arXiv subjects

Daniele Faccio

Publications and source records attributed to Daniele Faccio.

At least 19 recordsLinked to original sources

arXiv2026

SceneHI: High-Resolution 3D-Consistent Scene Texturing with Controllable Illumination

SceneHI is a framework that lifts high-resolution, illumination-aware priors from 2D diffusion models to perform 3D texture synthesis. It is the first to demonstrate that high-resolution textures, previously limited to 2D synthesis, can be generated directly on 3D objects without model fine-tuning or optimization. Designed for complex, multi-object environments, SceneHI uniquely combines 3D-consistency, high-resolution fidelity, and physically plausible baked shadows within a single generative pipeline. To enforce strict geometric coherence, we introduce an exact analytical pixel-to-texel mapping that aligns diffusion trajectories across multiple viewpoints. We utilize High-Resolution Latent Textures (HRLTs) as a persistent canvas for gradually denoised textures, while camera views perform the denoising steps in latent pixel space. This ensures a shared base texture that can be subsequently refined to high resolution without compromising multi-view consistency. Finally, a light-aware generative pass embeds realistic geometry-consistent shadows directly into the atlases, bridging the gap to production workflows. SceneHI achieves high visual fidelity while reducing generation time by 80% compared to existing scene-level methods.

cs.CV↗

arXiv2026

Making the Discrete Continuous: Synthetic RAW Augmentations for Fine-Grained Evaluation of Person Detection Performance in Low Light

Real-world deployment of AI vision models is both fueled and limited by the data available for training and testing. Real datasets are sparse and uneven: long-tailed or unbalanced distributions hinder generalization, and the low number of samples in low density regions makes it hard to run evaluations. Synthetic data can fill these gaps, providing us with a way to sample the input space more continuously and improve data coverage for benchmarks. Focusing on the autonomous driving safety-critical case of pedestrian detection in the dark, we show how synthetic low-light samples can be used to better characterize the performance of a state-of-the-art object detection model as a function of the scene illumination. We use a synthetic RAW image augmentation technique to generate low-light samples that match the noise model of the camera sensor. Performance metrics on real and synthetic low-light data are similar, indicating that the AI model finds it hard to distinguish between them.

cs.CV↗

arXiv2026

Single-Image Entanglement Verification with Spatially Encoded Measurement Contexts

Entangled photon pairs produced by spontaneous parametric down-conversion exhibit rich spatial entanglement structure that is often difficult to probe with conventional measurements. Here, we show that spin-orbit optical elements can convert this spatial structure into directly observable quantum interference patterns. Using a $q$-plate, we demonstrate that the relative wavefront curvature of biphoton states generated by a pair of nonlinear crystals can be retrieved from the spatial modulation of coincidence images. Building on this principle, we introduce a liquid-crystal metasurface that performs spatially multiplexed Bell measurements across the transverse profile of the photon field. The device, which we call a Clauser-Horne-Shimony-Holt (CHSH) plate, assigns different polarization projections to different azimuthal sectors of the beam, allowing the sixteen joint measurements required for a CHSH test to be realized simultaneously in a single acquisition. In this architecture, the spatial coordinate acts as a classical register selecting the measurement context, while photon pairs sample these contexts according to their emission directions. We further demonstrate that the same measurement concept can be implemented using a programmable spatial light modulator, providing a dynamically reconfigurable realization of the scheme. Our results show that spatially structured optical elements can transform Bell tests into parallel measurements distributed across the transverse plane, enabling rapid characterization of spatially varying entanglement. This approach opens new possibilities for structured-light quantum measurements, Bell-inequality-based imaging, and the study of spatially engineered entangled photon sources.

quant-ph↗

arXiv2026

Autoguided Online Data Curation for Diffusion Model Training

The costs of generative model compute rekindled promises and hopes for efficient data curation. In this work, we investigate whether recently developed autoguidance and online data selection methods can improve the time and sample efficiency of training generative diffusion models. We integrate joint example selection (JEST) and autoguidance into a unified code base for fast ablation and benchmarking. We evaluate combinations of data curation on a controlled 2-D synthetic data generation task as well as (3x64x64)-D image generation. Our comparisons are made at equal wall-clock time and equal number of samples, explicitly accounting for the overhead of selection. Across experiments, autoguidance consistently improves sample quality and diversity. Early AJEST (applying selection only at the beginning of training) can match or modestly exceed autoguidance alone in data efficiency on both tasks. However, its time overhead and added complexity make autoguidance or uniform random data selection preferable in most situations. These findings suggest that while targeted online selection can yield efficiency gains in early training, robust sample quality improvements are primarily driven by autoguidance. We discuss limitations and scope, and outline when data selection may be beneficial.

cs.CV↗

arXiv2025

Symbiotic Brain-Machine Drawing via Visual Brain-Computer Interfaces

Brain-computer interfaces (BCIs) are evolving from research prototypes into clinical, assistive, and performance enhancement technologies. Despite the rapid rise and promise of implantable technologies, there is a need for better and more capable wearable and non-invasive approaches whilst also minimising hardware requirements. We present a non-invasive BCI for mind-drawing that iteratively infers a subject's internal visual intent by adaptively presenting visual stimuli (probes) on a screen encoded at different flicker-frequencies and analyses the steady-state visual evoked potentials (SSVEPs). A Gabor-inspired or machine-learned policies dynamically update the spatial placement of the visual probes on the screen to explore the image space and reconstruct simple imagined shapes within approximately two minutes or less using just single-channel EEG data. Additionally, by leveraging stable diffusion models, reconstructed mental images can be transformed into realistic and detailed visual representations. Whilst we expect that similar results might be achievable with e.g. eye-tracking techniques, our work shows that symbiotic human-AI interaction can significantly increase BCI bit-rates by more than a factor 5x, providing a platform for future development of AI-augmented BCI.

q-bio.NC↗

arXiv2025

Intensity-Correlation Synthetic Wavelength Imaging in Dynamic Scattering Media

Imaging through dynamic scattering media, such as biological tissue, presents a fundamental challenge due to light scattering and the formation of speckle patterns. These patterns not only degrade image quality but also decorrelate rapidly, limiting the effectiveness of conventional approaches, such as those based on transmission matrix measurements. Here, we introduce an imaging approach based on second-order correlations and synthetic wavelength holography (SWH) to enable robust image reconstruction through thick and dynamic scattering media. By exploiting intensity speckle correlations and using short-exposure intensity images, our method computationally reconstructs images from a hologram without requiring phase stability or static speckles, making it inherently resilient to phase noise. Experimental results demonstrate high-resolution imaging in both static and dynamic scattering scenarios, offering a promising solution for biomedical imaging, remote sensing, and real-time imaging in complex environments.

physics.optics↗

arXiv2025

Generating quantum non-local entanglement with mechanical rotations

Recent experiments have searched for evidence of the impact of non-inertial motion on the entanglement of particles. The success of these endeavours has been hindered by the fact that such tests were performed within spatial scales that were only "local" when compared to the spatial scales over which the non-inertial motion was taking place. We propose a Sagnac-like interferometer that, by challenging such bottlenecks, is able to achieve entangled states through a mechanism induced by the mechanical rotation of a photonic interferometer. The resulting states violate the Bell-Clauser-Horne-Shimony-Holt (Bell-CHSH) inequality all the way up to the Tsirelson bound, thus signalling strong quantum nonlocality. Furthermore, we show that the Bell-CHSH inequality remains violated even without using any form of post-selection up to the value $1+\sqrt{2}$. Our results demonstrate that mechanical rotation can be thought of as resource for controlling quantum non-locality with implications also for recent proposals for experiments that can probe the quantum nature of curved spacetimes and non-inertial motion.

quant-ph↗

arXiv2025

Fisher information flow in artificial neural networks

The estimation of continuous parameters from measured data plays a central role in many fields of physics. A key tool in understanding and improving such estimation processes is the concept of Fisher information, which quantifies how information about unknown parameters propagates through a physical system and determines the ultimate limits of precision. With Artificial Neural Networks (ANNs) gradually becoming an integral part of many measurement systems, it is essential to understand how they process and transmit parameter-relevant information internally. Here, we present a method to monitor the flow of Fisher information through an ANN performing a parameter estimation task, tracking it from the input to the output layer. We show that optimal estimation performance corresponds to the maximal transmission of Fisher information, and that training beyond this point results in information loss due to overfitting. This provides a model-free stopping criterion for network training-eliminating the need for a separate validation dataset. To demonstrate the practical relevance of our approach, we apply it to a network trained on data from an imaging experiment, highlighting its effectiveness in a realistic physical setting.

cs.LG↗

arXiv2025

Non-classical optimization of entangled photons through complex media

Optimization approaches are ubiquitous in physics. In optics, they are key to manipulating light through complex media, enabling applications ranging from imaging to photonic simulators. In most demonstrations, however, the optimization process is implemented using classical coherent light, leading to a purely classical solution. Here we introduce the concept of optical non-classical optimization in complex media. We experimentally demonstrate the control and refocusing of non-classical light -- namely, entangled photon pairs -- through a scattering medium by directly optimizing the output coincidence rate. The optimal solutions found with this approach differ from those obtained using classical optimization, a result of entanglement in the input state. Beyond imaging, this genuinely non-classical optimization method has potential to tackle complex problems, as we show by simulating a spin-glass model with multi-spin interactions.

quant-ph↗

arXiv2025

Model-free estimation of the Cramér-Rao bound for deep-learning microscopy in complex media

Artificial neural networks have become important tools to harness the complexity of disordered or random photonic systems. Recent applications include the recovery of information from light that has been scrambled during propagation through a complex scattering medium, especially in the challenging case where the deterministic input-output transmission matrix cannot be measured. This naturally raises the question of what the limit is that information theory imposes on this recovery process, and whether neural networks can actually reach this limit. To answer these questions, we introduce a model-free approach to calculate the Cramér-Rao bound, which sets the ultimate precision limit at which artificial neural networks can operate. As an example, we apply this approach in a proof-of-principle experiment using laser light propagating through a disordered medium, evidencing that a convolutional network approaches the ultimate precision limit in the challenging task of localizing a reflective target hidden behind a dynamically-fluctuating scattering medium. The model-free method introduced here is generally applicable to benchmark the performance of any deep-learning microscope, to drive algorithmic developments and to push the precision of metrology and imaging techniques to their ultimate limit.

physics.optics↗

arXiv2025

Entangled-photon time- and frequency-resolved optical spectroscopy

Classical time-resolved optical spectroscopy experiments are performed using sequences of ultrashort light pulses, with photon fluxes incident on the sample which are many orders of magnitude higher than real-world conditions corresponding to sunlight illumination. Here we overcome this paradigm by exploiting quantum correlations to perform time-resolved spectroscopy with entangled photons. Starting from spontaneous parametric down-conversion driven by a continuous-wave laser, we exploit the temporal entanglement between randomly generated signal/idler pairs to obtain temporal resolution, and their spectral entanglement to select the excitation frequency. We also add spectral resolution in detection, using a Fourier transform approach which employs a common-path interferometer. We demonstrate the potential of our entangled-photon streak camera by resolving, at the single-photon level, excitation energy transfer cascades from LH2 to LH1 in the photosynthetic membrane and disentangling the lifetimes of two dyes in a mixture. We show that time-resolved spectroscopy with quantum light can be performed without compromising measurement time, recording a fluorescence time trace in less than a minute even for samples with low quantum yield, which can be reduced to sub-second times with acceptable signal-to-noise ratio. Our results provide a new approach to ultrafast optical spectroscopy, where experiments are performed under conditions comparable to real-world sunlight illumination.

quant-ph↗

arXiv2025

Quantum-enhanced second harmonic generation beyond the photon pairs regime

Two-photon processes are crucial in applications like microscopy and microfabrication, but their low cross-section requires intense illumination and limits, e.g., the penetration depth in nonlinear microscopy. Entangled states have been proposed to enhance the efficiency of two-photon interactions and have shown effectiveness at low intensities. This quantum enhancement is generally believed to be lost at high intensities, for more than one photon per mode, raising doubts about its usefulness. We explored experimentally and theoretically two-photon processes driven by entangled photons at intensities beyond this threshold and compared the results with the classical case. We found that a quantum advantage can still be observed at nearly one order of magnitude higher intensities than previously assumed. Our findings show a potential path for exploiting quantum-enhanced two-photon processes in practical applications.

quant-ph↗

arXiv2025

Photon Number-Resolving Quantum Reservoir Computing

Neuromorphic processors improve the efficiency of machine learning algorithms through the implementation of physical artificial neurons to perform computations. However, whilst efficient classical neuromorphic processors have been demonstrated in various forms, practical quantum neuromorphic platforms are still in the early stages of development. Here we propose a fixed optical network for photonic quantum reservoir computing that is enabled by photon number-resolved detection of the output states. This significantly reduces the required complexity of the input quantum states while still accessing a high-dimensional Hilbert space. The approach is implementable with currently available technology and lowers the barrier to entry to quantum machine learning.

quant-ph↗

arXiv2025

Creation of a black hole bomb instability in an electromagnetic system

The amplification and generation of electromagnetic radiation by a rotating metallic or lossy cylinder, first theorized by Zeldovich in the 1970s, is tightly connected to the concepts of quantum friction, energy extraction from rotating black holes and runaway mechanisms such as black hole bombs. Despite recent advances including acoustic analogues of the Zeldovich effect and the observation of a negative resistance in a low-frequency electromagnetic model, actual positive signal amplitude gain, the spontaneous generation of electromagnetic waves and runaway amplifi- cation effects have never been experimentally verified. Here, we demonstrate experimentally that a mechanically rotating metallic cylinder not only definitively acts as an amplifier of a rotating elec- tromagnetic field mode but also, when paired with a low-loss resonator, becomes unstable and acts as a generator, seeded only by noise. The system exhibits an exponential runaway amplification of spontaneously generated electromagnetic modes thus demonstrating the electromagnetic analogue of Press and Teukolskys black hole bomb. The exponential amplification from noise supports theoretical investigations into black hole instabilities and is promising for the development of future experiments to observe quantum friction in the form of the Zeldovich effect seeded by the quantum vacuum.

quant-ph↗

arXiv2025

IGAF: Incremental Guided Attention Fusion for Depth Super-Resolution

Accurate depth estimation is crucial for many fields, including robotics, navigation, and medical imaging. However, conventional depth sensors often produce low-resolution (LR) depth maps, making detailed scene perception challenging. To address this, enhancing LR depth maps to high-resolution (HR) ones has become essential, guided by HR-structured inputs like RGB or grayscale images. We propose a novel sensor fusion methodology for guided depth super-resolution (GDSR), a technique that combines LR depth maps with HR images to estimate detailed HR depth maps. Our key contribution is the Incremental guided attention fusion (IGAF) module, which effectively learns to fuse features from RGB images and LR depth maps, producing accurate HR depth maps. Using IGAF, we build a robust super-resolution model and evaluate it on multiple benchmark datasets. Our model achieves state-of-the-art results compared to all baseline models on the NYU v2 dataset for $\times 4$, $\times 8$, and $\times 16$ upsampling. It also outperforms all baselines in a zero-shot setting on the Middlebury, Lu, and RGB-D-D datasets. Code, environments, and models are available on GitHub.

cs.CV↗

arXiv2024

Photon transport through the entire adult human head

Optical brain imaging technologies are promising due to their relatively high temporal resolution, portability and cost-effectiveness. However, the highly scattering nature of near-infrared light in human tissue makes it challenging to collect photons emerging from more than 4 cm below the scalp, or with source-detector separation larger than several centimeters. We explore the physical limits of photon transport in the head and show that despite an extreme attenuation of ~10^(18), we can experimentally detect light that is transmitted diametrically through the entire adult human head. Analysis of various photon migration pathways through the head also indicates how the source-detector configuration can be used to isolate photons interacting with deep regions of the brain that are inaccessible with current optical techniques.

physics.optics↗

arXiv2024

Polarization Purity and Dispersion Characteristics of Nested Antiresonant Nodeless Hollow-Core Optical Fiber at Near- and Short-wave-IR Wavelengths for Quantum Communications

Advancements in quantum communication and sensing require improved optical transmission that ensures excellent state purity and reduced losses. While free-space optical communication is often preferred, its use becomes challenging over long distances due to beam divergence, atmospheric absorption, scattering, and turbulence, among other factors. In the case of polarization encoding, traditional silica-core optical fibers, though commonly used, struggle with maintaining state purity due to stress-induced birefringence. Hollow core fibers, and in particular nested antiresonant nodeless fibers (NANF), have recently been shown to possess unparalleled polarization purity with minimal birefringence in the telecom wavelength range using continuous-wave (CW) laser light. Here, we investigate a 1-km NANF designed for wavelengths up to the 2-$μ$m waveband. Our results show a polarization extinction ratio between ~-30 dB and ~-70 dB across the 1520 to 1620 nm range in CW operation, peaking at ~-60 dB at the 2-$μ$m design wavelength. Our study also includes the pulsed regime, providing insights beyond previous CW studies, e.g., on the propagation of broadband quantum states of light in NANF at 2 $μ$m, and corresponding extinction-ratio-limited quantum bit error rates (QBER) for prepare-measure and entanglement-based quantum key distribution (QKD) protocols. Our findings highlight the potential of these fibers in emerging applications such as QKD, pointing towards a new standard in optical quantum technologies.

physics.optics↗

arXiv2024

Advances in quantum imaging

Modern imaging technologies are widely based on classical principles of light or electromagnetic wave propagation. They can be remarkably sophisticated, with recent successes ranging from single molecule microscopy to imaging far-distant galaxies. However, new imaging technologies based on quantum principles are gradually emerging. They can either surpass classical approaches or provide novel imaging capabilities that would not otherwise be possible. {Here }we provide an overview {of the most recently developed quantum imaging systems, highlighting the non-classical properties of sources such as bright squeezed light, entangled photons, and single-photon emitters that enable their functionality.} We outline potential upcoming trends and the associated challenges, all driven by a central inquiry, which is to understand whether quantum light can make visible the invisible.

quant-ph↗