arXiv subjects

Marco Russo

Publications and source records attributed to Marco Russo.

7 recordsLinked to original sources

arXiv2026

Deploying and validating a metropolitan QKD secure network: architecture and field performance

The advent of cryptographically relevant quantum computers poses an existential threat to classical public-key infrastructure. Quantum Key Distribution (QKD) addresses this challenge by providing information-theoretic security for key establishment, independently of any computational hardness assumption. In this work, the deployment and experimental validation of a metropolitan-scale quantum-secure network between data centers in Milan is reported. The network operates over installed fiber infrastructure and implements a layered architecture integrating QKD hardware, standards-compliant Key Management (KM), and centralized Software-Defined Networking (SDN) orchestration. Dynamic path reconfiguration via active optical switching and trusted-node routing allow automated fail-over solutions. Application-layer validation across diverse protocols and workloads confirms the seamless interoperability of all system components. These results establish the technical and operational readiness of metropolitan QKD networks for production deployment, and offer a replicable blueprint for building quantum-secure communication infrastructure at metropolitan scale.

quant-ph↗

arXiv2025

Simulating single-photon experiments with a quantum computer

In this work, we simulate the behavior of photons in a laboratory experiment using a quantum computer and examine how the simulation results compare with the theoretical predictions. The experiment involves both protective and non-protective measurements. While the latter involves complete wavefunction collapse, the former combines weak interactions with a protective mechanism thereby preserving the photon wave function coherence until its final detection. The simulation gives insights as to how efficient quantum computers can be in simulating actual physical systems as the amount of computation increases.

quant-ph↗

arXiv2024

Machine Learning for Arbitrary Single-Qubit Rotations on an Embedded Device

Here we present a technique for using machine learning (ML) for single-qubit gate synthesis on field programmable logic for a superconducting transmon-based quantum computer based on simulated studies. Our approach is multi-stage. We first bootstrap a model based on simulation with access to the full statevector for measuring gate fidelity. We next present an algorithm, named adapted randomized benchmarking (ARB), for fine-tuning the gate on hardware based on measurements of the devices. We also present techniques for deploying the model on programmable devices with care to reduce the required resources. While the techniques here are applied to a transmon-based computer, many of them are portable to other architectures.

quant-ph↗

arXiv2024

European Quantum Ecosystems -- Preparing the Industry for the Quantum Security and Communications Revolution

There is mounting evidence that a second quantum revolution based on the technological capabilities to detect and manipulate single quantum particles (e.g., electrons, photons, ions, etc), a feat not achieved during the first quantum revolution, is progressing fast. It is expected that in less than 10 years, this second quantum revolution shall have a significant impact over numerous industries, including finance, medicine, energy, transportation, etc. Quantum computers threaten the status quo of cybersecurity, due to known quantum algorithms that can break asymmetric encryption, which is what gives us the ability to communicate securely using a public channel. Considering the world's dependence on digital communication through data exchange and processing, retaining the ability to communicate securely even once quantum computers come into play, cannot be stressed enough. Two solutions are available: Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC); which, we emphasise, are not mutually exclusive. The EuroQCI initiative, of which EQUO is a part of, focuses on QKD and aims to build a network whereby EU countries can communicate securely through QKD. To this aim, the DEP (Digital Europe Programme) project aims to bring technological matureness to QKD by deploying a QKD test network and, through this exercise, understand what is lacking from an operator's point of view when the time to integrate QKD in their network comes.

quant-ph↗

arXiv2023

Quantum Kernel Estimation With Neutral Atoms For Supervised Classification: A Gate-Based Approach

Quantum Kernel Estimation (QKE) is a technique based on leveraging a quantum computer to estimate a kernel function that is classically difficult to calculate, which is then used by a classical computer for training a Support Vector Machine (SVM). Given the high number of 2-local operators necessary for realizing a feature mapping hard to simulate classically, a high qubit connectivity is needed, which is not currently possible on superconducting devices. For this reason, neutral atom quantum computers can be used, since they allow to arrange the atoms with more freedom. Examples of neutral-atom-based QKE can be found in the literature, but they are focused on graph learning and use the analogue approach. In this paper, a general method based on the gate model is presented. After deriving 1-qubit and 2-qubit gates starting from laser pulses, a parameterized sequence for feature mapping on 3 qubits is realized. This sequence is then used to empirically compute the kernel matrix starting from a dataset, which is finally used to train the SVM. It is also shown that this process can be generalized up to N qubits taking advantage of the more flexible arrangement of atoms that this technology allows. The accuracy is shown to be high despite the small dataset and the low separation. This is the first paper that not only proposes an algorithm for explicitly deriving a universal set of gates but also presents a method of estimating quantum kernels on neutral atom devices for general problems using the gate model.

quant-ph↗

arXiv2022

A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals

Advanced LIGO and Advanced Virgo ground-based interferometers are instruments capable to detect gravitational wave signals exploiting advanced laser interferometry techniques. The underlying data analysis task consists in identifying specific patterns in noisy timeseries, but it is made extremely complex by the incredibly small amplitude of the target signals. In this scenario, the development of effective gravitational wave detection algorithms is crucial. We propose a novel layered framework for real-time detection of gravitational waves inspired by speech processing techniques and, in the present implementation, based on a state-of-the-art machine learning approach involving a hybridization of genetic programming and neural networks. The key aspects of the newly proposed framework are: the well structured, layered approach, and the low computational complexity. The paper describes the basic concepts of the framework and the derivation of the first three layers. Even if the layers are based on models derived using a machine learning approach, the proposed layered structure has a universal nature. Compared to more complex approaches, such as convolutional neural networks, which comprise a parameter set of several tens of MB and were tested exclusively for fixed length data samples, our framework has lower accuracy (e.g., it identifies 45% of low signal-to-noise-ration gravitational wave signals, against 65% of the state-of-the-art, at a false alarm probability of $10^{-2}$), but has a much lower computational complexity and a higher degree of modularity. Furthermore, the exploitation of short-term features makes the results of the new framework virtually independent against time-position of gravitational wave signals, simplifying its future exploitation in real-time multi-layer pipelines for gravitational-wave detection with new generation interferometers.

gr-qc↗

arXiv2022

Modeling Heavy-Ion Fusion Cross Section Data via a Novel Artificial Intelligence Approach

We perform a comprehensive analysis of complete fusion cross section data with the aim to derive, in a completely data-driven way, a model suitable to predict the integrated cross section of the fusion between light to medium mass nuclei at above barrier energies. To this end, we adopted a novel artificial intelligence approach, based on a hybridization of genetic programming and artificial neural networks, capable to derive an analytical model for the description of experimental data. The approach enables, for the first time, to perform a global search for computationally simple models over several variables and a considerable body of nuclear data. The derived phenomenological formula can serve to reproduce the trend of fusion cross section for a large variety of light to intermediate mass collision systems in an energy domain ranging approximately from the Coulomb barrier to the onset of multi-fragmentation phenomena.

nucl-ex↗