I’m Alessandro (Scinawa). I am a researcher in quantum algorithms at CQT (Centre for Quantum Technologies). I’ve done a PhD IRIF (Institut de Recherche en Informatique Fondamentale) and PCQC (Paris Center for Quantum Computation). I also work in the R&D of a company as quantum software developer.
In another life, I was a contractor for some nation-wide projects for 3 ICT companies. I’ve also been an expert witness (consulente tecnico di parte) for 2 court cases. For more details please see Linkedin, and Scholar.
I am a founding member of Inclusive Hacker Framework. A decade ago I started to be involved in the vivid italian hackers’ community, where sometimes I contribute with talks. I am also a fellow of Hermes Center for digital human rights, and support(ed) groups like Progetto Winston Smith and Metro Olografix.
I served on the Local Committee of: FOCS2018.
My research interests are centered around quantum algorithms and machine learning: I try to write new machine learning algorithms for quantum computers. Along the way I also enjoy topics like the information bottleneck, learning theory, quantum algorithms, and anything that is quantum. I study how to apply quantum algorithms to solve real problems in other domains, like cybersecurity and artificial intelligence, and finance.
My master’ thesis was on graph centrality measures. I worked on the 4th of the Axioms for centrality: a set of necessary conditions that a centrality measure should satisfy in order to be considered “good”, and behave as expected for any graph.
The level of achievement that you have in anything, is a reflection of how well you were able to focus on it. (Steve Vai) Data rights are human rights.
Luongo, A., Bellante, A. (to appear). Quantum algorithms for data representation.
Luongo, A. (to appear). Applications of quantum algorithms for spectral sums.
Luongo, A. (thesis). Quantum algorithms for machine learning.
Luongo, A., Shao C. (2020). Quantum algorithms for spectral sums. [pdf].
Kerenidis, I., Luongo, A., & Prakash, A. (2019). Quantum Expectation-Maximization for Gaussian mixture models. International Conference in Machine Learning 2020 [pdf].
Kerenidis, I., Landman, J., Luongo, A., & Prakash, A. (2019). q-means: A quantum algorithm for unsupervised machine learning. In Advances in Neural Information Processing Systems 2019 [pdf].
Kerenidis, I., & Luongo, A. (2020). Classification of the MNIST data set with quantum slow feature analysis. Physical Review A [pdf].
Boldi, P., Luongo, A., & Vigna, S. (2017). Rank monotonicity in centrality measures. Network Science. [pdf].
Di facili costumi ma di sani principi.