PhD Defense: Distributed Quantum Computing integrated into High-Performance Computing Environments

This thesis explores the integration of distributed quantum computing into high-performance computing (HPC) environments, addressing the current challenges and opportunities offered by this emerging paradigm. Despite recent advancements in quantum computing, there are still significant limitations, such as high levels of error and noise in existing systems, as well as limited scalability in the number of qubits per chip, the fundamental unit of quantum information analogous to the classical bit.

To overcome these hardware bottlenecks, distributed quantum computing proposes linking multiple smaller quantum processors via quantum communication networks. By sharing entanglement and distributing quantum states across various nodes, these interconnected systems can function as a single, highly scalable quantum machine.

Furthermore, due to the probabilistic nature of quantum computing, there are many areas where classical systems outperform quantum ones, particularly in terms of stability and precision. Therefore, just as GPUs and FPGAs have been integrated as heterogeneous devices in HPC systems, this thesis explores the use of quantum processing units (QPUs) as another specialized resource within these environments. The technical implications of this integration are analyzed, and software solutions are developed to incorporate QPUs into the most widely used frameworks in the field.

Potential applications of this integration include molecular simulation, advanced optimization, quantum cryptography, and machine learning, where quantum computing can offer significant improvements when combined efficiently and complementarily with classical resources.

Supervisor: Tomás Fernández Pena