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Roberto Proietti

Publications and source records attributed to Roberto Proietti.

5 recordsLinked to original sources
arXiv2026

Polarization Tracking and Active Compensation Using Classical Headers in Quantum Wrapper Networking

Quantum wrapper networking (QWN) is an emerging quantum networking protocol that wraps qubits in classical header bits to enable switching/routing, monitoring, and control without detecting the quantum signal. In this work, we encode header bits with two nonorthogonal polarization references to track and actively compensate for the changing birefringence of a 48 km deployed fiber link. Our method is analytical and deterministic, using motorized waveplates and a variable phase retarder to accurately and stably compensate the channel. We verify successful compensation by measuring the polarization stability of single photon qubits and the visibility of entangled photon pairs under both slow birefringence drift due to environmental fluctuations and large sudden changes designed to emulate those that occur during packet switching and rerouting over different fiber paths. For large, sudden changes, our compensator recovers the Stokes vector of single photons to within 10 degrees of the target state on the Poincar\'e sphere and restores the two-photon interference visibilities to their original levels on the deployed fiber link. Additionally, experiments monitoring long-term compensation over 44 hours show that active polarization compensation maintains the two-photon interference visibility, whereas disabling the compensator leads to visibility degradation over time, particularly in the diagonal-antidiagonal basis, where the average raw visibility decreases by 7.7%. These results add a polarization-control layer to QWN and illustrate that information-carrying headers can enable deterministic physical-layer compensation in the quantum channel over long-distance deployed fiber links.

quant-ph↗
arXiv2023

Quantum Wrapper Networking

We introduce a new concept of Quantum Wrapper Networking, which enables control, management, and operation of quantum networks that can co-exist with classical networks while keeping the requirements for quantum networks intact. The quantum wrapper networks (QWNs) enable the transparent and interoperable transportation of quantum wrapper datagrams consisting of quantum payloads and, notably, classical headers to facilitate the datagram switching without measuring or disturbing the qubits of the quantum payload. Furthermore, QWNs can utilize the common network control and management for performance monitoring on the classical header and infer the quantum channel quality.

quant-ph↗
arXiv2021

Izhikevich-Inspired Optoelectronic Neurons with Excitatory and Inhibitory Inputs for Energy-Efficient Photonic Spiking Neural Networks

We designed, prototyped, and experimentally demonstrated, for the first time to our knowledge, an optoelectronic spiking neuron inspired by the Izhikevich model incorporating both excitatory and inhibitory optical spiking inputs and producing optical spiking outputs accordingly. The optoelectronic neurons consist of three transistors acting as electrical spiking circuits, a vertical-cavity surface-emitting laser (VCSEL) for optical spiking outputs, and two photodetectors for excitatory and inhibitory optical spiking inputs. Additional inclusion of capacitors and resistors complete the Izhikevich-inspired optoelectronic neurons, which receive excitatory and inhibitory optical spikes as inputs from other optoelectronic neurons. We developed a detailed optoelectronic neuron model in Verilog-A and simulated the circuit-level operation of various cases with excitatory input and inhibitory input signals. The experimental results closely resemble the simulated results and demonstrate how the excitatory inputs trigger the optical spiking outputs while the inhibitory inputs suppress the outputs. Utilizing the simulated neuron model, we conducted simulations using fully connected (FC) and convolutional neural networks (CNN). The simulation results using MNIST handwritten digits recognition show 90% accuracy on unsupervised learning and 97% accuracy on a supervised modified FC neural network. We further designed a nanoscale optoelectronic neuron utilizing quantum impedance conversion where a 200 aJ/spike input can trigger the output from on-chip nanolasers with 10 fJ/spike. The nanoscale neuron can support a fanout of ~80 or overcome 19 dB excess optical loss while running at 10 GSpikes/second in the neural network, which corresponds to 100x throughput and 1000x energy-efficiency improvement compared to state-of-art electrical neuromorphic hardware such as Loihi and NeuroGrid.

cs.NE↗
arXiv2019

DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks

This paper proposes DeepRMSA, a deep reinforcement learning framework for routing, modulation and spectrum assignment (RMSA) in elastic optical networks (EONs). DeepRMSA learns the correct online RMSA policies by parameterizing the policies with deep neural networks (DNNs) that can sense complex EON states. The DNNs are trained with experiences of dynamic lightpath provisioning. We first modify the asynchronous advantage actor-critic algorithm and present an episode-based training mechanism for DeepRMSA, namely, DeepRMSA-EP. DeepRMSA-EP divides the dynamic provisioning process into multiple episodes (each containing the servicing of a fixed number of lightpath requests) and performs training by the end of each episode. The optimization target of DeepRMSA-EP at each step of servicing a request is to maximize the cumulative reward within the rest of the episode. Thus, we obviate the need for estimating the rewards related to unknown future states. To overcome the instability issue in the training of DeepRMSA-EP due to the oscillations of cumulative rewards, we further propose a window-based flexible training mechanism, i.e., DeepRMSA-FLX. DeepRMSA-FLX attempts to smooth out the oscillations by defining the optimization scope at each step as a sliding window, and ensuring that the cumulative rewards always include rewards from a fixed number of requests. Evaluations with the two sample topologies show that DeepRMSA-FLX can effectively stabilize the training while achieving blocking probability reductions of more than 20.3% and 14.3%, when compared with the baselines.

cs.NI↗