23 citations · 92 across the 10 of their papers we have counts for
11 papers
Practical advantage beyond the quadratic speedup limit with fully-quantum walks
Massimiliano Incudini, Guglielmo Mazzola
We introduce a new class of fully-quantum Metropolis walks in which both the proposal and acceptance steps are intrinsically quantum. Unlike standard quantum walks obtained by quan…
Hybrid Method of Efficient Simulation of Physics Applications for a Quantum Computer
Carla Rieger, Albert T. Schmitz, Gehad Salem +5
Quantum chemistry and materials science are among the most promising areas for demonstrating algorithmic quantum advantage and quantum utility due to their inherent quantum mechani…
Testing the presence of balanced and bipartite components in a sparse graph is QMA1-hard
Massimiliano Incudini, Casper Gyurik, Riccardo Molteni +1
Determining whether an abstract simplicial complex, a discrete object often approximating a manifold, contains multi-dimensional holes is a task deeply connected to quantum mechani…
Higher-order topological kernels via quantum computation
Massimiliano Incudini, Francesco Martini, Alessandra Di Pierro
Topological data analysis (TDA) has emerged as a powerful tool for extracting meaningful insights from complex data. TDA enhances the analysis of objects by embedding them into a s…
Amplitude-assisted tagging of longitudinally polarised bosons using wide neural networks
Michele Grossi, Massimiliano Incudini, Mathieu Pellen +1
Extracting longitudinal modes of weak bosons in LHC processes is essential to understand the electroweak-symmetry-breaking mechanism. To that end, we propose a general method, base…
Resource Saving via Ensemble Techniques for Quantum Neural Networks
Massimiliano Incudini, Michele Grossi, Andrea Ceschini +4
Quantum neural networks hold significant promise for numerous applications, particularly as they can be executed on the current generation of quantum hardware. However, due to limi…
The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning
Massimiliano Incudini, Michele Grossi, Antonio Mandarino +3
Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. A key issue is how to address the inherent non-linearity of cla…
Automatic and effective discovery of quantum kernels
Massimiliano Incudini, Daniele Lizzio Bosco, Francesco Martini +3
Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data. Quantum kernels are…
Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning
Francesco Di Marcantonio, Massimiliano Incudini, Davide Tezza +1
Exploiting the properties of quantum information to the benefit of machine learning models is perhaps the most active field of research in quantum computation. This interest has su…
Computing Graph Edit Distance with Algorithms on Quantum Devices
Massimiliano Incudini, Fabio Tarocco, Riccardo Mengoni +2
Distance measures provide the foundation for many popular algorithms in Machine Learning and Pattern Recognition. Different notions of distance can be used depending on the types o…
Facial Expression Recognition on a Quantum Computer
Riccardo Mengoni, Massimiliano Incudini, Alessandra Di Pierro
We address the problem of facial expression recognition and show a possible solution using a quantum machine learning approach. In order to define an efficient classifier for a giv…