collaborators

5 papers

quant-ph2026

Entanglement geometry separates circuit cutting, classical hardness, and trainability

Maria Gragera Garces, Sabina Drăgoi, Lirandë Pira

Circuit cutting promises to scale quantum computations beyond current hardware, but variational quantum advantage also requires low cutting overhead, classical hardness, and traina…

quant-ph2026

Quantum ring all-reduce: communication and privacy advantages for distributed learning

María Gragera Garcés, Lirandë Pira

Machine learning models have scaled to unprecedented sizes, making training across distributed devices the de facto standard in the field. In this work, we explore how quantum comm…

quant-ph2026

Distributed Quantum Circuit Optimisation: Evaluating Global and Local encodings

Maria Gragera Garces, Majid Haghparast

As distributed quantum architectures begin to emerge, understanding the interaction between quantum circuit optimisation and circuit partitioning becomes increasingly important. In…

quant-ph2026

On the Distortion of Partitioning Performance by Random Quantum Circuits

Maria Gragera Garces

Hypergraph partitioning is a central component of distributed quantum computing (DQC) compilers. However, due to the limited size of available quantum benchmark suites, many partit…

quant-ph2026

Distributed Quantum Error Mitigation: Global and Local ZNE encodings

Maria Gragera Garces

Errors are the primary bottleneck preventing practical quantum computing. This challenge is exacerbated in the distributed quantum computing regime, where quantum networks introduc…