Paolo Monti

As quantum computers scale, single-chip architectures face inherent limitations in qubit count. This drives the need for modular quantum computing and quantum data centers (QDCs), where multiple quantum processor units (QPUs) are interconnected to enable the distributed execution of a quantum algorithm. However, evaluating distributed quantum computing (DQC) architectures is challenging. Classical simulation is limited by the exponential growth of the state vector, limiting its ability to model large systems and realistically capture hardware noise and timing. Meanwhile, implementing QDC introduces interconnect noise challenges such as transduction inefficiency and optical fiber loss. In this work, we introduce a hardware-based emulation framework by partitioning a single quantum processor’s qubit coupling map into multiple logical QPUs. We show how noise arising from transduction and optical fibers can be modeled by adding an ancilla qubit representing the environment, based on quantum collision dynamics. This model is then translated into a gate-based circuit, in which the couplings between each portion act as controllable noisy quantum communication channels. We demonstrate the framework on IBM quantum hardware by executing remote gates under controllable communication noise. To highlight the flexibility of the platform, we further replicate the implementation results of the distributed Grover’s search algorithm on an ion-trap system. Finally, we test a larger circuit, i.e., a five-qubit quantum Fourier transform (QFT), achieving reasonable fidelity across logical QPUs. Overall, the framework provides a scalable hardware-level emulation platform that captures noise sources through physical qubits and is compatible with any platform supporting the Qiskit SDK.

Multi-band elastic optical networks (MB-EONs) transmit information in multiple bands to increase the available capacity. However, they suffer from quality of transmission (QoT) degradation caused by the inter-channel stimulated Raman scattering effect, which requires addressing through tailored resource assignment. Additionally, dynamically arriving and departing optical service requests generate spectrum fragmentation (SF), where spectrum resources become scattered into non-continuous chunks and aggravate service blocking ratio (SBR) even when the total available bandwidth is sufficient. To jointly address these challenges, we propose an SF- and QoT-aware algorithm for routing, band, modulation format, and spectrum assignment (RBMSA), along with proactive spectrum defragmentation (SD), referred to as SFQA-defrag. The algorithm considers SF metrics and QoT levels of available channels across multiple candidate paths to ensure that the QoT requirements are met while minimizing the SF. The SD process proactively reorganizes spectrum allocation to reduce fragmentation by consolidating the spectrum gaps, which leads to lower blocking of future requests. The SFQA-defrag algorithm is evaluated against benchmark algorithms that independently consider either QoT or SF in three reference backbone topologies. The results demonstrate that SFQA-defrag significantly reduces the SBR and SF compared to benchmarks, albeit with a slight increase in the average path length.

We investigate energy-efficient, traffic-adaptive resource provisioning for 6G x-haul networks by integrating a reconfigurable low-loss switching sub-system (referred to as an interlacer) into the TEFNET24 reference network topology from Telefónica. The interlacer combined with the digital subcarrier multiplexing (DSCM) transceivers forms a WDM-PON approach, enabling subcarrier-level switching at the network access, providing lower distribution loss than conventional power splitter (PS)-based TDMA-PON approaches. Moreover, under the practically low crosstalk levels reported for our interlacer implementations, the reduced distribution loss can be traded for increased modulation cardinality (e.g., 16QAM to 64QAM), allowing for reducing the number of allocated transceivers. We further examine a machine learning (ML)-driven framework for dynamic capacity provisioning and validate it using a real-world operator trace mapped onto the national-scale TEFNET24 topology, capturing 4G/5G load patterns with projections toward 6G. We compare static, semi-static, and fully dynamic strategies and show that ML-aided dynamic provisioning reduces transceiver overallocation by up to 63.7% relative to static methods, with the strongest gains during off-peak hours when traffic variability is highest. Finally, we quantify two complementary energy-saving mechanisms enabled by dynamic operation—subcarrier deactivation and transceiver sleep mode—with the latter yielding up to 21% total network energy savings while maintaining quality of service under time-varying demand.

The evolution of passive optical networks (PONs) is rapidly progressing, with the next generation aiming to surpass 100Gb/s/ λ and target 200Gb/s/ λ —a typical fourfold increase over the current 50Gb/s/ λ power splitter (PS)-based PON standard. This paper outlines current research and development challenges in PONs and explores potential advancements using programmable photonic Nyquist-shaped Interleaver (NS-INT) filters paired with coherent point-to-multipoint (P2MP) digital subcarrier multiplexing (DSCM) transceivers. Specifically, we evaluated, based on analytical estimations and simulations, two NS-INT implementations for mobile transport: one based on ring resonators-assisted filters and a second utilizing an optical 18-tap cascaded asymmetric Mach–Zehnder Interferometer (aMZI). We compare their effectiveness in optimizing filtering and minimizing crosstalk (XT) across various coupling coefficient scenarios. Furthermore, we assess the feasibility of these designs by examining their performance under XT and signal impairment caused by filter narrowing effects, particularly in the presence of signal-to-filter center frequency misalignment. Bit error rate (BER) evaluations demonstrate a tolerance to frequency misalignments of up to 3 GHz across all architectures. Among them, our cascaded aMZI design exhibits superior performance, attributed to its inherent optical finite impulse response (FIR) filter characteristics as a minimum-phase filter.

Quantum key distribution (QKD) networks offer scalable secure communication, while efficient integration with existing optical infrastructure requires careful consideration. This paper investigates enhancing the security of multipath QKD over multi-band systems by exploring the trade-offs between multi-band separation and multipath techniques. A novel, to our knowledge, framework, incorporating blocking ratio analysis for a single and multipath QKD, is proposed, comparing bitwise product and concatenated key generation methods. Our model considers propagation delays and key pool synchronization’s impact on the secret key rate (SKR). Our simulations using a U.S. long-haul network model demonstrate significant benefits of implementing QKD in alternative spectral bands. The results show substantial improvements in SKR at various span lengths in single-path scenarios. Additionally, increasing the number of quantum channels led to noticeable reductions in network blocking rates, enabling higher classical traffic loads throughout the network infrastructure. We introduce, to our knowledge, new KPIs: the blocking rate ( B _CQ ) and a comprehensive security rate ( C ), assessing the final key’s overall secrecy. For multipath schemes, simulations reveal that concatenated multipath QKD, while exhibiting a superior blocking rate, showed 12% less compromised secrecy at 4000 km (32% to 20%), compared to less than 10% for bitwise product multipath QKD under the same conditions. These findings provide valuable insights into designing efficient and secure quantum-enhanced optical networks, highlighting the complex interplay between security and efficiency in multipath QKD architectures.

The operational security of latency-sensitive networked applications is increasingly threatened by evolving malicious attacks that compromise operational integrity and network performance. Human-to-machine (H2M) applications, which rely on seamless bidirectional control signals and haptic feedback transmission, exemplify such latency-sensitive use cases. Existing learning-based malicious attack detection frameworks suffer from their reliance on pretrained datasets, making machine learning (ML) models within them ineffective against previously unseen attack patterns. As attack profiles dynamically evolve, static models become obsolete, necessitating adaptive mechanisms to maintain detection accuracy. In this context, concept drift adaptation will serve as a critical tool for enabling models to continuously adjust to changing traffic distributions and emerging attack patterns. However, real-world H2M applications lack access to accurately labeled malicious traffic data, making real-time adaptation of defense mechanisms infeasible. To address these challenges, we propose a concept drift adaptation-facilitated malicious attack defense (CDAD) framework. First, CDAD employs adaptive random forest (ARF) as an incremental learning approach, integrating an error-rate-based concept drift detection mechanism to dynamically identify evolving attack patterns and trigger adaptive model updates. Second, a haptic behavior classifier is introduced to classify expected human operator interactions and compare them with real-time haptic feedback from remote machines. This enables automated traffic relabeling, allowing CDAD to adapt to previously unseen attacks without relying on prelabeled datasets. The superior performance of CDAD over existing state-of-the-art methods is demonstrated across various malicious attack scenarios through extensive simulations. Results show that with CDAD, the attack success rate can be limited to 3% while maintaining an inference time below 1 ms, thereby ensuring effective and efficient malicious attack defense in latency-sensitive H2M applications.

Machine learning (ML) is emerging as a promising tool for estimating the Quality of Transmission (QoT) in optical networks, especially for unestablished lightpaths where traditional methods are limited. However, inaccuracies in ML-based QoT predictions—typically expressed in terms of generalized signal-to-noise ratio (GSNR)—can significantly affect network operation. Overestimation may lead to retransmissions due to overly aggressive modulation format choices, while underestimation results in underutilized spectral resources. To address this, we propose a novel ML architecture that incorporates an embedding layer for link-level features alongside path-and service-level inputs. Using data generated from an accurate analytical model, we show that our approach reduces prediction error by up to 34% compared to standard architectures. Simulated deployment scenarios further demonstrate operational benefits, with a 15.9% decrease in incorrect and a 34.8% reduction in overly conservative modulation format selections.

The extension of elastic optical network (EON) technologies to multi-band transmission (MB-EON) promises enhanced spectral efficiency, greater throughput, and long-term cost benefits for telecom operators. However, designing such networks presents challenges, particularly in optimizing physical parameters like optical power and quality of transmission (QoT) across different frequency bands. This paper introduces a methodology for optimal span-by-span power allocation using two hyper-accelerated power optimization (HPO) modes: flat launch power (FLP) and flat received power (FRP). This methodology significantly accelerate network power optimization while ensuring service stability in scenarios such as changes in network parameters, QoT degradation due to aging, and network re-optimization or upgrading. Through a comprehensive comparison, we find that FRP notably improves signal flatness and GSNR/OSNR, particularly in the S-band, contributing to a network-wide throughput increase in the order of 12% to 75%. Additionally, we demonstrate that HPO applied to global power optimization is simpler and more cost-effective than when applied to local methods for large-scale networks.