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Francesco Da Ros

Publications and source records attributed to Francesco Da Ros.

At least 19 recordsLinked to original sources
arXiv2026

Photonic Time-Delayed Quantum Extreme Learning Machine

We propose and numerically simulate a photonic quantum extreme learning machine (QELM) based on non-linear interactions in a time-delay non-linear interferometer with photon-number-resolving detection. We theoretically demonstrate the reservoir's computational capability by binary classification of the non-linear two moons benchmark. Analysis of the learned output weights and alternative quantum readouts shows that compact feature sets based on photon-number correlations preserve classification accuracy while potentially reducing measurement complexity. We further show that the QELM maintains its classification performance under the effect of losses by naturally redistributing the learned weights across a larger set of available features. These results demonstrate the resilience of the time-delay photonic implementation and highlight its potential as an experimentally accessible QELM platform.

quant-ph↗
arXiv2026

Fully Distributed Fiber-Optic Sensing Enabled by Kalman Filtering

Signal fading creates points along the fiber where phase cannot be extracted, so they are conventionally discarded. Instead, we propose a Kalman-based solution for ϕ-OTDR full-fiber monitoring. Experiments demonstrate phase and temperature estimation with approximately 15 times better spatial density than if removing these points.

physics.optics↗
arXiv2026

Sensitivity Limits for $ϕ$-OTDR

We demonstrate a new lower bound on the sensitivity of $ϕ$-OTDR by estimating phase using Kalman filtering. Numerical analysis shows a $\sim$35-dB reduction in the minimum detectable SNR, compared to when using conventional phase estimation.

physics.optics↗
arXiv2026

Beyond white- and black-box modeling tools in optical communications and optical computing: physics-informed data-driven modeling

Efficient optimization and control of photonic computing and communication systems increasingly rely on accurate surrogate models/digital twins. While data-driven models may achieve faster inference than traditional physics-based methods, they typically suffer from poor training data efficiency and limited generalizability. To address this trade-off, physics-informed data-driven modeling has emerged as a powerful hybrid paradigm. This paper presents a comparative analysis of these three modeling paradigms across three benchmark use cases: optical amplifiers, directly modulated lasers, and interferometer meshes. By evaluating model complexity, data efficiency, generalizability, and modularity, this work provides a detailed analysis of the respective trade-offs and highlights the advantages of combining physical insight with data-driven learning.

physics.optics↗
arXiv2026

Fundamentals of Optical Fiber Sensing Schemes Based on Coherent Optical Time Domain Reflectometry: Signal Under Dynamic Temperature Conditions

We present a theoretical, algorithmic, and experimental study of temperature sensing using $ϕ$-OTDR with coherent detection. A physics-based model is developed to relate the measured Rayleigh backscattered signal to temperature variations along the fiber, showing that the phase evolution encodes the cumulative temperature change between the interrogator and the sensing location, while the amplitude exhibits only local sensitivity. Based on this insight, we propose robust algorithms for temperature-event detection and temperature-profile reconstruction. Experimental results demonstrate reliable recovery of temperature-induced perturbations in standard single-mode fibers using coherently detected $ϕ$-OTDR.

physics.optics↗
arXiv2026

On-chip semi-device-independent quantum random number generator exploiting contextuality

We present a semi-device-independent quantum random number generator (QRNG) based on the violation of a contextuality inequality, implemented by the integration of two silicon photonic chips. Our system combines a heralded single-photon source with a reconfigurable interferometric mesh to implement qutrit state preparation, transformations, and measurements suitable for testing a KCBS contextuality inequality. This architecture enables the generation of random numbers from the intrinsic randomness of single-photon interference in a complex optical network, while simultaneously allowing a quantitative certification of their security without requiring entanglement. We observe a contextuality violation exceeding the classical bound by more than 10σ, unambiguously confirming non-classical behavior. From this violation, we certify a conditional min-entropy per experimental round of Hmin = 0.077 +- 0.002, derived via a tailored semidefinite-programming-based security analysis. Each measurement outcome therefore contains at least 0.077 +- 0.002 bits of extractable genuine randomness, corresponding to an asymptotic generation rate of 21.7 +- 0.5 bits/s. These results establish a viable route towards general-purpose, untrusted quantum random number generators compatible with practical integrated photonic quantum networks.

quant-ph↗
arXiv2026

Chip-to-chip entanglement distribution over 80-km multicore fiber link

Long-range quantum entanglement is essential for building large-scale quantum networks and unconditionally secure cryptographic systems based on quantum key distribution (QKD). While photonic integrated circuits offer a highly scalable platform, the fragility of phase coherence between spatial modes has prevented the distribution of path-encoded entanglement over long distances. Here, we report chip-to-chip distribution of path-encoded entangled states over 80 km between fully integrated silicon photonic transmitter and receiver chips. Telecom-band entangled photon pairs are generated via spontaneous four-wave mixing in on-chip spiral waveguides and distributed between chips over a dual-core, actively stabilized fiber link. Upon distribution, we measure a Bell state fidelity of $85.7 \pm 0.2 \%$. Implementing the BBM92 protocol with the same source, we obtain a secure key rate of 2.03 bit/s in the infinite-key regime. These results establish silicon photonic chips as a viable platform for long-distance path-encoded entanglement-based quantum key distribution, paving the way toward scalable, device-independent quantum networks.

quant-ph↗
arXiv2026

Experimental Analysis of a Self-Coherent M-QAM Receiver by Means of Recurrent Optical Spectrum Slicing and Direct Detection

High order modulation formats constitute the most prominent way for increasing spectral efficiency in transmission systems. Coherent transceivers that support such higher order formats require heavy digital signal processing (DSP), which increases the power consumption of coherent pluggables, well above the intensity modulation and direct detection (IM/DD) counterparts. Self-coherent or phase retrieval methods have emerged as potential solutions, trying to combine the merits of coherent technology with the simplicity of direct detection. In this work, we experimentally demonstrate the reception of quadrature amplitude modulation (QAM) modulation formats based on direct detection aided by the recurrent optical spectrum slicing (ROSS) photonic accelerator, utilizing minimal DSP and low modulator driving voltages. We experimentally demonstrate 32 Gbaud QAM-4/16 for 25 km, 50 km and 75 km in the C-band aided by a linear digital equalization and the use of programmable photonics as recurrent optical spectrum slicers. We showcase successful detection with driving swings below Vπ/3 in contrast to the full swing required by conventional coherent transceivers. We further improve the system performance utilizing geometric constellation shaping. Finally, we explore the potential power consumption improvement for the next-generation 1.6T pluggables, showcasing over 40% reduction with respect to the most lightweight state of the art coherent solutions reported in literature

physics.optics↗
arXiv2026

Distributed Coherent Optical Computing via Injection-Locked Photonic Networks

Coherent photonic computing uses both the phase and amplitude of light to implement linear operations such as dot products and matrix multiplication but requires phase stability between the interfering paths. This poses a challenge for such strategies when optical data is generated at a remote source due to environmental phase variations in fiber. Conventional approaches to distributed computing rely on optical-to-electrical conversion and buffering, limiting truly real-time and distributed computation. Here, we propose a new strategy via optical injection locking to enable distributed, real-time coherent optical processing without unnecessary conversions in the optical-to-electrical or analog-to-digital domains. Using a semiconductor laser rate-equation model, we explore the conditions required for stable operation by sweeping the power injection ratio, frequency detuning, and modulation conditions of the remote and injected lasers. Our results indicate that higher injection powers broaden the locking margin but more readily exhibit frequency-selective features associated with relaxation oscillations and increased amplitude-phase mixing, whereas lower injection powers yield a narrower, but more predictable operating window which remains stable under large modulation depth. End-to-end symbol-sequence simulations with balanced detection and temporal integration further confirm that reducing the injection ratio suppresses residual remote-modulation components in the injected laser output and improves computational accuracy. Overall, our study provides guidance and design trade-offs for remote coherent detection and distributed coherent photonic computing enabled by injection locking.

physics.optics↗
arXiv2025

Experimental End-to-End Optimization of Directly Modulated Laser-based IM/DD Transmission

Directly modulated lasers (DMLs) are an attractive technology for short-reach intensity modulation and direct detection communication systems. However, their complex nonlinear dynamics make the modeling and optimization of DML-based systems challenging. In this paper, we study the end-to-end optimization of DML-based systems based on a data-driven surrogate model trained on experimental data. The end-to-end optimization includes the pulse shaping and equalizer filters, the bias current and the modulation radio-frequency (RF) power applied to the laser. The performance of the end-to-end optimization scheme is tested on the experimental setup and compared to 4 different benchmark schemes based on linear and nonlinear receiver-side equalization. The results show that the proposed end-to-end scheme is able to deliver better performance throughout the studied symbol rates and transmission distances while employing lower modulation RF power, fewer filter taps and utilizing a smaller signal bandwidth.

eess.SP↗
arXiv2025

Subspace tracking: a novel measurement method to test the standard phase noise model of optical frequency combs

The introduction of digital signal processing (DSP) assisted coherent detection has been a cornerstone of modern fiber-optic communication systems. The ability to digitally, i.e. after analogue-to-digital converter, compensate for chromatic dispersion, polarization mode dispersion, and phase noise has rendered traditional analog feedback loops largely obsolete. While analog techniques remain prevalent for phase noise characterization of single-frequency lasers, the phase noise characterization of optical frequency combs presents a greater challenge. This complexity arises from different number of phase noise sources affecting an optical frequency comb. Here, we show how a phase noise measurement techniques method based on multi-heterodyne coherent detection and DSP-based subspace tracking can be used to identify, measure and quantify various phase noise sources associated with an optical frequency comb.

physics.optics↗
arXiv2025

Impact of nonlinear spectral broadening on the phase noise properties of electro-optic frequency comb

Electro-optic modulation is an attractive approach for generating flat, stable, and low-noise optical frequency combs with relatively high power per comb line. However, a key limitation of electro-optic combs is the restricted number of comb lines imposed by the available RF source power. To overcome this limitation, a nonlinear spectral broadening stage is typically employed. The phase noise characteristics of an electro-optic comb are well described by the standard phase noise model, which depends on two parameters: the seed laser and the RF source phase noise. A fundamental question that arises is how nonlinear broadening processes affect the phase noise properties of the expanded comb. To address this, we employ coherent detection, digital signal processing, and subspace tracking. Our experimental results show that the nonlinearly broadened comb preserves the standard phase noise model of the input electro-optic comb. In other words, the nonlinear processes neither introduce additional phase noise terms nor amplify the existing contributions from the seed laser and RF source. Hence, nonlinear broadening can be viewed as equivalent to driving the electro-optic comb with a much higher RF modulation power.

physics.optics↗
arXiv2025

Reconfigurable Integrated Photonic Chips as Dual-Purpose Neuromorphic Accelerators and Physical Unclonable Functions

In this work, we experimentally validate the dual use of a reconfigurable photonic integrated mesh as a neuromorphic accelerator, targeting signal equalization, and as a physical unclonable function offering authentication at the hardware level. The processing node is an optical spectrum slicing self-coherent transceiver targeting the mitigation of dispersion impairments of an intensity detected QPSK signal, after 25 km of transmission at 32 Gbaud. Unavoidable fabrication related imperfections of the nodes, such as waveguide roughness, can act as fingerprints of the device, and, during neuromorphic processing, result in unique weights at the digital back-end during signal equalization. Extracted security metrics offer low false positive/negative probability for the generated responses, confirming un-clonability, whereas bit-error-ratio for the QPSK equalization task was always below the hardware forward error correction limit. The experimental results substantiate the capability of the proposed scheme to simultaneously act as an accelerator and as a security token.

physics.optics↗
arXiv2024

End-to-End Learning of Transmitter and Receiver Filters in Bandwidth Limited Fiber Optic Communication Systems

This paper investigates the application of end-to-end (E2E) learning for joint optimization of pulse-shaper and receiver filter to reduce intersymbol interference (ISI) in bandwidth-limited communication systems. We investigate this in two numerical simulation models: 1) an additive white Gaussian noise (AWGN) channel with bandwidth limitation and 2) an intensity modulated direct detection (IM/DD) link employing an electro-absorption modulator. For both simulation models, we implement a wavelength division multiplexing (WDM) scheme to ensure that the learned filters adhere to the bandwidth constraints of the WDM channels. Our findings reveal that E2E learning greatly surpasses traditional single-sided transmitter pulse-shaper or receiver filter optimization methods, achieving significant performance gains in terms of symbol error rate with shorter filter lengths. These results suggest that E2E learning can decrease the complexity and enhance the performance of future high-speed optical communication systems.

eess.SP↗
arXiv2024

Multi-task Photonic Reservoir Computing: Wavelength Division Multiplexing for Parallel Computing with a Silicon Microring Resonator

Nowadays, as the ever-increasing demand for more powerful computing resources continues, alternative advanced computing paradigms are under extensive investigation. Significant effort has been made to deviate from conventional Von Neumann architectures. In-memory computing has emerged in the field of electronics as a possible solution to the infamous bottleneck between memory and computing processors, which reduces the effective throughput of data. In photonics, novel schemes attempt to collocate the computing processor and memory in a single device. Photonics offers the flexibility of multiplexing streams of data not only spatially and in time, but also in frequency or, equivalently, in wavelength, which makes it highly suitable for parallel computing. Here, we numerically show the use of time and wavelength division multiplexing (WDM) to solve four independent tasks at the same time in a single photonic chip, serving as a proof of concept for our proposal. The system is a time-delay reservoir computing (TDRC) based on a microring resonator (MRR). The addressed tasks cover different applications: Time-series prediction, waveform signal classification, wireless channel equalization, and radar signal prediction. The system is also tested for simultaneous computing of up to 10 instances of the same task, exhibiting excellent performance. The footprint of the system is reduced by using time-division multiplexing of the nodes that act as the neurons of the studied neural network scheme. WDM is used for the parallelization of wavelength channels, each addressing a single task. By adjusting the input power and frequency of each optical channel, we can achieve levels of performance for each of the tasks that are comparable to those quoted in state-of-the-art reports focusing on single-task operation...

cs.NE↗