Near-term quantum algorithms

The most popular quantumm algorithms, such as Grover search or Shor’s factorization, have demonstrated the potential for large speedups, but the required quantum resources are not immediately available on near-term quantum devices. Te presence of noise in preparation, gate application and readout, the small number of qubit and the limited connectivity require us to develop algorithms that are optimized for specific platforms and that squeeze all the available resources from small quantum processors.

In my research I focus in particular on the readout part of quantum simulation algorithms, developing methods to minimize the number of repetition of the quantum circuits by employing generalized quantum measurements.

Some relevant publications

• Anton Nykänen, Matteo A. C. Rossi, Elsi-Mari Borrelli, Sabrina Maniscalco, Guillermo García-Pérez (2022). Mitigating the measurement overhead of ADAPT-VQE with optimised informationally complete generalised measurements.

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• Guillermo García-Pérez, Matteo A. C. Rossi, Boris Sokolov, Francesco Tacchino, Panagiotis Kl. Barkoutsos, Guglielmo Mazzola, Ivano Tavernelli, Sabrina Maniscalco (2021). Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms. PRX Quantum, 2, 040342.

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• Guillermo García-Pérez, Oskari Kerppo, Matteo A. C. Rossi, Sabrina Maniscalco (2023). Experimentally accessible non-separability criteria for multipartite entanglement structure detection. Phys. Rev. Res. 5, 013226.

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