Keywords
AI, Algorithms, Quantum, Constraint Programming

Technologies
Python, Qiskit, Netsquid

This project involves the development of a novel framework for splitting large Quantum AI Algorithms over a Distributed Quantum Network. Quantum hardware experiences many challenges including scaling in terms of the number of qubits (quantum bits) and  presence of noise. This project combines the benefits of both DQC (Distributed Quantum Computing) and Heterogenous Hardware. It uses a constraint based framework with entanglement aware cutting to split the different qubits involved in the algorithm across multiple Quantum Processing Units (QPUs). 

The framework was built from a series of performance tests done on a simulated quantum network benchmarked based on execution time, fidelity and resource usage. The framework allocates each qubit to a QPU while minimising inter-QPU communication and ensures efficient use of each QPU's strength. A full simulation to run the proposed solution is also included within this project The novel framework that was developed as part of this project now enables the execution of large and otherwise complicated AI algorithms using DQC combined with Heterogenous QPUs. This overcomes a major challenge observed in Quantum Computing.