Tuesday, 2nd December 2025 (Quest-IS)
Note: This program includes links that allow direct access to detailed sections of the website (keynotes). Clicking a link will automatically take you to the relevant section, even if it is located on a different page.
Auditorium 1
08:30
Opening Session
Alain Aspect
(2022 Physics Nobel Prize)
09:00
Keynote Speaker : Djeylan Aktas
(Slovak Academy of Sciences, Slovakia)
10:00 - Plenary session "Communications"
Chairman :
10:00 - The Gilles Brassard Laboratory: A Research Platform for Secure Quantum-Era Communications (193) Antoine DIERICK, Lahatra RAKOTONDRAINIBE, Jonathan PISANE
The rise of quantum computing presents a major threat to the security of current communication networks, making it imperative to act now to protect both existing and future data exchanges. Although theoretical solutions—such as post-quantum cryptography (PQC) and quantum key distribution (QKD)—are available; these technologies require further development before integration into current networks.
The Gilles Brassard Laboratory (GBL) responds to this challenge by serving as a research and development platform focused on advancing, testing, and validating key components of quantum-resistant communication networks. This GBL is a quantum research laboratory unique in Europe, and of worldwide scope, a powerful tool equipped with an ultra-secure quantum key distributor QKD. The GBL is organized into two main segments: a quantum segment dedicated to implementing and assessing cryptographic technologies like PQC and QKD, and a radio segment that emulates terrestrial (such as LTE/5G/6G) and space-based communications (DVB-S, CCSDS, 5G NTN). Through collaborative efforts, the lab aims to accelerate the deployment of secure, future-proof communication systems by offering a realistic testing environment for researchers and engineers.
10:20 - Overview of NCIA’s activities in Quantum Communications (205) Joanna SLIWA, Konrad WRONA, Mikolaj PIETRUCZUK
This paper presents an overview of seven experimental studies,
being actively conducted by The NATO Communications and Information Agency, focusing on quantum-safe communications in military applications. The experiments explore a range of topics, including cryptographic agility, quantum-safe satellite communications, secure software supply chains and quantum-resilient friend-or-foe identification for unmanned aerial vehicles (UAVs). Collectively, these studies highlight challenges and design considerations for military systems’ architectures that prioritize security in the face of emerging quantum threats.
10:40 - METROLOGY FOR REAL-WORLD QKD (140) Alice MEDA, Salvatore VIRZÌ, Marco GRAMEGNA, Giorgio BRIDA, Marco GENOVESE, Ivo Pietro DEGIOVANNI
Quantum Key Distribution (QKD) is a technology that enables the sharing of secret cryptographic keys between two distant users (Alice and Bob), with intrinsic security guaranteed by the fundamental laws of nature.
QKD has become a mature technology, and in Europe, all 27 member states are collaborating on a European Commission initiative (EuroQCI) to design, develop, and deploy a quantum communication infrastructure. In Italy, the QUID project is responsible for implementing the Italian segment of EuroQCI [1].
QKD relies on single photons to secure the distribution of the keys and, to become a viable real-world solution, the metrological characterization of optical components and systems is fundamental. To obtain the appropriate security requirements, test and evaluation methods at single-photon level need to be developed; in particular, since the single-photon detectors represent the most vulnerable part of a QKD system, their characterization in terms of operating parameters (quantum efficiency, dead time, jitter, afterpulsing..) is of the utmost importance.
We present the INRIM efforts in the quantum efficiency calibration of single-photon avalanche detectors (SPADs), focusing on QKD application. The detection efficiency is evaluated for a fibre-coupled InGaAs/InP-SPAD [2, 3, 4] and for a free-space Si-SPAD [5]. The calibration is performed using different experimental setups and reference standards with proper traceability chains at the wavelength of 1550 nm and 850 nm respectively. Dependence of detection efficiency on polarization in superconducting nanowire single-photon detectors (SNSPDs) is also reported.
The work is fundamental to align the Italian deployment of QKD, in the framework of QUID, with validation needs, providing test services for the characterization, validation and certification for QKD.
[1] https://quid-euroqci-italy.eu/it/
[2] M. López, A. Meda, G. Porrovecchio, R. A. Starkwood (Kirkwood), M. Genovese, G. Brida, M. Šmid, C. J. Chunnilall, I. P. Degiovanni, and S. Kück, “A study to develop a robust method for measuring the detection efficiency of free-running InGaAs/InP single-photon detectors”, EPJ Quantum Technol. 7, 14, (2020).
[3] H. Georgieva, A. Meda, H. Hofer, S. M. F. Raupach, M. Gramegna, I. P. Degiovanni, M. Genovese, M. López and S. Kück, “Detection of ultra-weak laser pulses by free-running single-photon detectors: Modeling dead time and dark count effects”, Appl. Phys. Lett. 118, 174002, (2021).
[4] S. M. F. Raupach, I. P. Degiovanni, H. Georgieva, A. Meda, H. Hofer, M. Gramegna, M. Genovese, S. Kück, and M. López, “Detection rate dependence of the inherent detection efficiency in single-photon detectors based on avalanche diodes”, Phys. Rev. A 105, 042615 (2022)
[5] S. Virzì, A. Meda, E. Redolfi, M. Gramegna, G. Brida, M. Genovese, I. P. Degiovanni; Detection efficiency characterization for free-space single-photon detectors: Measurement facility and wavelength-dependence investigation. Appl. Phys. Lett. 25, 125 (22): 221108 (2024).
11:00 - QuaNTUM: A Modular Quantum Communication Testbed for Scalable Fiber and Satellite Integration (212) Julien CHÉNEDÉ
We present QuaNTUM, a modular and extensible quantum communication testbed under development at the Technical University of Munich (TUM) by the Quantum Communication Systems Engineering group (Prof. Tobias Vogl). The project aims to enable scalable, flexible, and secure quantum communication across fiber-based campus networks and satellite-ground links. QuaNTUM is designed as an open-access platform for experimental quantum communication protocols, quantum device benchmarking, and hybrid network integration.
The terrestrial network links quantum research institutes across the TUM Garching campus using single-mode fibers in a star-shaped topology. Each node is equipped with polarization-maintaining components, multiplexers, and time-synchronized analysis modules. A central switching hub dynamically routes quantum channels and supports fiber-to-fiber and wavelength-selective switching. Active polarization control with real-time feedback ensures low error rates and stable qubit transmission, making the infrastructure suitable for high-fidelity QKD and entanglement distribution.
A core component of QuaNTUM is its integration of deterministic single-photon sources based on optically active defects in hexagonal boron nitride (hBN). These carbon-based emitters are created through localized electron beam irradiation and exhibit stable emission in the visible spectrum with sub-Poissonian statistics and short excited-state lifetimes at room temperature. Characterization is performed using custom-built photoluminescence microscopy systems, complemented by FDTD simulations to optimize coupling into optical fibers and photonic structures.
One of these sources will be deployed in orbit as part of the QUICK³ CubeSat mission, marking one of the first demonstrations of a solid-state quantum emitter in space. By uniting fiber and free-space links with scalable hardware and open protocols, QuaNTUM serves as a forward-compatible foundation for future hybrid quantum networks—supporting both current testbed research and the long-term vision of a global quantum internet.
11:20
Keynote speaker : Richard Versluis
(TNO / TU Delft)
11:50-12:50 - Lunch break
Auditorium 1
12:50 - Plenary session "Quantum Enabling Technologies"
Chairman : Guillaume DE GIOVANN, Mathieu TAUPIN
12:50 - Thales' Roadmap and Product Innovations in Cryogenics for Quantum Applications (109) Christophe VASSE, Garmt DE JONGE, Emilien DURUPT, Florian DUPE, Daniel WILLEMS
As the quantum technology landscape continues to evolve, the need for advanced cryogenic solutions becomes increasingly crucial. This paper presents Thales innovative products and strategic roadmap aimed at enhancing the capabilities of quantum applications through cryogenics on a large of temperature (between 2K to 80K). We detail Thales state-of-the-art cryogenic technologies, including systems designed for cooling quantum processors, sensors, and other critical components, demonstrating their potential to optimize performance and stability in quantum systems. Furthermore, we outline our vision for the future of cryogenics within quantum technology, highlighting ongoing research initiatives, collaboration efforts, and upcoming product developments. Through a comprehensive analysis of market needs and technological advancements, we emphasize Thales commitment to addressing the challenges of cooling and maintaining quantum coherence.
13:10 - Development of cryogenic infrastructures for quantum computing (114) Simon CRISPEL, Florian MARTIN, Mathieu SZMIGIEL, Jean-Marc BERNHARDT
Quantum computing has recently gained interest from industry, opening new fields of applications. Air Liquide Advanced Technologies, thanks to its experiences on ultra-low temperature systems (CryoConcept, subsidiary has been commercializing Dilution Fridges for 20 years for scientific labs) and on Helium Refrigeration and Liquefaction systems for Physics and Industry, is actively developing solutions to address the many emerging challenges associated with Quantum Data Centers.
Recently, the challenges of scaling up various quantum computing technologies have been highlighted through the roadmaps of several major players. One key area of development is the need for increased cryogenic cooling power, which could be provided by helium refrigerators similar to those used to cool particle accelerator equipment or fusion reactors.
This presentation will address the adaptation of solutions developed by Air Liquide Advanced Technologies over several years for industrial and scientific helium cryogenics applications. It will focus on the upcoming needs of quantum computing, particularly in terms of energy efficiency, distribution, reliability, and operability leading to proposals of new cryogenic architectures.
By exploring these aspects, the presentation aims to contribute to the ongoing discourse surrounding the future of quantum computing and its integration into large-scale data centers, offering insights into the intricate challenges and innovative solutions within this burgeoning field.
13:30 - Preliminary Demonstration of Spin Qubit Control Using the Quantum Instrumentation Control Kit (QICK) (119) Dana EL HAJJ, Vivien SCHMITT, Xavier JEHL, Benjamin CRITON, Guillaume DE GIOVANNI, Jérémie THEZE, Arnaud PERRIN
In this paper, we present initial results demonstrating the use of the Quantum Instrumentation Control Kit (QICK) to perform measurements on a spin qubit. We used the RFSoC to perform energy-selective spin readout through a resonator coupled to the qubit. Thanks to a frequency up-converter, we also drive the spin at the Larmor frequency beyond the RFSoC range, typically 15 GHz. These early-stage measurements provide evidence that QICK can be adapted for spin qubit control. This work opens a path towards using an open-source hardware platform (QICK) to characterize and control spin qubits.
13:50 - Microwave interconnectivity as a scalable solution for Quantum Computing: Engineering, Measurement, and Standardization (120) Laurent PETIT, Evan ICHIR, Fabrice JANOT, Fleury GRANDJEAN, Guillaume MARION, Thierry LE NADAN, Claude BROCHETON, Bastien HUON, Jacques MARTINET, Julien LEGRAND
The transition from prototype to large-scale quantum computers requires a new class of microwave interconnect solutions capable of operating reliably at ultra-low temperatures. This paper presents a modular cryogenic interconnect framework designed for compatibility with superconducting quantum processors, addressing critical requirements for signal fidelity, thermal anchoring, and system scalability. The proposed architecture integrates cryogenic attenuators, filters, and switches optimized for operation in dilution refrigerators down to 10 millikelvin. Multiphysics modeling, including electro-thermal co-simulation and mesoscopic heat transport analysis, guides component design. Measurement validation combines calibrated RF methods with qubit-based performance metrics to assess thermal noise impact and signal integrity. Standardized calibration and benchmarking protocols ensure reproducibility across platforms. This work supports ongoing standardization efforts within CEN/CENELEC and paves the way toward scalable, low-noise quantum infrastructures.
14:10 - Scalable Photonic Control System for Large Scale Quantum Computers (203) Abhilash AMSANPALLY, Maeva FRANCO, Arnaud PERRIN, Jérémie THEZE, Dana EL HAJJ, Guillaume DE GIOVANNI
Scaling quantum computers require control systems that are both high-performance and cryogenically efficient. Traditional microwave-based electronics struggle in this environment due to heat generation (active and passive), signal loss, and electromagnetic interference. To address these challenges, we introduce fully photonic control architecture using Viqthor’s MENTHOR, a multichannel RF-over-Fiber (RFoF) transmitter designed for quantum computing applications. MENTHOR converts microwave signals up to 18 GHz into optical signals, transmitting them over single-mode fiber to reduce active heat load and signal loss. With an integrated cryo-compatible multi-channel photo detector, the system enables reliable signal recovery directly inside dilution refrigerators at temperatures as low as 55K. Our Cyro-photonic experimental results feature low relative intensity noise (RIN), high dynamic range, and flat signal response, ensuring high-fidelity control for quantum operations. This platform represents a major step forward in enabling high-density, fiber-based control systems for next-generation quantum processors.
14:30 - Rotary light drag and high transmission for selective frequencies through gain-assisted medium (110) Hazrat ALI, Aneesa NAWAZ, Nadia BOUTABBA
We investigate the rotary light drag in both linear and non-linear gain-assisted medium. Hence, we consider a four-level N-type atomic system with Spontaneously Generated Coherence (SGC) where, two nearly degenerated ground states are excited via a strong coupling laser beam and a weak probe. By deriving the second-order susceptibility via coherences induced by weak probe approximation, we explore the optical properties of the gain-assisted medium. Our analysis shows that the system exhibits a high selective gain profile due to SGC. Typically, the rotary light drag shifts for selected frequencies from clockwise to counterclockwise due to the gain. Moreover, SGC modifies the dispersion profile enhancing the gain by about 40 times, the light drag reaches about $10^{3}$ around the resonance, and a full transmission is achievable ( $\pm 0.5 $ in units of detuning) within a double EIT window. Finally the rotation and translation speed of, the medium are fully analysed. These results might be of great interest in fields such as High Power Laser Systems Generation, where there is a need to design selective wavelength division multiplexing with high transmission at specific wavelengths.
Auditorium 1
14:50 - Parallel session "computing, algorithms, simulation"
Chairman :
14:50 - Quantum Minimal Learning Machine: A Fidelity-Based Approach to Error Mitigation (182) Clemens LINDNER, Joonas HÄMÄLÄINEN, Matti RAASAKKA
We introduce the concept of quantum minimal learning machine (QMLM), a supervised similarity-based learning algorithm. The algorithm is conceptually based on a classical machine learning model and adopted to work with quantum data. We will motivate the theory and run the model as an error mitigation method for various parameters.
15:10 - No Scratch Quantum Computing by Reducing Qubit Overhead for Efficient Arithmetics (185) Omid FAIZY, Norbert WEHN, Paul LUKOWICZ, Maximilian KIEFER-EMMANOUILIDIS
Quantum arithmetic computation requires a substantial number of scratch qubits to stay reversible. These operations necessitate qubit and gate resources equivalent to those needed for the larger of the input or output registers due to state encoding. Quantum Hamiltonian Computing (QHC) introduces a novel approach by encoding input for logic operations within a single rotating quantum gate. This innovation reduces the required qubit register N to the size of the output states O, where N = log2 O. Leveraging QHC principles, we present reversible half-adder and full-adder circuits that compress the standard Toffoli + CNOT layout [Vedral et al., PRA, 54, 11, (1996)] from three-qubit and four-qubit formats for the Quantum half-adder circuit and five sequential
Fredkin gates using five qubits [Moutinho et al., PRX Energy 2, 033002
(2023)] for full-adder circuit; into a two-qubit, 4×4 Hilbert space. This
scheme, presented here, is optimized for classical logic evaluated on quantum hardware, which due to unitary evolution can bypass classical CMOS energy limitations to a certain degree. Although we avoid superposition of input and output states in this manuscript, this remains feasible in principle. We see the best application for QHC in finding the minimal qubit and gate resources needed to evaluate any truth table, advancing FPGA capabilities using integrated quantum circuits or photonics.
15:30 - Optimizing Qubit Routing with Bridge Gates: Extending Quantum Circuit Efficiency Across Arbitrary Distances (168) Ward VAN DER SCHOOT, Willem DE KOK, Frank PHILLIPSON
Qubit routing is a critical challenge in quantum computing, essential for implementing quantum circuits on hardware with limited connectivity. This paper introduces a novel perspective on qubit routing by exploring the use of bridge gates, which enable the execution of controlled NOT (CNOT) operations over non-adjacent qubits without re-routing the qubits. The study highlights the advantages of bridge gates compared to SWAP gates, particularly their ability to preserve qubit assignments and potentially optimize subsequent routing steps.
We propose an extension to the concept of bridge gates by generalizing them for arbitrary distances and provide constructions demonstrating their feasibility. By analysing their performance, we show that larger-distance bridge gates can significantly reduce the number of CNOT gates in certain circuits. Furthermore, a new qubit routing problem is defined, incorporating both SWAP and bridge gates, and we discuss how this impacts the complexity of the problem.
15:50 - Resource Estimation for Matrix Inversion via QSVT with the Clifford+T Gate Set (149) Hiroaki MURAKAMI, Kenzo MAKINO, Yasunori LEE, Keita KANNO, Tomonori FUKUTA
Solving linear systems of equations is a fundamental problem that serves as a crucial foundation in numerous fields. Initiated by the pioneering Harrow-Hassidim-Lloyd algorithm, the asymptotic performance of quantum linear system solvers has steadily improved over time. However, there remains a lack of quantitative evaluation of quantum resources based on explicit gate implementations. In this study, we particularly focus on the matrix inversion method via quantum singular value transformation and explicitly construct a quantum circuit to invert specific matrices using the standard universal gate set, Clifford+T gates. We examine the detailed methods for block-encoding, QSP angle finding, and gate decompositions, and numerically evaluate the overall resources required for the quantum circuit.
16:10 - When Quantum and Classical Models Disagree: Learning Beyond Minimum Norm Least Square (139) Slimane THABET, Léo MONBROUSSOU, Eliott Z. MAMON, Jonas LANDMAN
Quantum Machine Learning Algorithms based on Variational Quantum Circuits (VQCs) are important candidates for useful application of quantum computing. It is known that a VQC is a linear model in a feature space determined by its architecture. Such models can be compared to classical ones using various sets of tools, and surrogate models designed to classically approximate their results were proposed. At the same time, quantum advantages for learning tasks have been proven in the case of discrete data distributions and cryptography primitives. In this work, we propose a general theory of quantum advantages for regression problems. Using previous results, we establish conditions on the weight vectors of the quantum models that are necessary to avoid dequantization. We show that this theory is compatible with previously proven quantum advantages on discrete inputs, and provides examples of advantages for continuous inputs. This separation is connected to large weight vector norm, and we suggest that this can only happen with a high dimensional feature map. Our results demonstrate that it is possible to design quantum models that cannot be classically approximated with good generalization. Finally, we discuss how concentration issues must be considered to design such instances. We expect that our work will be a starting point to design near-term quantum models that avoid dequantization methods by ensuring non-classical convergence properties, and to identify existing quantum models that can be classically approximated.
Auditorium 2
14:50 -Parallel session "computing, algorithms, simulation"
Chairman : Víctor CANIVELL
14:50 - Q-PORT: Quantum Portfolio Optimization with Resource-Efficient Encoding and Scalability Analysis (131) Alberto MARCHISIO, Muhammad Umair HAFEEZ, Nouhaila INNAN, Muhammad KASHIF, Muhammad SHAFIQUE
Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) have emerged as promising approaches for solving portfolio optimization tasks. However, the practical scalability of these methods remains a challenge due to the inherent noise and limitations of Noisy Intermediate-Scale Quantum (NISQ) devices. In this paper, we present Q-PORT (Quantum Portfolio Optimization with Resource-efficient Encoding and Scalability Analysis), a systematic study on the trade-offs between quantum circuit depth, stock encoding strategies, and scalability in quantum portfolio optimization.
We investigate the impact of multi-qubit representations per stock and multi-stock encodings per qubit while varying circuit repetitions and Ansatz types. Our experimental results indicate that increasing qubits per stock offers negligible precision gains compared to classical Mean-Variance Optimization (MVO), while encoding multiple stocks per qubit significantly improves efficiency with minimal precision loss. These findings provide a new pathway toward resource-efficient and scalable quantum portfolio optimization, paving the way for near-term financial applications in quantum computing.
15:10 - Quantum Amplitude Estimation in Practice: A Case Study in Option Pricing (132) Nouhaila INNAN, Muhammad KASHIF, Alberto MARCHISIO, Moonis USMAN, Muhammad SHAFIQUE
Accurately estimating expected payoffs is central to the pricing of European call options, especially when valuation depends on low-probability events in the distribution tail. This study evaluates the performance of Quantum Amplitude Estimation (QAE), using Iterative Amplitude Estimation (IAE) and Maximum Likelihood Amplitude Estimation (MLAE), in pricing European call options based on historical Apple market data. Despite the theoretical advantages of QAE, our experiments show that both quantum estimators return an expected payoff of zero, even in scenarios where classical methods, Black Scholes and Monte Carlo simulation yield significantly positive values. This outcome stems from the limited resolution imposed by limited uncertainty qubits, which inadequately encode small-amplitude, in-the-money price regions. While QAE circuits correctly identify the realized market outcome, they fail to capture the full expectation implied by the distribution. These results highlight the current limitations of QAE under realistic constraints and underscore the importance of enhanced encoding strategies for future quantum financial applications.
15:30 - Quantum Approaches for the Unit Commitment Problem - a literature survey (143) Frank PHILLIPSON, Sven MULLER
This paper investigates the application of quantum computing to the Unit Commitment (UC) problem, a fundamental optimisation challenge in power system operations. From the literature, we can learn about various quantum approaches, including the Quantum Approximate optimisation Algorithm (QAOA), Quantum Annealing (QA), and hybrid quantum-classical methods. The review shows that in the literature it is believed that quantum computing can potentially solve the UC problem more efficiently than classical methods. QAOA shows promise in handling binary decision variables, while hybrid methods enhance computational efficiency and scalability. Quantum Annealing is effective for smaller UC instances, with larger problems requiring partitioning. Despite current hardware limitations, advancements in quantum algorithms and hybrid methods provide a strong foundation for future research. This study highlights the transformative potential of quantum computing in optimising power systems, emphasising the need for continued innovation in quantum hardware and error mitigation techniques.
15:50 - Toward Quantum Utility in Finance: A Robust Data-Driven Algorithm for Asset Clustering (207) Shivam SHARMA, Supreeth MYSORE VENKATESH, Pushkin KACHROO
Clustering financial assets based on return correlations is a fundamental task in portfolio optimization and statistical arbitrage. However, classical clustering methods often fall short when dealing with signed correlation structures, typically requiring lossy transformations and heuristic assumptions such as a fixed number of clusters. In this work, we apply the Graph-based Coalition Structure Generation algorithm (GCS-Q) to directly cluster signed, weighted graphs without relying on such transformations. GCS-Q formulates each partitioning step as a QUBO problem, enabling it to leverage quantum annealing for efficient exploration of exponentially large solution spaces. We validate our approach on both synthetic and real-world financial data, benchmarking against state-of-the-art classical algorithms such as SPONGE and $k$-Medoids. Our experiments demonstrate that GCS-Q consistently achieves higher clustering quality, as measured by Adjusted Rand Index and structural balance penalties, while dynamically determining the number of clusters. These results highlight the practical utility of near-term quantum computing for graph-based unsupervised learning in financial applications.
16:10 - Hot-Starting Quantum Portfolio Optimization (164) Sebastian SCHLÜTTER, Tomislav MARAS, Alexander DOTTERWEICH, Nico PIATKOWSKI
Combinatorial optimization with a smooth and convex objective function arises naturally in applications such as discrete mean-variance portfolio optimization, where assets must be traded in integer quantities. Although optimal solutions to the associated smooth problem can be computed efficiently, existing adiabatic quantum optimization methods cannot leverage this information. Moreover, while various warm-starting strategies have been proposed for gate-based quantum optimization, none of them explicitly integrate insights from the relaxed continuous solution into the QUBO formulation. In this work, a novel approach is introduced that restricts the search space to discrete solutions in the vicinity of the continuous optimum by constructing a compact Hilbert space, thereby reducing the number of required qubits. Experiments on software solvers and a D-Wave Advantage quantum annealer demonstrate that our method outperforms state-of-the-art techniques.
16:30-17:00 - Coffee Break
Auditorium 1
17:00 - Parallel session "computing, algorithms, simulation"
Chairman :
17:00 - Constrained Quantum optimization for Scheduling Aircraft Arrivals (142) Gaspard CASSASSOLLES, Hian Lee KWA, Teck Yoong CHAI, Yung Sze GAN
Air traffic management (ATM) around airports is a critical and complex task, particularly during the landing phase where aircraft must follow predefined routes marked by waypoints. Due to unpredictable factors such as weather conditions or delays, conflicts may arise when multiple aircraft converge on the same waypoint simultaneously, violating required temporal and spatial separation standards. This scheduling challenge can be cast as a constrained combinatorial optimization problem by assigning controlled delays or accelerations to aircraft and is currently solved using solutions such as Arrival Managers (AMAN) running on classical algorithms. However, due to its NP-hard nature, these algorithms may struggle to scale when scheduling very large numbers of aircraft.
In this work, we explore the use of quantum computing for conflict-free scheduling in terminal airspace. We formulate the problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem and apply variational quantum algorithms, in particular the Quantum Approximate Optimization Algorithm (QAOA) and its generalization, QAOAnsatz. These hybrid quantum-classical algorithms are particularly suited for near-term quantum hardware and have demonstrated potential even in the Noisy Intermediate-Scale Quantum (NISQ) era.
We analyze the quantum optimizer’s solution quality across varying circuit depths and classical optimizers for multiple problem sizes, as well as introduce a Conditional Value at Risk (CVaR)-based circuit cost function to guide the optimization toward high-quality solutions. The results obtained are validated on both the ATM scheduling problem and the canonical MaxCut problem. We discuss the trade-off between solution quality and in-constraint probability for the QAOA, the QAOAnsatz, and a more expressive VQE.
17:20 - QAOA in Quantum Datacenters: Parallelization, Simulation, and Orchestration (112) Amana LIAQAT, Ahmed DARWISH, Adrian ROMAN, Stephen DIADAMO
Scaling quantum computing requires networked systems, leveraging HPC for distributed simulation now and quantum networks in the future. Quantum datacenters will be the primary access point for users, but current approaches demand extensive manual decisions and hardware expertise. Tasks like algorithm partitioning, job batching, and resource allocation divert focus from quantum program development. We present a massively parallelized, automated QAOA workflow that integrates problem decomposition, batch job generation, and high-performance simulation. Our framework automates simulator selection, optimizes execution across distributed, heterogeneous resources, and provides a cloud-based infrastructure, enhancing usability and accelerating quantum program development. We find that QAOA partitioning does not significantly degrade optimization performance and often outperforms classical solvers. We introduce our software components – Divi, Maestro, and our cloud platform -demonstrating ease of use and superior performance over existing methods.
17:40 - Towards Large-Scale Satellite Acquisition Scheduling with Hybrid Quantum-Classical Optimization (198) Amer DELILBASIC, Morris RIEDEL, Kristel MICHIELSEN, Gabriele CAVALLARO
The need for on-demand satellite acquisitions for Earth observation is rapidly increasing, along with the availability of public and commercial satellite constellations capable of fulfilling such requests. Scheduling acquisitions optimally remains a computationally challenging task, especially in complex scenarios with multiple satellites, constraints, and high number of requests. Today, heuristic algorithms are commonly used to find sub-optimal solutions that balance computational time and acquisition value, for example in terms of economic return. The potential role of quantum optimization in this domain is still an open question. While recent demonstrations have explored quantum approaches, they are limited to small problem instances due to current hardware constraints and the difficulty of simulating large quantum systems with classical hardware.
In this work, we present a hybrid quantum-classical optimization approach to address acquisition scheduling problems of any size. At each iteration, a classical heuristic generates a global candidate solution. From this, a smaller local sub-problem is extracted and solved using a quantum optimization algorithm. We validate the method on large-scale instances, leveraging the D-Wave Advantage quantum annealer to solve the sub-problems.
18:00 - Quantum model for CVRPTW (190) Imran MEGHAZI, Eric BOURREAU
This paper proposes a quantum algorithm for the capacited vehicle routing problem with time windows (CVRPTW) based on Grover Search framework. This problem is often faced by Postal services in the context of package delivery or other time-sensitive operations.
We provide an implementation on gate based quantum computer of a model inspired by classical «route first, cluster second» technique. The quantum paradigm allows to overcome the inherent suboptimality of this decomposition. In the current NISQ (Noisy Intermediate-Scale Quantum) era, the most important limitation is the number of available qubits which makes time windows and capacity constraints hard to tackle.
We introduce a qubit-efficient split-inspired modeling which adds only a linear number of decision qubits to standard quantum formulations for Traveling Salesman Problem (TSP).
18:20 - A Specialized Hybrid Column Generation Framework for the Quadratic Multi-Knapsack Problem (127) Luis Fernando PÉREZ ARMAS, Samuel DELEPLANQUE, Stefan CREEMERS
This study explores the application of a specialized hybrid column generation framework for solving the \emph{quadratic multi-knapsack problem (QMKP)}, a renowned NP-hard problem in the literature. We also address several of its common variants, which arise in a variety of practical decision-making contexts. Our method integrates a classical linear relaxation for the master problem, followed by the use of quantum annealing to solve the pricing sub-problem. The master problem selects the optimal combination of columns, each representing a feasible assignment of items to knapsacks, while the pricing sub-problem generates promising columns by solving a quadratic unconstrained binary optimization (QUBO) formulation. The use of quadratic terms enables the effective modeling of complex constraints, such as item incompatibilities or grouping requirements. In addition, the quantum annealer is leveraged not only for its ability to solve QUBOs, but also for its capacity to generate diverse, high-quality solutions in a short amount of time, that can be efficiently be integrated into the relaxed master problem. Through extensive computational experiments, we demonstrate the potential of this hybrid framework to efficiently solve QMKP instances, achieving high-quality solutions with notable computational advantages over classical approaches.
Auditorium 2
17:00 -Parallel session "computing, algorithms, simulation"
Chairman :
17:00 - IQNN-CS: Interpretable Quantum Neural Network for Credit Scoring (135) Abdul Samad KHAN, Nouhaila INNAN, Aeysha KHALIQUE, Muhammad SHAFIQUE
Credit scoring is a high-stakes task in financial services, where model decisions directly impact individuals’ access to credit and are subject to strict regulatory scrutiny. While Quantum Machine Learning (QML) offers new computational capabilities, its black-box nature poses challenges for adoption in domains that demand transparency and trust. In this work, we present IQNN-CS, an interpretable quantum neural network framework designed for multiclass credit risk classification. The architecture combines a variational QNN with a suite of post-hoc explanation techniques tailored for structured data. To address the lack of structured interpretability in QML, we introduce Inter-Class Attribution Alignment (ICAA), a novel metric that quantifies attribution divergence across predicted classes, revealing how the model distinguishes between credit risk categories. Evaluated on two real-world credit datasets, IQNN-CS demonstrates stable training dynamics, competitive predictive performance, and enhanced interpretability. Our results highlight a practical path toward transparent and accountable QML models for financial decision-making.
17:20 - Subspace Preserving Quantum Convolutional Neural Network Architectures (137) Léo MONBroussou, Jonas LANDMAN, Letao WANG, Alex B. GRILO, Elham KASHEFI
Subspace preserving quantum circuits are a class of quantum algorithms that, relying on some symmetries in the computation, can offer theoretical guarantees for their training. Those algorithms have gained extensive interest as they can offer polynomial speed-up and can be used to mimic classical machine learning algorithms. In this work, we propose a novel convolutional neural network architecture model based on Hamming weight preserving quantum circuits. In particular, we introduce convolutional layers, and measurement based pooling layers that preserve the symmetries of the quantum states while realizing non-linearity using gates that are not subspace preserving. Our proposal offers significant polynomial running time advantages over classical deep-learning architecture. We provide an open source simulation library for Hamming weight preserving quantum circuits that can simulate our techniques more efficiently with GPU-oriented libraries. Using this code, we provide examples of architectures that highlight great performances on complex image classification tasks with a limited number of qubits, and with fewer parameters than classical deep-learning architectures.
17:40 - Quantum Geometric Learning: Encoding and Classification in Kendall Shape Spaces (174) Rasha FRIJI
Kendall shape spaces provide a powerful geometric framework for modeling shapes independently of rotation, translation, and scale. These spaces, which arise naturally in statistical shape analysis and morphometrics, are structured as quotient Riemannian manifolds, such as complex projective spaces. In parallel, quantum machine learning (QML) has shown promise in tackling high-dimensional data and structured learning problems, yet its extension to non-Euclidean data domains remains largely unexplored. In this work, we propose a theoretical framework that enables QML algorithms to operate on Kendall shape spaces. We define a quantum encoding scheme that maps centered and normalized shape configurations into quantum states via amplitude encoding on the complex projective manifold. Furthermore, we introduce a quantum kernel function grounded in the geodesic distance between shapes, enabling quantum-enhanced classification and clustering algorithms. This approach opens the door to geometric quantum learning on structured manifolds and offers new perspectives for the efficient quantum processing of shape-based data. We discuss the feasibility of implementing this framework on NISQ hardware and highlight its potential for future applications in quantum shape analysis and beyond.
18:00 - Quantum-Enhanced Consensus Clustering through Quantum Annealing and QAOA (199) Daniele FRANCH, Rui WANG, Amer DELILBASIC, Kristel MICHIELSEN, Gabriele CAVALLARO
Consensus clustering aggregates multiple base clusterings into a single robust solution but poses significant computational challenges due to its NP-hard nature. We present a Quadratic Unconstrained Binary Optimization (QUBO) formulation tailored for consensus clustering, incorporating a pairwise disagreement cost and a transitivity constraint enforced through adaptive slack variables. To address scalability, we propose a refinement strategy that selectively penalizes only the violating triplets and iteratively improves approximate solutions by modifying the QUBO coefficients. This approach reduces the complexity while preserving the solution quality. We aim to evaluate the method using synthetic and Earth Observation datasets on both quantum and classical algorithms, including Quantum Annealing (QA), Quantum Approximate Optimization Algorithm (QAOA), and Simulated Annealing. Preliminary results show that our formulation effectively captures clustering structure and that the proposed refinements significantly improve solution quality, offering a viable path toward scalable quantum-assisted clustering.
18:20 - Hybrid quantum classical algorithms: a cloud on-demand viewpoint (227) Aleksander WENNERSTEEN, Kemal BIDZHIEV, Mauro D'ARCANGELO, Matthieu MOREAU, Anton QUELLE, Alexandre DAUPHIN, Mourad BEJI
In the last decade, advances in quantum technologies have allowed for the rapid development of industrialized quantum processing units. These new devices exploit the laws of quantum mechanics to perform complex calculations. Quantum processing units require new ways of thinking and programming. In particular, these new algorithms will be hybrid, with part of the computation performed on classical high-performance computing hardware and part on the dedicated quantum hardware.
At Pasqal, we have developed a cloud platform hosting a neutral atom quantum processing unit (QPU) operating in the analog paradigm and a series of hybrid quantum classical algorithms that cover applications such as quantum optimization, quantum machine learning and quantum simulation.
In this paper, we will show how this platform is used during the execution of real workloads.