arXiv subjects

Luca Cappelli

Publications and source records attributed to Luca Cappelli.

11 recordsLinked to original sources

arXiv2026

Comparative qualification of advanced plasma-facing materials for fusion pilot plants through public- and private-sector experiments in DIII-D

A coordinated DIII-D campaign exposed and comparatively assessed 44 advanced plasma-facing materials from 12 institutions, including four public-private fusion partnerships, to support fusion pilot plant wall and divertor material down-selection. Samples were exposed using the Divertor Materials Evaluation System (DiMES) under Ohmic, L-mode, and H-mode conditions with edge-localized modes, at 0.2-2.5 MW m$^{-2}$ on flush geometries and 10-15 MW m$^{-2}$ on 10$^{\circ}$ angled geometries. Engineered tungsten architectures retained integrity; long-fiber Wf/W showed the clearest crack-arrest behavior. W-Re and K-doped W showed near-ITER-W-like responses, while additively manufactured W-Ta showed heat-flux-sensitive mass losses of 0.64 mg for the flat sample and 2.19-2.87 mg for angled samples. After irradiation to 0.3 dpa at 550$^{\circ}$C, neutron-irradiated ITER-grade W retained 2.8 times more deuterium than pristine W, while TiB$_2$ showed the lowest D$_2$ release in the Ohmic set. VTaHfMo was the most stable refractory multi-principal-element alloy. NbC and (Nb$_{0.5}$Ta$_{0.5}$)C retained integrity with 0.02-0.03 mg mass loss, whereas ZrC lost 7 mg. CVD SiC retained macroscopic integrity but exhibited an effective Si erosion yield of 0.5, about 5-10 times above prior DIII-D trends. Renewable boron pebble rods underwent controlled recession; 13% of released boron was ionized near the outer strike point and up to 50% was recovered locally. Initial in-situ chromium gross-erosion measurements yielded values of order $10^{-2}$. Together, these results provide cross-material benchmarks for fusion pilot plant down-selection and future AI/ML-assisted plasma-facing-material development.

physics.plasm-ph↗

arXiv2026

Lowest order Carleman linearization for low Reynolds long-term behaviour of fluid flow simulations

It is shown that the lowest (second) order truncation of the Carleman linearization of the fluid equations (C2) recovers the late stage of the evolution, namely the steady-state solution, although to a decreasing degree of accuracy at increasing Reynolds number. This asymptotic property is first proved analytically for the decaying logistic with external forcing and then shown to hold to a significant degree of accuracy also for the more complex case of two-dimensional Kolmogorov-like fluid flow at low Reynolds numbers, below $Re \sim 10$. This time-asymptotic property may open interesting prospects for the quantum simulation of low-Reynolds steady-state fluid flows.

quant-ph↗

arXiv2026

Schr\"odinger-Navier-Stokes Equation for the Quantum Simulation of Navier-Stokes Flows

The search for quantum-like wave formulations of the Navier-Stokes (Schr\"odinger-Navier-Stokes, SNS for short) equations describing classical dissipative fluids has met with increasing attention in the recent years, due to the large portfolio of potential applications in science and engineering. A SNS formulation of classical fluids was first presented in a largely un-noticed paper by Dietrich and Vautherin back in 1985(Journal de Physique). In this paper, we revisit this specific SNS approach and assess its viability for quantum implementations based on Carleman embedding/linearization techniques. Specifically, we i) Clarify in full mathematical detail why the SNS dissipator presents a steep challenge for quantum computers and propose a way out strategy based on the Hamilton-Jacobi (HJ) formulation of fluid dynamics; ii) Develop a corresponding quantum algorithm using a new technique based on a tensor-network representation of Carleman embedding of the HJ equations (CHJ) which permits substantial memory savings; iii) Emulate the CHJ quantum algorithm on a classical computer and analyse its convergence and accuracy for the specific case of Kolmogorov-like flows at moderate Reynolds numbers. To the best of our knowledge, this is the first quantum algorithm based on a quantum-like wave formulation of the genuine Navier-Stokes equations, including pressure, dissipation and vorticity.

quant-ph↗

arXiv2026

A Quantum Genetic Algorithm with application to Cosmological Parameters Estimation

An Amplitude-Encoded Quantum Genetic Algorithm (AEQGA) has been developed to minimize $\chi^2$ functions of different cosmological probes (Supernovae Type Ia, Baryon Acoustic Oscillations, Cosmic Microwave Background Radiation), to find the best-fit value for two cosmological parameters, namely the Hubble Constant and the density matter content of the Universe today. Our main aim is to pave the way to testing the adoption of quantum optimization in the inference of the cosmological parameters that describe the universe evolution. AEQGA computes the merit function classically, and then uses a quantum circuit to entangle the population and perform crossover and mutation operations. The results show consistency with the isocontours of the objective functions. We then tested the general behavior of AEQGA as a function of its hyperparameters and compared it with a second quantum genetic algorithm found in the literature as well as with classical algorithms, finding consistent results.

astro-ph.CO↗

arXiv2026

Block encoding of sparse matrices with a periodic diagonal structure

Block encoding is a successful technique used in several powerful quantum algorithms. In this work we provide an explicit quantum circuit for block encoding a sparse matrix with a periodic diagonal structure. The proposed methodology is based on the linear combination of unitaries (LCU) framework and on an efficient unitary operator used to project the complex exponential at a frequency $\omega$ multiplied by the computational basis into its real and imaginary components. We demonstrate a distinct computational advantage with a $\mathcal{O}(\text{poly}(n))$ gate complexity, where $n$ is the number of qubits, in the worst-case scenario used for banded matrices, and $\mathcal{O}(n)$ when dealing with a simple diagonal matrix, compared to the exponential scaling of general-purpose methods for dense matrices. Various applications for the presented methodology are discussed in the context of solving differential problems such as the advection-diffusion-reaction (ADR) dynamics, using quantum algorithms with optimal scaling, e.g., quantum singular value transformation (QSVT). Numerical results are used to validate the analytical formulation.

quant-ph↗

arXiv2025

Quantum Markov Chain Monte Carlo for Cosmological Functions

We present an implementation of Quantum Computing for a Markov Chain Monte Carlo method with an application to cosmological functions, to derive posterior distributions from cosmological probes. The algorithm proposes new steps in the parameter space via a quantum circuit whose resulting statevector provides the components of the shift vector. The proposed point is accepted or rejected via the classical Metropolis-Hastings acceptance method. The advantage of this hybrid quantum approach is that the step size and direction change in a way independent of the evolution of the chain, thus ideally avoiding the presence of local minima. The results are consistent with analyses performed with classical methods, both for a test function and real cosmological data. The final goal is to generalize this algorithm to test its application to complex cosmological computations.

astro-ph.CO↗

arXiv2025

Quantum Algorithm for the Fixed-Radius Neighbor Search

Neighbor search is a computationally demanding problem, usually both time- and memory-consuming. The main problem of this kind of algorithms is the long execution time due to cache misses. In this work, we propose a quantum algorithm for the Fixed RAdius Neighbor Search problem (FRANS) based on the fixed-point version of Grover's algorithm. We propose an efficient circuit for solving the FRANS with linear query complexity with the number of particles $N$. The quantum circuit returns the list of all the neighbors' pairs within the fixed radius, together with their distance, avoiding the slow down given by cache miss. We analyzed the gate and the query complexity of the circuit. Our FRANS algorithm presents a query complexity of $\mathcal{O}(N/\sqrt{M})$, where $M$ is the number of solutions, reaching the optimal lower bound of the Grover's algorithm. We propose different implementations of the oracle, which must be chosen depending on the precise structure of the database. Among these, we present an implementation using the Chebyshev distance with depth $\mathcal{O}(q_1)$, where $2^{q_1}$ is the number of grid points used to discretize a spatial dimension. State-of-the-art algorithms for state preparation allow for a trade-off between depth and width of the circuit, with a volume (depth$\times$ width) of $\mathcal{O}(N\log(N))$. This unfavorable scaling can be brought down to $\mathcal{O}(\text{poly}(\log N))$ in case of structured datasets. We proposed a stopping criterion based on Bayes interference and tested its validity on $1D$ simulations. Finally, we accounted for the readout complexity and assessed the resilience of the model to the readout error, suggesting an error correction-free strategy to check the accuracy of the results.

quant-ph↗

arXiv2025

The Application of Quantum Fourier Transform in Cosmic Microwave Background Data Analysis

The Cosmic Microwave Background (CMB) data analysis and the map-making process rely heavily on the use of spherical harmonics. For suitable pixelizations of the sphere, the (forward and inverse) Fourier transform plays a crucial role in computing all-sky map from spherical harmonic expansion coefficients -- or from angular power spectrum -- and vice versa. While the Fast Fourier Transform (FFT) is traditionally employed in these computations, the Quantum Fourier Transform (QFT) offers a theoretical advantage in terms of computational efficiency and potential speedup. In this work, we study the potential advantage of using the QFT in this context by exploring the substitution of the FFT with the QFT within the \textit{healpy} package. Performance evaluations are conducted using the Aer simulator. Our results indicate that QFT exhibits potential advantages over FFT that are particularly relevant at high-resolution. However, classical-to-quantum data encoding overhead represents a limitation to current efficiency. In this work, we adopted amplitude encoding, due to its efficiency on encoding maximum data to minimum number of qubits. We identify data encoding as a potential significant bottleneck and discuss its impact on quantum speedup. Future improvements in quantum encoding strategies and algorithmic optimizations could further enhance the feasibility of QFT in CMB data analysis.

astro-ph.IM↗

arXiv2025

Numerical limits in the integration of Vlasov-Poisson equation for Cold Dark Matter

The Vlasov-Poisson systems of equations (VP) describes the evolution of a distribution of collisionless particles under the effect of a collective-field potential. VP is at the basis of the study of the gravitational instability of cosmological density perturbations in Dark-Matter (DM), but its range of application extends to other fields, such as plasma physics. In the case of Cold Dark Matter, a single velocity is associated with each fluid-element (or particle) , the initial condition presents a stiff discontinuity. This creates problems such as diffusion or negative distribution function when a grid based method is used to solve VP. In this work we want to highlight this problem, focusing on the technical aspects of this phenomenon. By comparing different finite volume methods and a spectral method we observe that, while all integration schemes preserve the invariants of the system (e.g, energy), the physical observable of interest, i.e., the density, is not correctly reproduced. We thus compare the density obtained with the different Eulerian integration schemes with the result obtained from a reference N-body method. We point out that the most suitable method to solve the VP system for a self-gravitating system is a spectral method.

physics.comp-ph↗

arXiv2025

Benchmarking Quantum Convolutional Neural Networks for Signal Classification in Simulated Gamma-Ray Burst Detection

This study evaluates the use of Quantum Convolutional Neural Networks (QCNNs) for identifying signals resembling Gamma-Ray Bursts (GRBs) within simulated astrophysical datasets in the form of light curves. The task addressed here focuses on distinguishing GRB-like signals from background noise in simulated Cherenkov Telescope Array Observatory (CTAO) data, the next-generation astrophysical observatory for very high-energy gamma-ray science. QCNNs, a quantum counterpart of classical Convolutional Neural Networks (CNNs), leverage quantum principles to process and analyze high-dimensional data efficiently. We implemented a hybrid quantum-classical machine learning technique using the Qiskit framework, with the QCNNs trained on a quantum simulator. Several QCNN architectures were tested, employing different encoding methods such as Data Reuploading and Amplitude encoding. Key findings include that QCNNs achieved accuracy comparable to classical CNNs, often surpassing 90\%, while using fewer parameters, potentially leading to more efficient models in terms of computational resources. A benchmark study further examined how hyperparameters like the number of qubits and encoding methods affected performance, with more qubits and advanced encoding methods generally enhancing accuracy but increasing complexity. QCNNs showed robust performance on time-series datasets, successfully detecting GRB signals with high precision. The research is a pioneering effort in applying QCNNs to astrophysics, offering insights into their potential and limitations. This work sets the stage for future investigations to fully realize the advantages of QCNNs in astrophysical data analysis.

astro-ph.HE↗

arXiv2023

From Vlasov-Poisson to Schr\"odinger-Poisson: dark matter simulation with a quantum variational time evolution algorithm

Cosmological simulations describing the evolution of density perturbations of a self-gravitating collisionless Dark Matter (DM) fluid in an expanding background, provide a powerful tool to follow the formation of cosmic structures over wide dynamic ranges. The most widely adopted approach, based on the N-body discretization of the collisionless Vlasov-Poisson (VP) equations, is hampered by an unfavorable scaling when simulating the wide range of scales needed to cover at the same time the formation of single galaxies and of the largest cosmic structures. The dynamics described by the VP equations is limited by the rapid increase of the number of resolution elements which is required to simulate an ever growing range of scales. Recent studies showed an interesting mapping of the 6-dimensional+1 (6D+1) VP problem into a more amenable 3D+1 non-linear Schr\"odinger-Poisson (SP) problem for simulating the evolution of DM perturbations. This opens up the possibility of improving the scaling of time propagation simulations using quantum computing. In this paper, we introduce a quantum algorithm for simulating the (SP) equation by adapting a variational real-time evolution approach to a self-consistent, non-linear, problem. To achieve this, we designed a novel set of quantum circuits that establish connections between the solution of the original Poisson equation and the solution of the corresponding time-dependent Schr\"odinger equation. We also analyzed how nonlinearity impacts the variance of observables. Furthermore, we explored how the spatial resolution behaves as the SP dynamics approaches the classical limit and discovered an empirical logarithmic relationship between the required number of qubits and the scale of the SP equation. This entire approach holds the potential to serve as an efficient alternative for solving the Vlasov-Poisson (VP) equation by means of classical algorithms.

quant-ph↗