PAGE CONTENTS
Objectives
Quantum computing promises to revolutionise a number of fields by solving certain computational tasks faster than what can be achieved with classical computers. To fully achieve this ambitious promise, it is crucial to interconnect Quantum Processing Units (QPUs) in order to run algorithms cooperatively in a distributed fashion, tackling problems beyond the capability of a single QPU and thus enabling a whole new range of applications and use cases. In this context, a paramount problem is how to efficiently distribute entanglement over large distances in order to interconnect faraway QPU. In this project, we are going to study architectures for distributed quantum computing (DQC) enabled by space connectivity.
To this aim, we will survey different use cases, quantum algorithms and their suitability for parallelisation. We will also address different qubit platforms and other relevant components, such as entangled photon sources, transducers, and quantum memories. We will run extensive simulations collecting all these ingredients together to identify an architecture supporting as many use cases as possible and to formulate a technology roadmap for future developments.
Challenges
- Identification of use cases where a distributed quantum algorithm outperforms a classical solution.
- Identification of the quantum algorithms more suitable for parallelisation and investigation of the distributed compiler performance.
- Identification of the best qubit platform(s) allowing for long-distance interconnection via entanglement distribution.
- Propose practical architectures to run distributed quantum algorithms. This architecture shall be able to perform non-local two-qubit gate operations that will be required to distribute the computation among different QPU.
- Identification of the most promising components, whose development will enable the successful realisation of as many use cases as possible.
Plan
- Use cases, and reference scenario definition
- Technical Specification
- Optimal Technical Baseline Identification
- Technology Assessment and Development Plan
Current Status
The study investigated the use of a satellite-enabled, distributed distance-based quantum classifier (DDBC) for privacy-preserving machine learning across multiple institutions (e.g. hospitals).
A complete system architecture is developed, integrating ground-based QPUs with satellite-mediated entanglement distribution. Analytical models are constructed for both the quantum algorithm and the communication link, including detailed treatment of Rydberg-based quantum processor noise, entanglement generation probabilities, latency constraints, satellite network topography, and resource scaling.
Extensive simulations were conducted across multiple configurations, varying the number of parties, feature dimensions, shot counts, and noise regimes. Benchmarking against key performance indicators shows that basic distributed operation and architectural feasibility are demonstrated, but critical requirements, including classification accuracy, large-scale data handling, and realistic long-distance deployment, are partly met.
A development roadmap is provided outlining the required advances in quantum memories, photon sources, processors, and quantum interconnects.
