Leonardo Lavagna

Leonardo Lavagna

I'm a mathematician specialized in Data Science currently pursuing a PhD in Quantum Computing

Visiting period at CERN

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I’m thrilled to announce that I will spend a 3-month visiting period at CERN contributing to the quantum-algorithmic efforts related to the ongoing ATLAS experiment during the final period of LHC Run 3 in the Quantum Technology Initiative group. More details on this incredible experience will be coming soon!

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Enhancing QAOA Ansatz via Multi-Parameterized Layer and Blockwise Optimization

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The contribution about QAOA in the conference ICASSP 2026 (see this previous post) has been published here. I’ve also updated the blog, with a brief discussion abut the main ideas behind the paper. In particular, in this paper we focuse on multi-parameterized layers and advanced parameters’ update, in particular on the effect that an extra layer at the end of a standard QAOA circuit has, and on possible (blockwise) optimizations of the parameters of the extended circuit, including ablation studies. Concretely, applied to the MaxCut problem across diverse graph families, the proposed architecture achieves, with an order of magnitude smaller circuit depth, approximation ratios comparable to QAOA, using a single cost–mixer layer, thus reducing gate counts by up to fivefold. Moreover, ablation studies prove that blockwise fine-tuning is crucial to deliver higher-quality solutions at shallower depth, offering a practical, quantum hardware-efficient alternative for signal processing applications. Most of the code is an adaptation of this repository. Read more

Papers related to the 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing

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I’m happy to announce that we will share two contributions at the conference ICASSP 2026. The first contribution is a natural extension and enhancement of the Hybrid quantum-classical approach for anomaly detection in one-dimensional time series discussed here. The second contribution, instead, is focused on the QAOA and proposes a new variant of this quantum optimization algorithm where the standard QAOA ansatz is enhanced via a blockwise optimization strategy of its parameters and with the addition of a multi-parameterized layer before the measurements. Further details about this second contribution will be given in a subsequent announcement (I still have to update the blog!). If you are in Barcelona in the period 4-8 may we have our presentations already scheduled (see here and here), feel free to say hi to my collegues! Read more