Program

Abstracts (in no particular order)

Stuart Hadfield "Hybrid quantum-classical approaches to combinatorial optimization"

(Tentative) Effective applications of quantum computers to real-world problems remains challenging. Hard combinatorial optimization problems, ubiquitous across science and industry, are attractive targets for quantum enhancements. While significant progress has been made, modern quantum gate-model devices or annealers still have much room for improvement toward becoming competitive with sophisticated classical solvers, which have benefited from decades of development, empirical testing, and refinement. This has inspired a new paradigm of hybrid approaches that directly leverage the power of classical compute and algorithm design, while leveraging quantum devices for subroutines within an overall solver framework. This talk explores two promising directions: first, Noise-Directed Adaptive Remapping (NDAR), a meta-heuristic algorithm that leverages noise to solve binary optimization problems, showing significant improvements on an 82-qubit device. Second, Iterative Quantum Optimization (IQO), where quantum subroutines are embedded within effective classical algorithms like greedy or local search methods. We argue that hybrid approaches offer a promising path toward quantum utility with near term quantum devices.

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.

Jonas Stein "Constraint-preserving quantum optimization via indicator functions"

One of the biggest challenges in quantum optimization algorithms is the efficient incorporation of constraints. While straightforward penalty-based approaches effectively enlarge and roughen the search space, the standardly employed mixer-based approaches often require deep circuit constructions. This talk highlights a frequently dismissed alternative: Encoding constraints through an indicator function that maps all infeasible solutions to a single, most-expensive cost value. For the QAOA, this can be accomplished by controlling the application of the cost unitary on an ancillary qubit, that encodes the feasibility of the current superposition of solutions, which in turn can be computed using subroutines such as the QPE. This approach preserves the modular structure of the QAOA and allows for the combination of different constraint-enforcing techniques such as XY-mixers for Hamming-weight constraints and indicator functions for inequality constraints. The talk concludes by showcasing the potential benefits of this modularity for a use case in energy grid management. 

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.

Jeanette Lorenz "How applications can guide developments in quantum computing"

Recent years have seen significant progress towards error-corrected qubits, but presently available quantum computers remain limited in their number of qubits and quality. In parallel, significant efforts were put into exploring applications of quantum computing, but without resulting in any practical, industrially relevant quantum advantage yet. In principle, quantum computing is expected to lead to disruptive changes in a variety of different industries and data science tasks, ranging, e.g. from speeding up drug development to tackling NP-hard optimization problems. Still, investigating applications of quantum computing has already provided us with very important, although mostly theoretical insights. For example, investigating variational quantum algorithms pointed us to the difficulties of controlling their sampling overhead and their stability in general. We also realized that quantum computers are likely to work synergistically as quantum accelerators alongside classical computers, putting us into the situation that we need to control, optimize and improve their interplay. We are also required to understand when and how a prospective quantum advantage will result in an overall benefit for quantum-classical algorithms. Here, it turns out that the performance of classical and quantum parts are closely linked and depend on each other. Eventually, these findings guide us to both the necessity to advance quantum algorithms and software, as well as to advance quantum hardware beyond realizing logical qubits. This talk will expand on these aspects and will additionally bring forward the idea of systematic benchmarks at all layers of the quantum software stack to be able to also quantify prospective benefits of quantum computing practically.

Titos Matsakos "Quantum algorithms for portfolio optimisation and financial risk management"

Quantum algorithms have a range of applications in both portfolio and financial risk management. I will present quantum circuits that implement stochastic models for equity, interest rate, and credit risk factors, addressing both market and credit risk use cases. The generation of risk factor scenarios within quantum circuits enables Monte Carlo simulations, bypassing the computational cost of generating probability distributions in a classical computer. Additionally, I will present a quantum algorithm for portfolio optimisation, which reformulates discrete optimization as an unstructured quantum search problem. This approach can handle more complex objective functions than those of the QUBO framework.

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, 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.

Alexander Stasik "Quantum random walks for decision making and optimization"

Random walks provide a natural framework for modeling sequential discrete decision processes. Their quantum counterparts have been proposed as potential tools for addressing such problems, particularly if suitable oracles can be constructed to encode general loss functions over decision sequences. In this talk, we discuss perspectives on how quantum random walks could be applied to decision-making and optimization problems, and outline challenges and opportunities in oracle design.

Giorgio Sartor "An exact branch and bound algorithm for the generalized qubit mapping problem"

Quantum circuits are typically represented by a (ordered) sequence of gates over a set of virtual qubits. During compilation, the virtual qubits of the gates are assigned to the physical qubits of the underlying quantum hardware. To ensure that the resulting circuit respects hardware connectivity constraints, additional SWAP gates are inserted as needed. Together, this is called the Qubit Mapping Problem (QMP), which is known to be NP-hard. A very common way to deal with the complexity of the QMP is to partition the sequence of gates into a sequence of gate groups (or layers). However, this imposes a couple of important restrictions: (1) SWAP gates can only be added between pairs of consecutive groups, and (2) all the gates belonging to a certain group have to be executed (in parallel) in the same time slot. The first one prevents gates to be re-arranged optimally, while the second one imposes a time discretization that practically ignores gate execution time. Here we present a flexible branch and bound algorithm for a generalized version of the QMP that either considers or ignores the gate layering and the gate execution time. The algorithm can find proven optimal solutions for all variations of the QMP, but also offers a great platform for different heuristic algorithms. We present results on several benchmark sets of small quantum circuits, and we show how ignoring the layering can significantly improve some key performance indicators of the compiled circuit.

Ruben Pariente Bassa "A quantum greedy algorithm for the minimum vertex cover"

The Minimum Vertex Cover (MVC) problem, a fundamental NP-hard combinatorial optimization task, seeks the smallest set of vertices in a graph such that every edge is incident to at least one selected vertex. I introduce a quantum greedy algorithm for MVC that combines a constraint-preserving quantum ansatz with a classical sequential variable-fixing strategy. Using a single-layer multi-controlled rotation circuit, the quantum state encodes an unbalanced superposition over all feasible covers, enabling greedy selection toward minimal solutions. This hybrid approach preserves feasibility by construction, explores the solution space efficiently, and retains higher-dimensional superpositions throughout the optimization, offering a scalable alternative to penalty-based QAOA for constrained combinatorial optimization on near-term quantum hardware.

Franz Georg Fuchs "Constraint-Preserving Quantum Optimization: Mixers, Encodings, and Efficient Circuits"

Constraint-preserving quantum optimization offers a principled alternative to penalty-based methods by restricting quantum evolution to the feasible subspace of an optimization problem. In this work, we unify and extend recent advances in constrained quantum algorithms by jointly addressing three core components: mixer Hamiltonians, problem encodings, and circuit-level implementations. We develop a general framework for constructing mixers that guarantee feasibility throughout the evolution, including stabilizer-based and projector-defined constructions that apply to a wide range of combinatorial and physical constraints. We analyze encoding strategies for discrete optimization problems, with particular emphasis on non-power-of-two variable domains, and show how subspace encodings and balanced full-space encodings impact both optimization landscapes and hardware resources. Finally, we present compact circuit constructions for implementing constrained evolutions, enabling efficient realization of mixers and excitation operators with reduced entangling-gate and fault-tolerant overhead. Together, these results clarify the trade-offs between expressivity, resource efficiency, and algorithmic performance in constraint-preserving quantum optimization, and provide practical guidelines for designing scalable quantum optimization algorithms on near-term and fault-tolerant quantum hardware.