1 citations · 1 across the 9 of their papers we have counts for
9 papers
MPStab: an hybrid stabilizers tensor-network quantum circuit simulator
Giulio Crognaletti, Mattia Robbiano, Michele Grossi +1
The development of techniques for simulating quantum systems using classical computers is a paramount task for two primary reasons: i) there exist configurations for which classica…
Universal Optimization and Tighter Fidelity Bounds for Approximate Quantum Error Correction
Jing Wu, Michele Grossi, Doga Kurkcuoglu +1
Approximate quantum error correction (AQEC) extends the framework of discrete- and continuous-variable quantum error correction beyond the Knill-Laflamme (KL) conditions, where the…
Quantum Fourier Generative Models Trainable at Large Scale
Cenk Tüysüz, Oleksandr Kyriienko, Michele Grossi
We propose an algorithmic framework for building and training quantum generative models corresponding to multivariate probability distributions. Our model uses parallel Fourier fea…
Learning partial transpose signatures in qubit ququart states from a few measurements
Christian Candeago, Paolo Da Rold, Michele Grossi +2
Higher-dimensional quantum systems are attracting interest for improving quantum protocol performance by increasing memory space. Characterizing quantum resources of such systems i…
Sample-based training of quantum generative models
Maria Demidik, Cenk Tüysüz, Michele Grossi +1
Quantum computers can efficiently sample from probability distributions that are believed to be classically intractable, providing a foundation for quantum generative modeling. How…
Qiboml: towards the orchestration of quantum-classical machine learning
Matteo Robbiati, Andrea Papaluca, Andrea Pasquale +12
We present Qiboml, an open-source software library for orchestrating quantum and classical components in hybrid machine learning workflows. Building on Qibo's quantum computing cap…
Quantum Advantage in Learning Quantum Dynamics via Fourier coefficient extraction
Alice Barthe, Mahtab Yaghubi Rad, Michele Grossi +1
One of the key challenges in quantum machine learning is finding relevant machine learning tasks with a provable quantum advantage. A natural candidate for this is learning unknown…
Expressive equivalence of classical and quantum restricted Boltzmann machines
Maria Demidik, Cenk Tüysüz, Nico Piatkowski +2
Quantum computers offer the potential for efficiently sampling from complex probability distributions, attracting increasing interest in generative modeling within quantum machine…
Estimates of loss function concentration in noisy parametrized quantum circuits
Giulio Crognaletti, Michele Grossi, Angelo Bassi
Variational quantum computing offers a powerful framework with applications across diverse fields such as quantum chemistry, machine learning, and optimization. However, its scalab…