Probabilistic Safety Assessment (PSA) is a rigorous methodology used to evaluate risks in complex industrial systems. This domain presents significant challenges, particularly when exploring the potential of quantum computing to address combinatorial problems inherent in such assessments. A variety of algorithms have been proposed to tackle PSA in both static and dynamic contexts. However, these approaches face notable limitations in the current Noisy Intermediate-Scale Quantum (NISQ) era, including issues related to noise, insufficient qubit availability, and gate inefficiency.

In this presentation, we provide insights into several hybridization experiments that combine classical and quantum techniques. Furthermore, we explore other promising avenues, particularly those inspired by quantum algorithms, which may offer viable alternatives for advancing risk analysis in industrial systems.

Photons are central carriers of quantum information, offering long coherence times, fast operations, and natural compatibility with quantum communication. However, scalable photonic quantum computing is hindered by the probabilistic nature of entanglement generation with linear optics. Heralded fusion gates succeed only with a fixed probability, so multiplexing many parallel sources and fast optical switches is required to boost success rates. This approach, while in principle scalable, comes at the price of enormous resource overheads in terms of photon sources, active routing, and feed-forward control.

A promising alternative is to replace such complex multiplexed architectures with advanced quantum emitters that act as deterministic sources of entangled photons. In this talk, I will introduce the measurement-based and fusion-based paradigms of photonic quantum computing, emphasizing the role of graph states as the essential resource. I will then describe how quantum emitters—such as spins in semiconductor quantum dots coupled to optical cavities—can directly generate complex entangled states. Finally, I will present recent experimental results demonstrating flexible generation of “caterpillar” graph states with quantum dots, highlighting the path towards scalable and hardware-efficient photonic quantum computing.

Distributed quantum computing (DQC) offers a path to scalability by networking small processors, where shared entanglement is a scarce resource to budget alongside depth and noise. This talk presents two case studies. For PDEs, we introduce a variational solver that minimizes inter-QPU entanglement while preserving solution quality. For MIS on neutral-atom hardware, we outline how a distributed approach could enable MIS on graph topologies beyond the embedding limits of a single device.

The above are based on work in progress for two projects in collaboration with EDF, and with EDF, Pasqal and LIP6, respectively.

Distributed Quantum Computing (DQC) envisions a network of Quantum Processing Units (QPUs) interconnected via classical and quantum channels. Quantum channels enable entanglement sharing between QPUs, allowing inter-device quantum operations. In this context, the compiler plays a key role in automating the mapping of quantum algorithms onto distributed architectures while minimizing entanglement overhead. This talk introduces key ideas and tools for designing such compilers to optimize the scalability and performance of DQC systems.

In this talk we will raise some of the open questions and approaches to distributed quantum computing, and distributed quantum information processing more generally. We will present results on how to share entangled resources across quantum processors and some distribution tasks beyond immediate running of algorithms including networks of sensors and secure multiparty computation.

The Qaptiva platform from Eviden enables the deployment of a complete quantum computing environment on an HPC cluster, with the integration of quantum processors (QPU). We will review recent deployments in national computing centers and discuss the main challenges of scaling up.

Our presentation will address the challenge of building a SaaS platform that offers quantum algorithms. Simply put, we face three key challenges in scaling up:

The challenge of market relevance, in a landscape where hardware maturity has not yet been achieved.
The challenge of skills, both in theoretical aspects (mathematics, data science, and quantum) and in navigating the diverse technological offerings, each with its own development environment.
The challenge of industrialization, as we must provide a «  »as-a-service » » platform capable of functioning in «  »production, » » even though most quantum capabilities currently do not offer service-level guarantees.

It is a difficult and ambitious challenge but very exciting, and we have strategies to address these different aspects.

Integration of QC within HPC compute centers is on the way, making QC a new compute paradigm. QC  offers ways to address previously unreachable problems such as NP problems. On the other hand, the integration of QC in HPC  is quite challenging, requiring the development of new middleware layers.
When considering QC, challenges appears in several domains, involving system oriented features as well as high level libraries providing building blocks for end-user to build HPC/QC ready applications. In order to address this integration, middleware should be structured and interfaces should be defined.