8 citations · 11 across the 11 of their papers we have counts for
11 papers
Approximate Quantum State Preparation Through Proximal Policy Optimization
Marco Mordacci, Michele Amoretti
In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed. QSP is a challenging task, since the search space grows exponenti…
Parallel QEC Decoding Applied to Distributed Quantum Computing
Gabriele Incardona, Davide Ferrari, Michele Amoretti
A novel parallel approach is proposed for QEC decoding based on Belief Propagation with Ordered Statistics Decoding. The main idea is to pre-process the error vectors obtained from…
Algebraic Reduction to Improve an Optimally Bounded Quantum State Preparation Algorithm
Giacomo Belli, Michele Amoretti
The preparation of -qubit quantum states is a cross-cutting subroutine for many quantum algorithms, and the effort to reduce its circuit complexity is a significant challenge. I…
Advanced Scheduling Strategies for Distributed Quantum Computing Jobs
Gongyu Ni, Davide Ferrari, Lester Ho +1
Distributed quantum computing (DQC) is being actively investigated as a means of scaling the number of qubits across multiple connected quantum devices. This includes quantum circu…
Training Variational Quantum Circuits Using Particle Swarm Optimization
Marco Mordacci, Michele Amoretti
In this work, the Particle Swarm Optimization (PSO) algorithm has been used to train various Variational Quantum Circuits (VQCs). This approach is motivated by the fact that common…
Impact of Single Rotations and Entanglement Topologies in Quantum Neural Networks
Marco Mordacci, Michele Amoretti
In this work, an analysis of the performance of different Variational Quantum Circuits is presented, investigating how it changes with respect to entanglement topology, adopted gat…
Triplet Loss Based Quantum Encoding for Class Separability
Marco Mordacci, Mahul Pandey, Paolo Santini +1
An efficient and data-driven encoding scheme is proposed to enhance the performance of variational quantum classifiers. This encoding is specially designed for complex datasets lik…
Quantum State Preparation with the QNN-based SRBB Algorithm
Giacomo Belli, Marco Mordacci, Michele Amoretti
In this work, a novel algorithm structured on Lie algebras for the approximate quantum state preparation problem is proposed, addressing a challenge of fundamental importance in ma…
A Novel Single-Layer Quantum Neural Network for Approximate SRBB-Based Unitary Synthesis
Giacomo Belli, Marco Mordacci, Michele Amoretti
In this work, a novel quantum neural network is introduced as a means to approximate any unitary evolution through the Standard Recursive Block Basis (SRBB) and is subsequently red…
Multi-Class Quantum Convolutional Neural Networks
Marco Mordacci, Davide Ferrari, Michele Amoretti
Classification is particularly relevant to Information Retrieval, as it is used in various subtasks of the search pipeline. In this work, we propose a quantum convolutional neural…
Evaluation of Quantum and Hybrid Solvers for Combinatorial Optimization
Amedeo Bertuzzi, Davide Ferrari, Antonio Manzalini +1
Academic and industrial sectors have been engaged in a fierce competition to develop quantum technologies, fueled by the explosive advancements in quantum hardware. While universal…