arXiv subjects

Daniele Dragoni

Publications and source records attributed to Daniele Dragoni.

17 recordsLinked to original sources

arXiv2026

Parallel splitting method for large-scale quadratic programs

Current algorithms for large-scale industrial optimization problems typically face a trade-off: they either require exponential time to reach optimal solutions, or employ problem-specific heuristics. To address these limitations, we introduce SPLIT, a general-purpose quantum-inspired framework for decomposing large-scale quadratic programs into smaller subproblems, which are solved in parallel. SPLIT heuristically accounts for objective-function cross-interactions between subproblems, usually neglected in other decomposition techniques. The SPLIT framework can integrate generic subproblem solvers, from branch-and-bound to quantum optimization methods. We demonstrate its effectiveness through comparisons with commercial solvers and published results on MaxCut and Antenna Placement Problems, with up to 20,000 variables. Our results show that SPLIT is capable of providing drastic reductions in computational time, while delivering high-quality solutions. In these regards, the proposed method is well-suited for near real-time applications that require a solution within a strict time frame, or when the problem size exceeds hardware limitations of dedicated devices, such as current quantum computers.

quant-ph↗

arXiv2026

Combinatorial optimization of connected UAV communication bridges for emergency response

We present a combinatorial optimization problem for the strategic deployment of UAVs equipped with 5G antennas to assist rescue operations in regions hit by natural disasters. Our goal is to optimize the placement of UAVs to provide coverage in flying ad-hoc networks among given candidate sites. Our formulation aims to maximize signal coverage and minimize interference while ensuring network connectivity. To mitigate interference effects, we incorporate the use of multiple frequencies. We formulate this problem as an integer quadratic program (IQP). We present numerical solutions obtained via the CPLEX solver and conduct a preliminary analysis of the problem's scalability in realistic network configurations. Our findings reveal a significant exponential increase in Time-to-Solution (TTS) as the number of sites grows, which poses a critical challenge in urgent, time-sensitive scenarios. To address this issue, approximate suboptimal solutions can be produced by enforcing a time limit on the solver. Although these solutions are not optimal, they preserve connectivity in most cases, providing a practical trade-off between solution quality and computational times that remain within feasible limits for real-time UAV redeployment. Recognizing the limitations of classical solvers in these contexts, we explore quantum computing as a promising alternative. Specifically, we reformulate the problem as a quadratic unconstrained binary optimization (QUBO) problem, suitable for most quantum algorithms. Through high-performance computing emulation, we show that the quantum adiabatic algorithm (QAA) can accurately solve small-scale instances, paving the way for future application of quantum computing to large-scale, time-critical optimization problems in disaster response.

quant-ph↗

arXiv2026

Quantum Computing for Industrial Electromagnetics: Applicability and Case Studies in Solving Maxwell's Equations

Computational electromagnetics plays a central role in many industrial applications but often requires substantial computational resources, particularly when fine spatial discretizations are needed. While classical approaches remain the standard, quantum computing offers the potential to accelerate large-scale simulations by encoding them with a limited number of qubits. Here, we investigate the performance and resource scaling of the Harrow-Hassidim-Lloyd (HHL) and Quantum Singular Value Transformation (QSVT) algorithms for solving linear systems generated by the finite-difference time-domain (FDTD) method, a widely adopted numerical scheme for discretizing Maxwell's equations. We benchmark their performance across representative industrial use cases, including radar propagation, lens simulations, and beamforming processes. Our results demonstrate the validity of the approaches, achieving state infidelities smaller than $2\cdot 10^{-3}$ with success probabilities greater than $10^{-3}$, compatible with practical quantum state sampling. Overall, we observe that the QSVT method consistently delivers higher accuracy. We further observe that the condition number of the linear matrix, a key factor governing the performance of quantum solvers, saturates as the number of spatial lattice points increases. This implies that the spatial grid can be scaled to realistic industrial dimensions without increasing the HHL or QSVT circuit depth due to ill-conditioned matrices.

quant-ph↗

arXiv2026

A Resource-Efficient Quantum Framework for Graph Coloring and Chromatic Number Estimation

Many industrial optimization tasks can be modeled as graph coloring, where adjacent vertices must have different colors. This NP-hard problem is challenging for large graphs. We present a quantum encoding requiring qubits that scale logarithmically with the number of colors and linearly with vertices. Using adiabatic evolution with a novel mixer Hamiltonian and vertex terms, we compute the chromatic number and demonstrate robustness by solving constrained truck loading problems.

quant-ph↗

arXiv2026

Feasibility-driven QAOA with penalty scheduling

Most available quantum algorithms address constrained optimization problems by treating constraints as soft penalty terms within a QUBO formulation. This approach requires careful adjustment of the penalty coefficients, which scales poorly with the number of constraints and lacks a proper strategy to balance feasibility and solution quality. In this work, we introduce two extensions of standard linear-ramp QAOA (lr-QAOA) tailored to problems with multiple heterogeneous constraints. We first construct $Λ$-lr-QAOA, in which each penalty term is assigned its own linear-ramp schedule, promoting penalty weights from external hyperparameters to internal variational parameters of QAOA, similarly to the objective and mixer parameters. By optimizing all schedules jointly in a single run, this approach eliminates nested penalty tuning and scales more efficiently to multiple constraints. The optimization is guided by a feasibility-driven loss function that pushes the quantum state towards high-quality feasible solutions. As a further refinement, we introduce piecewise-ramp QAOA, in which the linear ramps are replaced by two-segment piecewise schedules, enhancing the expressiveness of the Ansatz at the cost of a small parameter overhead independent of the circuit depth. We benchmark both methods on Earth-observation satellite mission planning tasks formulated as budget-constrained Maximum Weight Independent Set problems. Numerical results show that piecewise-ramp QAOA consistently outperforms lr-QAOA and $Λ$-lr-QAOA across circuit depths and system sizes. Furthermore, both $Λ$-lr-QAOA and piecewise-ramp QAOA exhibit a high feasibility rate, which is crucial in industrial applications. Our analysis highlights an intrinsic feasibility-optimality trade-off, which we address by introducing a filtered variant of the loss providing a single hyperparameter to tune this balance.

quant-ph↗

arXiv2026

Emergency hub placement with a neutral-atom quantum computer

We study the problem of emergency operation center placement in disaster response, where a minimal number of hubs must be selected to ensure timely coverage of all affected locations. This task can be formulated as a minimum dominating set problem on a graph encoding reachability within a target response time. We propose a hybrid quantum-classical approximation framework that leverages neutral-atom quantum computers as independent set samplers. Candidate dominating sets are constructed from both small maximal independent sets and complements of large independent sets, and are subsequently refined via a lightweight classical procedure. We benchmark the approach on synthetic instances and realistic case studies, and implement it on the Fresnel quantum processor by Pasqal, solving instances of up to 100 nodes. Our results show that quantum-generated samples, despite hardware noise, enable near-optimal solutions of the placement problem. Overall, our results demonstrate that neutral-atom devices operating in analog mode can already be used to tackle graph optimization problems for real-world applications.

quant-ph↗

arXiv2026

High-expressibility Quantum Neural Networks using only classical resources

Quantum neural networks (QNNs), as currently formulated, are near-term quantum machine learning architectures that leverage parameterized quantum circuits with the aim of improving upon the performance of their classical counterparts. In this work, we show that some desired properties attributed to these models can be efficiently reproduced without necessarily resorting to quantum hardware. We indeed study the expressibility of parametrized quantum circuit commonly used in QNN applications and contrast it to those of two classes of states that can be efficiently simulated classically: matrix-product states (MPS), and Clifford-enhanced MPS (CMPS), obtained by applying a set of Clifford gates to MPS. In addition to expressibility, we assess the level of primary quantum resources, entanglement and non-stabilizerness (a.k.a. "magic"), in random ensembles of such quantum states, tracking their convergence towards the Haar distribution. While MPS require a large number of parameters to effectively reproduce an arbitrary quantum state, we find that CMPS approach the Haar distribution more rapidly, in terms of both entanglement and magic. Our results on states with up to 20 qubits indicate that high expressibility in QNNs is attainable with purely classical resources.

quant-ph↗

arXiv2026

Drone delivery packing problem on a neutral-atom quantum computer

Quantum architectures based on neutral atoms have gained significant attention in recent years as specialized computational machines due to their ability to directly encode the independent set constraint on graphs, exploiting the Rydberg blockade mechanism. In this work, we address the Drone Delivery Packing Problem via a hybrid quantum-classical framework leveraging a neutral-atom quantum processing unit (QPU). We reformulate the optimization task as a graph-partitioning problem based on the independent sets (ISs) of a scheduling graph that encodes delivery incompatibilities. Each partition corresponds to deliveries assigned to a single drone, with the objective of minimizing the total number of partitions. While the ISs represent time-feasible schedules, battery-duration constraints are enforced through a classical post-processing routine. This methodology enables the recovery of optimal delivery schedules, provided a sufficient number of samples is collected from the QPU to resolve the solution space. We benchmark the hybrid workflow through numerical emulations and demonstrate its effectiveness on Pasqal's Fresnel QPU, reporting hardware experiments with configurations of up to 100 atoms.

quant-ph↗

arXiv2026

Liquid anomalies and Fragility of Supercooled Antimony

Phase-change materials (PCMs) based on group IV, V, and VI elements, such as Ge, Sb, and Te, exhibit distinctive liquid-state features, including thermodynamic anomalies and unusual dynamical properties, which are believed to play a key role in their fast and reversible crystallization behavior. Antimony (Sb), a monoatomic PCM with ultrafast switching capabilities, stands out as the only elemental member of this group for which the properties of the liquid and supercooled states have so far remained unknown. In this work, we use large-scale molecular dynamics simulations with a neural network potential trained on first-principles data to investigate the liquid, supercooled, and amorphous phases of Sb across a broad pressure-temperature range. We uncover clear signatures of anomalous behavior, including a density maximum and non-monotonic thermodynamic response functions, and introduce a novel octahedral order parameter that captures the structural evolution of the liquid. Moreover, extrapolation of the viscosity to the glass transition, based on configurational and excess entropies, indicates that Sb is a highly fragile material. Our results present a compelling new case for the connection between the liquid-state properties of phase-change materials and their unique ability to combine high amorphous-phase stability with ultrafast crystallization.

cond-mat.mtrl-sci↗

arXiv2025

Constraint-preserving quantum algorithm for the multi-frequency antenna placement problem

Quantum algorithms for combinatorial optimization typically encode constraints as soft penalties within the objective function, which can reduce efficiency and scalability compared to state-of-the-art classical methods that instead exploit constraints to guide the search toward high-quality solutions. Although solving this issue for an arbitrary problem is inherently a hard task, we address this challenge for a specific problem in the field of telecommunications, the multi-frequency antenna placement problem, by introducing a constraint-preserving quantum adiabatic algorithm (QAA). To this aim, we construct a quantum circuit that prepares an initial state comprising an equal superposition of all feasible solutions, and define a custom mixer that preserves both the one-hot encoding constraint for vertex coloring and the cardinality constraint on the number of antennas. This scheme can be extended to a broader range of applications characterized by similar constraints. We first benchmark the performance of this quantum algorithm against a basic version of QAA, demonstrating superior performance in terms of feasibility and success probability. We then apply this algorithm to large problem sizes with hundreds of variables using a constraint-aware decomposition method based on the SPLIT framework. Our results indicate competitive performance against other large-scale classical approaches, such as branch-and-bound and simulated annealing. This work supports previous claims that constraint-aware algorithms are crucial for the practical and efficient application of quantum methods in industrial settings.

quant-ph↗

arXiv2025

Practical Application of the Quantum Carleman Lattice Boltzmann Method in Industrial CFD Simulations

Computational Fluid Dynamics simulations are crucial in industrial applications but require extensive computational resources, particularly for extreme turbulent regimes. While classical digital approaches remain the standard, quantum computing promises a breakthrough by enabling a more efficient encoding of large-scale simulations with a limited number of qubits. This work presents a practical numerical assessment of a hybrid quantum-classical approach to CFD based on the Lattice Boltzmann Method (LBM). The inherently non-linear LBM equations are linearized via a Carleman expansion and solved using the quantum Harrow Hassidim Lloyd algorithm (HHL). We evaluate this method on three benchmark cases featuring different boundary conditions, periodic, bounceback, and moving wall, using statevector emulation on high-performance computing resources. Our results confirm the validity of the approach, achieving median error fidelities on the order of $10^{-3}$ and success probabilities sufficient for practical quantum state sampling. Notably, the spectral properties of small lattice systems closely approximate those of larger ones, suggesting a pathway to mitigate one of HHL's bottlenecks: eigenvalue pre-evaluation.

quant-ph↗

arXiv2025

Quantum machine learning algorithms for anomaly detection: A review

The advent of quantum computers has justified the development of quantum machine learning algorithms , based on the adaptation of the principles of machine learning to the formalism of qubits. Among such quantum algorithms, anomaly detection represents an important problem crossing several disciplines from cybersecurity, to fraud detection to particle physics. We summarize the key concepts involved in quantum computing, introducing the formal concept of quantum speed up. The review provides a structured map of anomaly detection based on quantum machine learning. We have grouped existing algorithms according to the different learning methods, namely quantum supervised, quantum unsupervised and quantum reinforcement learning, respectively. We provide an estimate of the hardware resources to provide sufficient computational power in the future. The review provides a systematic and compact understanding of the techniques belonging to each category. We eventually provide a discussion on the computational complexity of the learning methods in real application domains.

quant-ph↗

arXiv2024

Testing Quantum and Simulated Annealers on the Drone Delivery Packing Problem

Using drones to perform human-related tasks can play a key role in various fields, such as defense, disaster response, agriculture, healthcare, and many others. The drone delivery packing problem (DDPP) arises in the context of logistics in response to an increasing demand in the delivery process along with the necessity of lowering human intervention. The DDPP is usually formulated as a combinatorial optimization problem, aiming to minimize drone usage with specific battery constraints while ensuring timely consistent deliveries with fixed locations and energy budget. In this work, we propose two alternative formulations of the DDPP as a quadratic unconstrained binary optimization (QUBO) problem, in order to test the performance of classical and quantum annealing (QA) approaches. We perform extensive experiments showing the advantages as well as the limitations of quantum annealers for this optimization problem, as compared to simulated annealing (SA) and classical state-of-the-art commercial tools for global optimization.

math.CO↗

arXiv2024

Evaluating the Practicality of Quantum Optimization Algorithms for Prototypical Industrial Applications

The optimization of the power consumption of antenna networks is a problem with a potential impact in the field of telecommunications. In this work, we investigate the application of the quantum approximate optimization algorithm (QAOA) and the quantum adiabatic algorithm (QAA), to the solution of a prototypical model in this field. We use statevector emulation in a high-performance computing environment to compare the performance of these two algorithms in terms of solution quality, using selected evaluation metrics. We estimate the circuit depth scaling with the problem size while maintaining a certain level of solution quality, and we extend our analysis up to 31 qubits, which is rarely addressed in the literature. Our calculations show that as the problem size increases, the probability of measuring the exact solution decreases exponentially for both algorithms. This issue is particularly severe when we include constraints in the problem, resulting in full connectivity between the sites. Nonetheless, we observe that the cumulative probability of measuring solutions close to the optimal one remains high also for the largest instances considered in this work. Our findings keep the way open to the application of these algorithms, or variants thereof, to generate suboptimal solutions at scales relevant to industrial use-cases.

quant-ph↗

arXiv2018

Vibrational and thermoelastic properties of bcc iron from selected EAM potentials

A comprehensive, critical study of the vibrational, thermodynamic and thermoelastic properties of bcc iron is presented, using well established semi-empirical embedded-atom method potentials available in the literature. Classical molecular dynamics simulations are used to address temperature effects, where dynamical matrices are constructed as a time average of the second moment of the atomic displacements. The $C_{11}, C_{44}, C'$ elastic constants are then obtained from the sound velocities along high symmetry directions in reciprocal space. Results are compared to ultrasonic measurements and highlight the limitations of the potentials considered here in describing thermoelastic properties.

cond-mat.mtrl-sci↗

arXiv2017

Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron

We show that the Gaussian Approximation Potential machine learning framework can describe complex magnetic potential energy surfaces, taking ferromagnetic iron as a paradigmatic challenging case. The training database includes total energies, forces, and stresses obtained from density-functional theory in the generalized-gradient approximation, and comprises approximately 150,000 local atomic environments, ranging from pristine and defected bulk configurations to surfaces and generalized stacking faults with different crystallographic orientations. We find the structural, vibrational and thermodynamic properties of the GAP model to be in excellent agreement with those obtained directly from first-principles electronic-structure calculations. There is good transferability to quantities, such as Peierls energy barriers, which are determined to a large extent by atomic configurations that were not part of the training set. We observe the benefit and the need of using highly converged electronic-structure calculations to sample a target potential energy surface. The end result is a systematically improvable potential that can achieve the same accuracy of density-functional theory calculations, but at a fraction of the computational cost.

cond-mat.mtrl-sci↗

arXiv2015

Thermoelastic properties of $α$-iron from first-principles

We calculate the thermomechanical properties of $α$-iron, and in particular its isothermal and adiabatic elastic constants, using first-principles total-energy and lattice-dynamics calculations, minimizing the quasi-harmonic vibrational free energy under finite strain deformations. Particular care is made in the fitting procedure for the static and temperature-dependent contributions to the free energy, in discussing error propagation for the two contributions separately, and in the verification and validation of pseudopotential and all-electron calculations. We find that the zero-temperature mechanical properties are sensitive to the details of the calculation strategy employed, and common semi-local exchange-correlation functionals provide only fair to good agreement with experimental elastic constants, while their temperature dependence is in excellent agreement with experiments in a wide range of temperature almost up to the Curie transition.

cond-mat.mtrl-sci↗