circuit construction and transformation as sequential decision problems. By combining reinforcement learning with structured representations such as discrete gate sets and ZX diagrams, these approaches enable the automated discovery of efficient circuits and optimization strategies. Overall, the results highlight the potential of AI-driven methods to improve performance of core components of the quantum software stack.

Chinonso Onah "Constraint-Aware Quantum Optimization in Practice: From Transportation and Logistics to Hardware-Efficient Quantum Algorithms"

Many real-world optimization problems in transportation, logistics, energy systems, and scheduling are dominated by hard constraints, yet most quantum optimization approaches still enforce feasibility indirectly via penalty terms. In this talk, I will present an application-driven perspective on constraint-aware quantum optimization, showing how problem–algorithm co-design can lead to concrete performance and resource advantages on near-term hardware. I will focus on permutation- and assignment-type problems motivated by transportation and logistics, including shared transportation and matching scenarios, and show how embedding constraints directly into the quantum representation fundamentally changes algorithmic behavior. Using the Constraint-Enhanced QAOA (CE-QAOA) framework as a unifying example, I will demonstrate how encoding feasibility at the level of the state space and mixer dynamics leads to (i) reduced circuit depth, (ii) elimination of redundant penalty terms, and (iii) improved robustness under realistic coherence limits. The talk will connect theoretical insights—such as mixer-induced connectivity, spectral gap considerations, and controllability on encoded manifolds—to concrete application workflows and benchmarking results. I will also discuss implications for circuit synthesis, compilation, and hardware-aware execution, highlighting why feasibility-focused design could lead to a practical advantage for industrially motivated quantum optimization.

Simone Montangero "Tensor network methods for quantum optimizations"

Tensor network methods provide a natural bridge between classical and quantum algorithms for optimization. They enable the simulation, benchmarking, and validation of quantum optimization strategies on classical hardware. We discuss recent results on integer factorization, equational reasoning, and quantum circuit compilation validated via tensor network techniques.