Publications

Here you can find a selection of publications. A complete list with all my scientific contributions can be found here with corresponding BibTeX entries here.

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[C10] Semi-Supervised Multichannel Anomaly Detection Using QAOA-Driven Selection and Set Covering

Published in IJCNN, 2026

In this paper we present a hybrid quantum–classical method for detecting anomalies in multivariate time serie extending our previous approach to handle multiple correlated channels. Read more

Recommended citation: M. Casalbore, L. Lavagna, A. Rosato, F. Cuomo and M. Panella, "Semi-Supervised Multichannel Anomaly Detection Using QAOA-Driven Selection and Set Covering," 2026 International Joint Conference on Neural Networks (IJCNN), Maastricht, Netherlands, 2026, pp. 1-6 https://ieeexplore.ieee.org/document/11729523

[J4] From Problem-Structured QAOA to Problem-Agnostic Variational MaxCut Solvers: Representation, Redundancy, and Optimization Landscapes

Published in Applied Mathematics and Statistics, 2026

In this paper we investigate how circuit architecture, parameterization, and optimization landscapes influence the performance of the Quantum Approximate Optimization Algorithm on The MaxCut problem Read more

Recommended citation: Lavagna, L.; Panella, M. "From Problem-Structured QAOA to Problem-Agnostic Variational MaxCut Solvers: Representation, Redundancy, and Optimization Landscapes". Applied Mathematics and Statistics 2026, 3 (2), 21. https://www.sciltp.com/journals/ams/articles/2610005354

[C9] Additive Resource Scaling in Quantum Circuits for Search in Cryptanalysis

Published in ISCAS, 2026

In this paper we show that non-isolated quantum circuits for cryptographic applications under attacks that leverage both quantum algorithms and quantum channels can exhibit an additive time-space trade-off, instead of standard multiplicative scaling Read more

Recommended citation: L. Lavagna, G. Vittori, A. Rosato and M. Panella, "Additive Resource Scaling in Quantum Circuits for Search in Cryptanalysis," 2026 IEEE International Symposium on Circuits and Systems (ISCAS), Shanghai, China, 2026, pp. 2758-2762 https://ieeexplore.ieee.org/document/11562218

[C8] A Unified Quantum Circuit Framework for Grover Search and Quantum Walks in NISQ Cryptanalysis

Published in ISCAS, 2026

This paper explores the use of quantum search in symmetric cryptanalysis within the Noisy Intermediate-Scale Quantum (NISQ) regime, introducing a unified quantum circuit framework that expresses Grover’s algorithm as a coined quantum walk. Read more

Recommended citation: L. Lavagna, G. Vittori, A. Rosato and M. Panella, "A Unified Quantum Circuit Framework for Grover Search and Quantum Walks in NISQ Cryptanalysis," 2026 IEEE International Symposium on Circuits and Systems (ISCAS), Shanghai, China, 2026, pp. 837-841 https://ieeexplore.ieee.org/document/11562366

[C7] Time Series Anomaly Detection with Quantum Variational Methods and Set Covering

Published in ICASSP, 2026

We propose an enhanced hybrid quantum-classical framework for time series anomaly detection. Building on our previous formulation of the problem as a Quadratic Unconstrained Binary Optimization solved via the Quantum Approximate Optimization Algorithm, we extend the methodology with a statistical model selector and a refined set-covering inference scheme that accounts for temporal-value asymmetries. Read more

Recommended citation: M. Casalbore, L. Lavagna, A. Rosato and M. Panella, "Time Series Anomaly Detection with Quantum Variational Methods and Set Covering," ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2026 https://ieeexplore.ieee.org/document/11464824

[C6] Enhancing QAOA Ansatz via Multi-Parameterized Layer and Blockwise Optimization

Published in ICASSP, 2026

We propose Enhanced QAOA (EQAOA), a modified ansatz that appends a compact multi-parameter layer and uses a blockwise optimization scheme. This design preserves the QAOA evolution while broadening the candidate solution space and easing solution exploration. Read more

Recommended citation: F. De Falco, L. Lavagna, A. Ceschini, and M. Panella, "Enhancing QAOA Ansatz via Multi-Parameterized Layer and Blockwise Optimization" In Proceedings of the 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2026). Barcelona, Spain, 2026, pp. 20417-20421, https://ieeexplore.ieee.org/document/11461345

[O1] Smart Microfluidics: a curated dataset of microfluidic liposome formulations with cross-laboratory validation for machine-learning applications

Published in Data in Brief, 2026

This dataset compiles microfluidic liposome production experiments from two laboratories, linking formulation parameters and operating conditions with vesicle size and polydispersity measurements. After a standardized Python-based preprocessing workflow, the harmonized dataset (304 formulations plus 12 validation runs) supports machine-learning benchmarking and evaluation of cross-laboratory reproducibility. Code available here. Read more

Recommended citation: L. Lavagna, G. Buttitta, S. Bonacorsi, C. Barbarito, M. Moliterno, G. Saito, I. Oddone, G. Verdone, S. Raimondi, and M. Panella, "Smart Microfluidics: a curated dataset of microfluidic liposome formulations with cross-laboratory validation for machine-learning applications," Data in Brief, 2026, 112667. https://www.sciencedirect.com/science/article/pii/S2352340926002209

[C5] Hybrid Quantum-Classical Framework for Anomaly Detection in Time Series with QUBO formulation and QAOA

Published in IJCNN, 2025

We introduce a hybrid quantum-classical framework to address anomaly detection problems in time series data using an innovative Quadratic Unconstrained Binary Optimization formulation. Read more

Recommended citation: M. Casalbore, L. Lavagna, A. Rosato and M. Panella, "Hybrid Quantum-Classical Framework for Anomaly Detection in Time Series with QUBO formulation and QAOA," 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 2025, pp. 1-8. https://ieeexplore.ieee.org/document/11228152

[C4] Novel Quantum Approaches to Hyperdimensional Computing for Neural Networks

Published in IJCNN, 2025

We introduce new quantum machine learning models that combine, within a single framework, both quantum and hyperdimensional computing. Read more

Recommended citation: L. Lavagna, A. Ceschini, A. Rosato and M. Panella, "Novel Quantum Approaches to Hyperdimensional Computing for Neural Networks," 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 2025, pp. 1-8. https://ieeexplore.ieee.org/document/11229083

[J3] Machine Learning-Guided microfluidic optimization of clinically inspired liposomes for nanomedicine applications

Published in International Journal of Pharmaceutics, 2025

This article discusses the transformative role of machine learning in optimizing liposome development, a process traditionally limited by its complexity, high cost, and time-intensive experimentation. By analyzing over 300 experimental conditions using a microfluidic production platform, the study demonstrates how predictive models can efficiently determine key liposome characteristics such as particle size and polydispersity index. An open-source simulation tool was also developed to allow researchers to virtually design and test formulations, reducing the need for extensive laboratory work. The models were validated through independent experiments, confirming their robustness and adaptability. Read more

Recommended citation: G. Buttitta, L. Lavagna, S. Bonacorsi, C. Barbarito, M. Moliterno, G. Saito, I. Oddone, G. Verdone, S. Raimondi, M. Panella, "Machine Learning-Guided microfluidic optimization of clinically inspired liposomes for nanomedicine applications", International Journal of Pharmaceutics, Vol. 686, 2025. https://www.sciencedirect.com/science/article/pii/S0378517325011998

[J2] On the Effects of Small Graph Perturbations in the MaxCut Problem by QAOA

Published in AVS Quantum Science, 2025

In this article we investigate the Maximum Cut (MaxCut) problem on different graph classes with the quantum approximate optimization algorithm (QAOA) using symmetries. In particular, heuristics on the relationship between graph symmetries and the approximation ratio achieved by a QAOA simulation are considered Read more

Recommended citation: L. Lavagna, S. Piperno, A. Ceschini and M. Panella, "On the Effects of Small Graph Perturbations in the MaxCut Problem by QAOA ," 2025 AVS Quantum Science 7(4), doi: 10.1116/5.0253160. https://www.researchgate.net/publication/396483291_Small_graph_perturbations_QAOA_and_the_MaxCut_problem

[J1] A topical review on time-independent perturbation theory in one-dimensional quantum systems

Published in Physica Scripta, 2025

This review presents time-independent perturbative methods for solving the one-dimensional Schrödinger equation, highlighting representative cases that reveal key aspects of the theory. The focus is on their relevance to quantum computing applications, particularly in systems with finite-dimensional state spaces. There is also an app in the Projects & Software page. Read more

Recommended citation: L. Lavagna, S. Carillo and M. Panella, "A topical review on time-independent perturbation theory in one-dimensional quantum systems," 2025 Phys. Scr. 100 102001. https://iopscience.iop.org/article/10.1088/1402-4896/ae0a8f

[C3] Trade-offs in Cryptosystems by Boolean and Quantum Circuits

Published in ISCAS, 2025

This paper explores the integration of quantum tools into classical cryptographic systems to assess their impact on the feasibility-efficiency trade-off in the context of Noisy Intermediate-Scale Quantum technology. By focusing on the hybridization of classical low-level primitives and high level cryptographic tools, the study investigates the effect of quantum contributions on encryption schemes corresponding to trapdoor permutations, revealing significant insights into the intersection of cryptography, quantum computing, and Boolean circuit theory. Read more

Recommended citation: L. Lavagna, F. De Falco, A. Ceschini, A. Rosato and M. Panella, "Trade-offs in Cryptosystems by Boolean and Quantum Circuits," 2025 IEEE International Symposium on Circuits and Systems (ISCAS), London, UK, pp. 1-5. https://ieeexplore.ieee.org/document/11043205

[C2] Evolving Hybrid Quantum-Classical GRU Architectures for Multivariate Time Series

Published in MLSP, 2024

This paper is defines and explores an hybrid quantum-classical Gated Recurrent Unit used to infer information about multidimensional time series. Read more

Recommended citation: Francesca De Falco, Leonardo Lavagna, Andrea Ceschini, Antonello Rosato, Massimo Panella: Evolving Hybrid Quantum-Classical GRU Architectures for Multivariate Time Series, in the 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP). https://inspirehep.net/literature/2849278

[C1] A Layerwise-Multi-Angle Approach to Fine-Tuning the Quantum Approximate Optimization Algorithm

Published in IJCNN, 2024

This work was the natural development of my master’s thesis on quantum optimization, where we investigated parameter fixing strategies for the Quantum Approximate Optimization Algorithm (QAOA) and its Multi-Angle extension. Read more

Recommended citation: L. Lavagna, A. Ceschini, A. Rosato and M. Panella, "A Layerwise-Multi-Angle Approach to Fine-Tuning the Quantum Approximate Optimization Algorithm," 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024, pp. 1-8. https://ieeexplore.ieee.org/document/10650075

[P1] On the Effects of Small Graph Perturbations in the MaxCut Problem by QAOA

Published in arXiv, 2024

We study the Quantum Approximate Optimization Algorithm (QAOA) to solve the MaxCut problem on graphs focusing on the relationship between graph symmetries, its perturbations, and the approximation ratio achieved by the QAOA. The main focus is to leverage symmetries in the graph to gain in approximation ratio, by studying simple graph perturbations in terms of the corresponding automorphism groups and spectral properties. Read more

Recommended citation: Leonardo Lavagna, Simone Piperno, Andrea Ceschini, Massimo Panella: On the Effects of Small Graph Perturbations in the MaxCut Problem by QAOA. Arxiv preprint 2408.15413 (2024). https://www.arxiv.org/abs/2408.15413