arXiv subjects

Davide Costa

Publications and source records attributed to Davide Costa.

18 recordsLinked to original sources

arXiv2026

Light-Hole Spin Qubits in Strained SiGe Lattice-Matched to Ge

Strained germanium ($\varepsilon$-Ge) quantum wells on metamorphic SiGe buffers have enabled advanced hole-based spin qubit devices. Alternatively, unstrained Ge with lattice-matched strained silicon-germanium ($\varepsilon$-SiGe) barriers eliminates the need for metamorphic buffers altogether. The ground state character of both these platforms is predominantly heavy-hole (HH) with a largely anisotropic spin response. We propose and study an alternative heterostructure, lattice-matched to Ge, in which both the SiGe quantum well and barriers are tensile strained, with their composition contrast providing the band offset for confinement and the tensile strain stabilizing a light-hole (LH) ground state. We show large spin-orbit coupling (SOC), both linear and cubic, along with a significantly more isotropic spin response compared to strained HH qubits. We also study the decoherence properties of the proposed device, showing an appreciable gain in the quality factor compared to their HH counterparts. Finally, we propose a bilayer heterostructure that allows for electrical switching between HH and LH ground state character.

cond-mat.mes-hall↗

arXiv2026

Buried germanium quantum well proximitised by magnetic field-resilient superconducting platinum iridium germanosilicide

Hybrid superconductor-semiconductor systems provide a versatile platform for quantum technologies, ranging from superconducting-spin interfaces to topological quantum devices. Progress toward scalable implementations requires superconductors that exhibit high critical fields ($>1$T) at accessible temperatures integrated with low-disorder semiconductor heterostructures. Here we demonstrate a superconducting platinum iridium germanosilicide (PtIrSiGe), with critical out-of-plane magnetic field up to $B_{\perp} = 1.9$T and critical temperature of $T_c\sim 1.85$K, integrated with planar germanium with mobility $\mu = 1.3\times 10^6$cm$^{2}$/Vs via top-down lithography fabrication. We show that the integrity of the germanium quantum well and mobility and density of the 2D hole gas are preserved despite annealing at $500\deg$C, a temperature comparable to that used for strained germanium epitaxy. We further demonstrate proximitisation of a buried germanium quantum well in a gate-defined Josephson junction/SQUID on a Ge/SiGe heterostructure.

cond-mat.mes-hall↗

arXiv2026

Confinement drives valley splitting above 4K in buried silicon quantum wells

Controlling the energy scales of a quantum system is essential for defining robust qubits. In silicon spin qubits, the nearly degenerate conduction-band valleys create a leakage channel from the single-spin computational basis, posing a challenge to scaling and to shuttling-based architectures. Here, we measure the relevant energy scales of single-electron spin qubits in buried silicon quantum wells co-designed for low disorder and high valley splitting. Across a linear array of four quantum dots with an average orbital energy of 2.4(2) meV, we report an average single-electron valley splitting of 0.40(6) meV and an average two-electron singlet-triplet splitting of 0.24(7) meV. In three dots, we observe a strong correlation between valley splitting and orbital energy, with an average linear coefficient of $\approx 0.22$ (meV/meV), demonstrating that electrostatic confinement can increase the valley splitting by several hundred microelectronvolts. In contrast, the remaining dot exhibits the highest valley splitting of 0.76(2) meV and low correlation, suggesting excellent characteristics for spin-qubit operation. Our findings demonstrate that strong confinement can be exploited in buried quantum wells to effectively enhance the valley splitting, thereby establishing a viable path toward the realization of shuttling and sparse-occupation-based architectures in low-disorder heterostructures.

cond-mat.mes-hall↗

arXiv2026

Towards Realistic 3D Sonar Simulation

As underwater robotics research increasingly addresses complex 3D perception and autonomous navigation, the fidelity of sonar simulation has become a key factor in algorithm development. Current simulation frameworks typically rely on geometry-driven rendering, approximating 3D sonar as an underwater equivalent to LiDAR, which fails to account for fundamental acoustic phenomena such as refraction, multi-path interference, and phase-dependent signal formation. This paper proposes a modular architecture for realistic 3D sonar simulation that integrates GPU-accelerated graphics engines with physically grounded acoustic propagation principles. We implement a volumetric 3D sonar model within the NVIDIA Isaac Sim environment, modeled after the Water Linked 3D-15 sensor, and integrate it into a comprehensive underwater simulation framework. The system is validated through a hardware-in-the-loop configuration, where a modified FastLIO2 SLAM pipeline, executed on an NVIDIA Jetson Orin Nano, performs sensor fusion using synthetic 3D sonar, DVL, IMU, and pressure data. Finally, a qualitative comparison between simulated outputs and real-world data from harbor sheet-pile inspections is provided, characterizing the remaining sim-to-real gap and establishing a roadmap toward fully acoustics-driven volumetric sensing.

cs.RO↗

arXiv2026

Sea Trial Validation of the ROS-DESERT Middleware with Autonomous Underwater Vehicles

This paper presents a modular software architecture that enables environmental-aware coordination of heterogeneous Autonomous Underwater Vehicles (AUVs) to improve underwater acoustic connectivity. The architecture combines a Robot Operating System 2 application layer with the DESERT Underwater communication framework through the rmw_desert middleware, and integrates a Robot Operating System 1 bridge to ensure interoperability with legacy vehicle front-seat controllers. This design enables fine-grained, cross-layer configurability of the communication stack and supports onboard processing of environmental measurements to inform adaptive communication behaviors. As a representative use case, this architecture is used to implement a lightweight depth-optimization strategy that exploits environmental awareness and AUV mobility to improve acoustic link performance. The complete software stack is validated through sea trials conducted off the Gulf of La Spezia in littoral water with an average depth of approximately 100m using a deployment involving three AUVs with distinct operational roles. Experimental results indicate that depth-adaptive repositioning yields measurable gains in packet reception at horizontal separation of approximately 1km, while differences are negligible at shorter ranges where the received signal energy remains above demodulation thresholds. Beyond link-level performance the sea trials confirm the feasibility, modularity, and practical deployability of the proposed architecture on existing AUV platforms.

cs.NI↗

arXiv2025

Conductance Plateaus at Quantum Hall Integer Filling Factors in Germanium Quantum Point Contacts

Constricting transport through a one-dimensional quantum point contact in the quantum Hall regime enables gate-tunable selection of the edge modes propagating between voltage probe electrodes. Here we investigate the quantum Hall effect in a quantum point contact fabricated on low disorder strained germanium quantum wells. For increasing magnetic field, we observe Zeeman spin-split 1D ballistic hole transport evolving to integer quantum Hall states, with well-defined quantised conductance increasing in multiples of $e^2/h$ down to the first integer filling factor $\nu=1$. These results establish strained germanium as a viable platform for complex experiments probing many-body states and quantum phase transitions.

cond-mat.mes-hall↗

arXiv2025

Using Landau quantization to probe disorder in semiconductor heterostructures

Understanding scattering mechanisms in semiconductor heterostructures is crucial to reducing sources of disorder and ensuring high yield and uniformity in large spin qubit arrays. Disorder of the parent two-dimensional electron or hole gas is commonly estimated by the critical, percolation-driven density associated with the metal-insulator transition. However, a reliable estimation of the critical density within percolation theory is hindered by the need to measure conductivity with high precision at low carrier densities, where experiments are most difficult. Here, we connect experimentally percolation density and quantum Hall plateau width, in line with an earlier heuristic intuition, and offer an alternative method for characterizing semiconductor heterostructure disorder.

cond-mat.mes-hall↗

arXiv2025

Quantum oscillations in two-dimensional hole gases with competing cyclotron and Zeeman energy

Evaluation of critical bandstructure and quantum transport parameters in two-dimensional systems is challenging when competition emerges among different energy scales shaping quantum oscillations in a magnetic field. Here we overcome this challenge in low-disorder strained germanium quantum wells by evaluating self-consistently effective mass, g-factor, and quantum lifetime. As a result, we estimate a quantum mobility of 133(3)$\times$10$^3$ cm$^2$/Vs, setting a benchmark for 2D holes in group IV semiconductors. The high quality of the hole gas if further highlighted by observing clean fractional quantum Hall states at low magnetic field and low density.

cond-mat.mes-hall↗

arXiv2025

Long-term operation of the screen-printed graphite-based resistive coatings on the HPL electrode for the Resistive Plate Chamber

The reliability of large-area Resistive Plate Chambers (RPCs) operated under High-Luminosity Large Hadron Collider (HL-LHC) conditions is governed by the long-term stability and radiation tolerance of screen-printed graphite/phenoxy coatings on high-pressure-laminate (HPL) electrodes. This work presents a comprehensive, end-to-end qualification of such coatings that integrates industrial process control and metrology with controlled humidity/temperature campaigns, extended high-voltage stress testing to decade-scale charge levels, and representative neutron and gamma irradiation at CERN facilities. The results establish reproducible industrial coating production, stable performance under sustained operation and irradiation, and practical acceptance criteria with operating and monitoring guidelines. The study provides a transferable quality-assurance framework for graphite-based resistive coatings on HPL electrodes, enabling reproducible production and reliable RPC performance for the HL-LHC upgrades and for future high-rate collider experiments.

hep-ex↗

arXiv2025

Buried unstrained germanium channels: a lattice-matched platform for quantum technology

Strained Ge ($\epsilon$-Ge) and strained Si ($\epsilon$-Si) buried quantum wells have enabled advanced spin-qubit quantum processors. However, in the absence of suitable lattice-matched substrates, $\epsilon$-Ge and $\epsilon$-Si are deposited on defective, metamorphic SiGe substrates, which may impact device performance and scaling. Here an alternative platform is introduced, based on the heterojunction between unstrained Ge and a lattice-matched strained SiGe ($\epsilon$-SiGe) barrier, eliminating the need for metamorphic buffers altogether. In a structure with a 52-nm-thick $\epsilon$-SiGe barrier, a low-disorder two-dimensional hole gas is demonstrated with a high-mobility of 1.33$\times$10$^5$ cm$^2$/Vs and a low percolation density of 1.4(1)$\times$10$^1$$^0$ cm$^-$$^2$. Quantum transport shows that holes confined in the buried unstrained Ge channel have a strong density-dependent in-plane effective mass and out-of-plane $g$-factor, pointing to a significant heavy-hole$-$light-hole mixing in agreement with theory. Measurements of Zeeman spin-split levels in quantum point contacts further highlight this character, showing a two-fold larger in-plane $g$-factor in Ge than in $\epsilon$-Ge. The prospect of strong spin-orbit interaction, isotopic purification, and of hosting superconducting pairing correlations make this platform appealing for fast quantum hardware and hybrid quantum systems.

cond-mat.mes-hall↗

arXiv2025

Resistive Plate Chamber Detector Construction and Certification: State-of-the-Art Facilities at the Max Planck Institute for Physics, in Partnership with Industrial Partners

Resistive Plate Chambers (RPCs) featuring 1 mm gas volumes combined with high-pressure phenolic laminate (HPL) electrodes provide excellent timing resolution down to a few hundred picoseconds, along with spatial resolution on the order of a few millimeters. Thanks to their relatively low production cost and robust performance in high-background environments, RPCs have become essential components for instrumenting large detection areas in high-energy physics experiments. The growing demand for these advanced RPC detectors, particularly for the High-Luminosity upgrade of the Large Hadron Collider (HL-LHC), necessitates the establishment of new production facilities capable of delivering high-quality detectors at an industrial scale. To address this requirement, a dedicated RPC assembly and certification facility has been developed at the Max Planck Institute for Physics in Munich, leveraging strategic collaborations with industrial partners MIRION and PTS. This partnership facilitated the transfer of advanced, research-level assembly methodologies into robust, scalable industrial processes. Through a structured, phased prototyping and certification approach, initial tests on small-scale ($40 \times 50 \, cm^2$) prototypes validated the scalability and applicability of optimized production procedures to large-scale ($1.0 \times 2.0 \, m^2$) RPC detectors. Currently, the project has entered its final certification phase, involving extensive performance and longevity testing, including a year-long irradiation campaign at CERN's Gamma Irradiation Facility (GIF++). This article details the development and successful industrial implementation of novel assembly techniques, highlighting the enhanced capabilities and reliability of RPC detectors prepared through this industrial-academic collaboration, ensuring readiness for upcoming challenges in high-energy physics detector instrumentation.

physics.ins-det↗

arXiv2025

QARPET: A Crossbar Chip for Benchmarking Semiconductor Spin Qubits

Large-scale integration of semiconductor spin qubits into quantum processors hinges on the ability to characterize quantum components at scale, a task challenged by their operation at sub-kelvin temperatures, in the presence of magnetic fields, and by the use of radio-frequency signals. Here, we present QARPET (Qubit-Array Research Platform for Engineering and Testing), a scalable architecture for characterizing spin qubits using a quantum dot crossbar array. The crossbar features tightly-pitched spin qubit tiles and is implemented in planar germanium, by fabricating a large device with the potential to host 1058 hole spin qubits. Measurements on a patch of 40 tiles demonstrate key device functionality at millikelvin temperatures, including tile addressability, threshold voltage and charge noise statistics, and the characterisation of hole spin qubits and their coherence times in a single tile. These demonstrations pave the way for a new generation of quantum devices designed for the statistical characterisation of spin qubits.

cond-mat.mes-hall↗

arXiv2024

Exploiting epitaxial strained germanium for scaling low noise spin qubits at the micron-scale

Disorder in the heterogeneous material stack of semiconductor spin qubit systems introduces noise that compromises quantum information processing, posing a challenge to coherently control large-scale quantum devices. Here, we exploit low-disorder epitaxial strained quantum wells in Ge/SiGe heterostructures grown on Ge wafers to comprehensively probe the noise properties of complex micron-scale devices comprising of up to ten quantum dots and four rf-charge sensors arranged in a two-dimensional array. We demonstrate an average charge noise of $\sqrt{S_{0}}=0.3(1)$ $\mu\mathrm{eV}/\sqrt{\mathrm{Hz}}$ at 1 Hz across different locations on the wafer, providing a benchmark for quantum confined holes. We then establish hole-spin qubit control in these heterostructures and extend our investigation from electrical to magnetic noise through spin echo measurements. Exploiting dynamical decoupling sequences, we quantify the power spectral density components arising from the hyperfine interaction with $^{73}$Ge spinful isotopes and identify coherence modulations associated with the interaction with the $^{29}$Si nuclear spin bath near the Ge quantum well. We estimate an integrated hyperfine noise amplitude $\sigma_f$ of 180(8) kHz from $^{73}$Ge and of 47(5) kHz from $^{29}$Si, underscoring the need for full isotopic purification of the qubit host environment.

cond-mat.mes-hall↗

arXiv2024

Reducing disorder in Ge quantum wells by using thick SiGe barriers

We investigate the disorder properties of two-dimensional hole gases in Ge/SiGe heterostructures grown on Ge wafers, using thick SiGe barriers to mitigate the influence of the semiconductor-dielectric interface. Across several heterostructure field effect transistors we measure an average maximum mobility of $(4.4 \pm 0.2) \times 10^{6}~\mathrm{cm^2/Vs}$ at a saturation density of $(1.72 \pm 0.03) \times 10^{11}~\mathrm{cm^{-2}}$, corresponding to a long mean free path of $(30 \pm 1)~\mathrm{\mu m}$. The highest measured mobility is $4.68 \times 10^{6}~\mathrm{cm^2/Vs}$. We identify uniform background impurities and interface roughness as the dominant scattering mechanisms limiting mobility in a representative device, and we evaluate a percolation-induced critical density of $(4.5 \pm 0.1)\times 10^{9} ~\mathrm{cm^{-2}}$. This low-disorder heterostructure, according to simulations, may support the electrostatic confinement of holes in gate-defined quantum dots.

cond-mat.mes-hall↗

arXiv2024

Is Contrasting All You Need? Contrastive Learning for the Detection and Attribution of AI-generated Text

The significant progress in the development of Large Language Models has contributed to blurring the distinction between human and AI-generated text. The increasing pervasiveness of AI-generated text and the difficulty in detecting it poses new challenges for our society. In this paper, we tackle the problem of detecting and attributing AI-generated text by proposing WhosAI, a triplet-network contrastive learning framework designed to predict whether a given input text has been generated by humans or AI and to unveil the authorship of the text. Unlike most existing approaches, our proposed framework is conceived to learn semantic similarity representations from multiple generators at once, thus equally handling both detection and attribution tasks. Furthermore, WhosAI is model-agnostic and scalable to the release of new AI text-generation models by incorporating their generated instances into the embedding space learned by our framework. Experimental results on the TuringBench benchmark of 200K news articles show that our proposed framework achieves outstanding results in both the Turing Test and Authorship Attribution tasks, outperforming all the methods listed in the TuringBench benchmark leaderboards.

cs.CL↗

arXiv2023

Germanium wafers for strained quantum wells with low disorder

We grow strained Ge/SiGe heterostructures by reduced-pressure chemical vapor deposition on 100 mm Ge wafers. The use of Ge wafers as substrates for epitaxy enables high-quality Ge-rich SiGe strain-relaxed buffers with a threading dislocation density of (6$\pm$1)$\times$10$^5$ cm$^{-2}$, nearly an order of magnitude improvement compared to control strain-relaxed buffers on Si wafers. The associated reduction in short-range scattering allows for a drastic improvement of the disorder properties of the two-dimensional hole gas, measured in several Ge/SiGe heterostructure field-effect transistors. We measure an average low percolation density of (1.22$\pm$0.03)$\times$10$^{10}$ cm$^{-2}$, and an average maximum mobility of (3.4$\pm$0.1)$\times$10$^{6}$ cm$^2$/Vs and quantum mobility of (8.4$\pm$0.5)$\times$10$^{4}$ cm$^2$/Vs when the hole density in the quantum well is saturated to (1.65$\pm$0.02)$\times$10$^{11}$ cm$^{-2}$. We anticipate immediate application of these heterostructures for next-generation, higher-performance Ge spin-qubits and their integration into larger quantum processors.

cond-mat.mes-hall↗

arXiv2023

Visually Wired NFTs: Exploring the Role of Inspiration in Non-Fungible Tokens

The fervor for Non-Fungible Tokens (NFTs) attracted countless creators, leading to a Big Bang of digital assets driven by latent or explicit forms of inspiration, as in many creative processes. This work exploits Vision Transformers and graph-based modeling to delve into visual inspiration phenomena between NFTs over the years. Our goals include unveiling the main structural traits that shape visual inspiration networks, exploring the interrelation between visual inspiration and asset performances, investigating crypto influence on inspiration processes, and explaining the inspiration relationships among NFTs. Our findings unveil how the pervasiveness of inspiration led to a temporary saturation of the visual feature space, the impact of the dichotomy between inspiring and inspired NFTs on their financial performance, and an intrinsic self-regulatory mechanism between markets and inspiration waves. Our work can serve as a starting point for gaining a broader view of the evolution of Web3.

cs.SI↗

arXiv2023

Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction

Non-Fungible Tokens (NFTs) represent deeds of ownership, based on blockchain technologies and smart contracts, of unique crypto assets on digital art forms (e.g., artworks or collectibles). In the spotlight after skyrocketing in 2021, NFTs have attracted the attention of crypto enthusiasts and investors intent on placing promising investments in this profitable market. However, the NFT financial performance prediction has not been widely explored to date. In this work, we address the above problem based on the hypothesis that NFT images and their textual descriptions are essential proxies to predict the NFT selling prices. To this purpose, we propose MERLIN, a novel multimodal deep learning framework designed to train Transformer-based language and visual models, along with graph neural network models, on collections of NFTs' images and texts. A key aspect in MERLIN is its independence on financial features, as it exploits only the primary data a user interested in NFT trading would like to deal with, i.e., NFT images and textual descriptions. By learning dense representations of such data, a price-category classification task is performed by MERLIN models, which can also be tuned according to user preferences in the inference phase to mimic different risk-return investment profiles. Experimental evaluation on a publicly available dataset has shown that MERLIN models achieve significant performances according to several financial assessment criteria, fostering profitable investments, and also beating baseline machine-learning classifiers based on financial features.

cs.LG↗