arXiv subjects

Edoardo Giusto

Publications and source records attributed to Edoardo Giusto.

12 recordsLinked to original sources

arXiv2026

SQUIRO: A Framework for Security-Aware Quantum-Classical Scheduling on Kubernetes

Distributed infrastructure schedulers traditionally optimise capacity, locality, and cost, but provide limited support for security posture and emerging quantum-classical workloads. As hybrid quantum-classical computing becomes increasingly practical and post-quantum security requirements begin to affect infrastructure deployment, schedulers must jointly reason about heterogeneous compute resources, security constraints, and quantum backend characteristics. We present SQUIRO, a framework for security-aware quantum-classical scheduling based on a platform-independent Unified Scheduling Model (USM) and a six-step Scheduler Design Methodology (SDM) that together enable the derivation of concrete schedulers for Kubernetes, high-performance computing (HPC), and federated environments. The framework combines multidimensional security posture enforcement through hard feasibility constraints with residual-risk optimisation, and introduces a circuit-aware quantum backend selector that accounts for coherence margin, calibration freshness, queue pressure, and hardware capabilities through a forward-compatible colocation hierarchy. Evaluation on synthetic Kubernetes clusters shows that the security model enforces complete compliance for regulated workloads by construction, while global optimisation reduces infrastructure cost by up to 51% and energy consumption by up to 63% compared with greedy placement in underloaded scenarios, without compromising admission priorities. Additional experiments characterise the solve-time growth of the current CP-SAT formulation and show that circuit-aware backend selection systematically diverges from naive error-rate ranking under coherence- and queue-limited conditions.

cs.ET↗

arXiv2026

Neural-powered unit disk graph embedding: qubits connectivity for some QUBO problems

Graph embedding is a recurrent problem in quantum computing, for instance, quantum annealers need to solve a minor graph embedding in order to map a given Quadratic Unconstrained Binary Optimization (QUBO) problem onto their internal connectivity pattern. This work presents a novel approach to constrained unit disk graph embedding, which is encountered when trying to solve combinatorial optimization problems in QUBO form, using quantum hardware based on neutral Rydberg atoms. The qubits, physically represented by the atoms, are excited to the Rydberg state through laser pulses. Whenever qubits pairs are closer together than the blockade radius, entanglement can be reached, thus preventing entangled qubits to be simultaneously in the excited state. Hence, the blockade radius determines the adjacency pattern among qubits, corresponding to a unit disk configuration. Although it is straightforward to compute the adjacency pattern given the qubit coordinates, identifying a feasible unit disk arrangement that matches the desired QUBO matrix is, on the other hand, a much harder task. In the context of quantum optimization, this issue translates into the physical placement of the qubits in the 2D/3D register to match the machine's Ising-like Hamiltonian with the QUBO formulation of the optimization problems. The proposed solution exploits the power of neural networks to transform an initial embedding configuration, which does not match the quantum hardware requirements or does not account for the unit disk property, into a feasible embedding properly representing the target optimization problems. Experimental results show that this new approach overcomes in performance Gurobi solver.

quant-ph↗

arXiv2026

Harnessing a 256-qubit Neutral Atom Simulator for Graph Classification

Neutral atom platforms are analogue quantum simulators that offer the possibility to map graphs onto a 2D qubit register using programmable Rubidium atoms arrays, whose valence electrons' energy state is used as qubits, using optical tweezers. This makes it possible to implement algorithms for solving graph combinatorial optimization and Quantum Machine Learning (QML) tasks, such as graph classification. However, the restrictions of real hardware, as well as the very low number of publicly available machines, make such implementation non-trivial. In this work, we manage to compute the Quantum Evolution Kernel (QEK) to extract the features from graphs of the PROTEINS dataset using the 256-qubits Aquila platform (available through AWS) and then we apply classical Machine Learning (ML) techniques for the final classification. The method is benchmarked against classical kernels, resulting in slightly better performance, proving the effectiveness of the method, even in the case of a noisy quantum simulator.

quant-ph↗

arXiv2026

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks

Quantum machines are among the most promising technologies expected to provide significant improvements in the following years. However, bridging the gap between real-world applications and their implementation on quantum hardware is still a complicated task. One of the main challenges is to represent through qubits (i.e., the basic units of quantum information) the problems of interest. According to the specific technology underlying the quantum machine, it is necessary to implement a proper representation strategy, generally referred to as embedding. This paper introduces a neural-enhanced optimization framework to solve the constrained unit disk problem, which arises in the context of qubits positioning for neutral atoms-based quantum hardware. The proposed approach involves a modified autoencoder model, i.e., the Distances Encoder Network, and a custom loss, i.e., the Embedding Loss Function, respectively, to compute Euclidean distances and model the optimization constraints. The core idea behind this design relies on the capability of neural networks to approximate non-linear transformations to make the Distances Encoder Network learn the spatial transformation that maps initial non-feasible solutions of the constrained unit disk problem into feasible ones. The proposed approach outperforms classical solvers, given fixed comparable computation times, and paves the way to address other optimization problems through a similar strategy.

quant-ph↗

arXiv2026

BBQ-mIS: a parallel quantum algorithm for graph coloring problems

Among the limitations of current quantum machines, the qubits count represents one of the most critical challenges for porting reasonably large computational problems, such as those coming from real-world applications, to the scale of the quantum hardware. In this regard, one possibility is to decompose the problems at hand and exploit parallelism over multiple size-limited quantum resources. To this purpose, we designed a hybrid quantum-classical algorithm, i.e., BBQ-mIS, to solve graph coloring problems on Rydberg atoms quantum machines. The BBQ-mIS algorithm combines the natural representation of Maximum Independent Set (MIS) problems onto the machine Hamiltonian with a Branch&Bound (BB) approach to identify a proper graph coloring. In the proposed solution, the graph representation emerges from qubit interactions (qubits represent vertexes of the graph), and the coloring is then retrieved by iteratively assigning one color to a maximal set of independent vertexes of the graph, still minimizing the number of colors with the Branch&Bound approach. We emulated real quantum hardware onto an IBM Power9-based cluster, with 32 cores/node and 256 GB/node, and exploited an MPI-enhanced library to implement the parallelism for the BBQ-mIS algorithm. Considering this use case, we also identify some technical requirements and challenges for an effective HPC-QC integration. The results show that our problem decomposition is effective in terms of graph coloring solutions quality, and provide a reference for applying this methodology to other quantum technologies or applications.

quant-ph↗

arXiv2025

Productive Quantum Programming Needs Better Abstract Machines

An effective, accessible abstraction hierarchy has made using and programming computers possible for people across all disciplines. Establishing such a hierarchy for quantum programming is an outstanding challenge, especially due to a proliferation of different conventions and the rapid pace of innovation. One critical portion of the hierarchy is the abstract machine, the layer that separates a programmer's mental model of the hardware from its physical realization. Drawing on historical parallels in classical computing, we explain why having the "right" quantum abstract machine (QAM) is essential for making progress in the field and propose a novel framework for evaluating QAMs based on a set of desirable criteria. These criteria capture aspects of a QAM such as universality, compactness, expressiveness, and composability, which aid in the representation of quantum programs. By defining this framework we take steps toward defining an optimal QAM. We further apply our framework to survey the landscape of existing proposals, draw comparisons, and assess them based on our criteria. While these proposals share many common strengths, we find that each falls short of our ideal. Our framework and our findings set a direction for subsequent efforts to define a future QAM that is both straightforward to map to a variety of quantum computers, and provides a stable abstraction for quantum software development.

quant-ph↗

arXiv2024

Q-SCALE: Quantum computing-based Sensor Calibration for Advanced Learning and Efficiency

In a world burdened by air pollution, the integration of state-of-the-art sensor calibration techniques utilizing Quantum Computing (QC) and Machine Learning (ML) holds promise for enhancing the accuracy and efficiency of air quality monitoring systems in smart cities. This article investigates the process of calibrating inexpensive optical fine-dust sensors through advanced methodologies such as Deep Learning (DL) and Quantum Machine Learning (QML). The objective of the project is to compare four sophisticated algorithms from both the classical and quantum realms to discern their disparities and explore possible alternative approaches to improve the precision and dependability of particulate matter measurements in urban air quality surveillance. Classical Feed-Forward Neural Networks (FFNN) and Long Short-Term Memory (LSTM) models are evaluated against their quantum counterparts: Variational Quantum Regressors (VQR) and Quantum LSTM (QLSTM) circuits. Through meticulous testing, including hyperparameter optimization and cross-validation, the study assesses the potential of quantum models to refine calibration performance. Our analysis shows that: the FFNN model achieved superior calibration accuracy on the test set compared to the VQR model in terms of lower L1 loss function (2.92 vs 4.81); the QLSTM slightly outperformed the LSTM model (loss on the test set: 2.70 vs 2.77), despite using fewer trainable weights (66 vs 482).

cs.LG↗

arXiv2024

Dependable Classical-Quantum Computer Systems Engineering

Quantum Computing (QC) offers the potential to enhance traditional High-Performance Computing (HPC) workloads by leveraging the unique properties of quantum computers, leading to the emergence of a new paradigm: HPC-QC. While this integration presents new opportunities, it also brings novel challenges, particularly in ensuring the dependability of such hybrid systems. This paper aims to identify integration challenges, anticipate failures, and foster a diverse co-design for HPC-QC systems by bringing together QC, cloud computing, HPC, and network security. The focus of this emerging inter-disciplinary effort is to develop engineering principles that ensure the dependability of hybrid systems, aiming for a more prescriptive co-design cycle. Our framework will help to prevent design pitfalls and accelerate the maturation of the QC technology ecosystem. Key aspects include building resilient HPC-QC systems, analyzing the applicability of conventional techniques to the quantum domain, and exploring the complexity of scaling in such hybrid systems. This underscores the need for performance-reliability metrics specific to this new computational paradigm.

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

Towards practical Quantum Credit Risk Analysis

In recent years, a CRA (Credit Risk Analysis) quantum algorithm with a quadratic speedup over classical analogous methods has been introduced. We propose a new variant of this quantum algorithm with the intent of overcoming some of the most significant limitations (according to business domain experts) of this approach. In particular, we describe a method to implement a more realistic and complex risk model for the default probability of each portfolio's asset, capable of taking into account multiple systemic risk factors. In addition, we present a solution to increase the flexibility of one of the model's inputs, the Loss Given Default, removing the constraint to use integer values. This specific improvement addresses the need to use real data coming from the financial sector in order to establish fair benchmarking protocols. Although these enhancements come at a cost in terms of circuit depth and width, they nevertheless show a path towards a more realistic software solution. Recent progress in quantum technology shows that eventually, the increase in the number and reliability of qubits will allow for useful results and meaningful scales for the financial sector, also on real quantum hardware, paving the way for a concrete quantum advantage in the field. The paper also describes experiments conducted on simulators to test the circuit proposed and contains an assessment of the scalability of the approach presented.

cs.ET↗

arXiv2022

QuFI: a Quantum Fault Injector to Measure the Reliability of Qubits and Quantum Circuits

Quantum computing is a new technology that is expected to revolutionize the computation paradigm in the next few years. Qubits exploit the quantum physics proprieties to increase the parallelism and speed of computation. Unfortunately, besides being intrinsically noisy, qubits have also been shown to be highly susceptible to external sources of faults, such as ionizing radiation. The latest discoveries highlight a much higher radiation sensitivity of qubits than traditional transistors and identify a much more complex fault model than bit-flip. We propose a framework to identify the quantum circuits sensitivity to radiation-induced faults and the probability for a fault in a qubit to propagate to the output. Based on the latest studies and radiation experiments performed on real quantum machines, we model the transient faults in a qubit as a phase shift with a parametrized magnitude. Additionally, our framework can inject multiple qubit faults, tuning the phase shift magnitude based on the proximity of the qubit to the particle strike location. As we show in the paper, the proposed fault injector is highly flexible, and it can be used on both quantum circuit simulators and real quantum machines. We report the finding of more than 285M injections on the Qiskit simulator and 53K injections on real IBM machines. We consider three quantum algorithms and identify the faults and qubits that are more likely to impact the output. We also consider the fault propagation dependence on the circuit scale, showing that the reliability profile for some quantum algorithms is scale-dependent, with increased impact from radiation-induced faults as we increase the number of qubits. Finally, we also consider multi qubits faults, showing that they are much more critical than single faults. The fault injector and the data presented in this paper are available in a public repository to allow further analysis.

cs.ET↗

arXiv2021

A Systematic Methodology to Compute the Quantum Vulnerability Factors for Quantum Circuits

Quantum computing is one of the most promising technology advances of the latest years. Once only a conceptual idea to solve physics simulations, quantum computation is today a reality, with numerous machines able to execute quantum algorithms. One of the hardest challenges in quantum computing is reliability. Qubits are highly sensitive to noise, which can make the output useless. Moreover, lately it has been shown that superconducting qubits are extremely susceptible to external sources of faults, such as ionizing radiation. When adopted in large scale, radiation-induced errors are expected to become a serious challenge for qubits reliability. In this paper, we propose an evaluation of the impact of transient faults in the execution of quantum circuits. Inspired by the Architectural and Program Vulnerability Factors, widely adopted to characterize the reliability of classical computing architectures and algorithms, we propose the Quantum Vulnerability Factor (QVF) as a metric to measure the impact that the corruption of a qubit has on the circuit output probability distribution. First, we model faults based on the latest studies on real machines and recently performed radiation experiments. Then, we design a quantum fault injector, built over Qiskit, and characterize the propagation of faults in quantum circuits. We report the finding of more than 15,000,000 fault injections, evaluating the reliability of three quantum circuits and identifying the faults and qubits that are more likely than others to impact the output. With our results, we give guidelines on how to map the qubits in the real quantum computer to reduce the output error and to reduce the probability of having a radiation-induced corruption to modify the output. Finally, we compare the simulation results with experiments on physical quantum computers.

cs.ET↗