arXiv subjects

Enrico Russo

Publications and source records attributed to Enrico Russo.

At least 19 recordsLinked to original sources

arXiv2026

Formal, Executable and Explainable Runtime Monitoring of Spoken Air Traffic Control Operational Procedures

Air traffic control procedures are executed through spoken exchanges between controllers and pilots. These interactions are essential to the safety of air transportation: failures in their execution can create severe operational hazards, as evidenced by past fatal accidents. Assessing whether an instruction has been followed requires relating what was said to the aircraft concerned, its state, and the obligations that pilots must meet. We present a runtime verification framework that monitors such procedures by checking controller-pilot exchanges, surveillance data, and onboard observations. The framework parses radio communications into events linked to the entities they concern and merges them with surveillance and onboard observations into a time-stamped trace. The ICAO-derived obligations as formalized as temporal formulas with explicit time bounds and evaluated over execution traces. Every violation is reported along with the breached obligations and the observations that support the verdict. With real traffic, the complete pipeline reaches an F1 of 0.85 against blind human-annotated violations; in 1,495 synthetic situations derived from two public corpora, the monitor logic returns the expected verdict in every case. In two historical accidents reconstructed from official investigation reports, the monitor identifies the same procedural deviations documented by the investigators.

cs.AI↗

arXiv2026

TRACE-CTI: Auditable Post-Extraction Governance of TTP Claims with Knowledge Graphs

Security Operations Centers increasingly rely on automated mapping of Cyber Threat Intelligence reports to MITRE ATT&CK, yet extractor outputs remain fallible and are often stored without the evidence, provenance, and validation history needed to decide whether an individual mapping should be trusted. We present TRACE- CTI, a post-extraction claim-governance framework that preserves run-level Predictions, aggregates them into configuration-level GraphAssertions, materializes setup-deduplicated corroboration as ConsensusAssertions, and exposes only GraphAssertions backed by policy-compliant validation grounds. The framework retains native evidence granularity, complete extraction provenance, versioned trust decisions, and non-destructive revocation history. We evaluate TRACE-CTI on two public CTI corpora comprising 65 reports and 5,303 sentences, using a controlled 2 x 3 matrix of retrievers and generator families, incrementally ingested across six GraphVersions. All setups are incorporated without schema modification; provenance paths remain complete, operational scopes remain disjoint, and every trusted GraphAssertion has an active qualifying validation ground. Cross-generator-family setup pairs exhibit greater output diversity than same-family pairs. At the final graph state, increasing setup support from k >= 1 to six-setup unanimity raises gold-aligned precision from 25.3% to 90.6%, while recall decreases from 88.2% to 16.3%. The graph also directly answers seven questions about provenance, trust, versioning, dependency, disagreement, and review-queue that the evaluated minimal flat output cannot fully answer without enrichment or reprocessing. These results support explicit, auditable governance of extracted TTP claims; the observed corroboration trajectory is descriptive and does not establish statistical independence or a causal model-family effect.

cs.AI↗

arXiv2026

COSMA: Communication-aware Optimization of Fermionic Simulation Kernels for Modular Quantum Architectures

Quantum simulation is a leading application of quantum computing, but scaling to chemically relevant problems requires modular architectures composed of interconnected quantum processing units. In such systems, inter-core quantum communication becomes a major performance bottleneck. In this work, we present COSMA, a communication-aware compilation framework for fermionic simulation kernels targeting modular quantum architectures. Our approach jointly optimizes fermion-to-qubit mapping, Pauli scheduling, and qubit allocation to minimize inter-core state transfers. Evaluated on molecular benchmarks, COSMA achieves up to $2.5\times$ reduction in communication cost compared to state-of-the-art baselines, with a median improvement of $1.7\times$. These results demonstrate that cross-layer co-design is essential for efficient and scalable quantum simulation on multi-core quantum hardware.

quant-ph↗

arXiv2026

MATCHA: Efficient Deployment of Deep Neural Networks on Multi-Accelerator Heterogeneous Edge SoCs

Deploying DNNs on System-on-Chips (SoC) with multiple heterogeneous acceleration engines is challenging, and the majority of deployment frameworks cannot fully exploit heterogeneity. We present MATCHA, a unified DNN deployment framework that generates highly concurrent schedules for parallel, heterogeneous accelerators and uses constraint programming to optimize L3/L2 memory allocation and scheduling. Using pattern matching, tiling, and mapping across individual HW units enables parallel execution and high accelerator utilization. On the MLPerf Tiny benchmark, using a SoC with two heterogeneous accelerators, MATCHA improves accelerator utilization and reduces inference latency by up to 35% with respect to the the state-of-the-art MATCH compiler.

cs.DC↗

arXiv2026

Gravitational Decoherence Estimation in Optomechanical Systems

We develop a comprehensive quantum estimation framework to quantify how precisely gravitationally induced decoherence can be inferred in optomechanical systems, using single-mode Gaussian probe states. Our approach combines a microscopic description of the gravitational diffusion mechanism with quantum Fisher information to determine the ultimate sensitivity achievable in principle. We show that gravitational diffusion leaves distinct, measurable signatures in the mechanical state, both during transient evolution and in the stationary regime. Finally, we identify how probe state preparation shapes the attainable precision, thereby establishing fundamental limits for detecting and estimating gravity-driven decoherence.

quant-ph↗

arXiv2026

CHAOS: Controlled Hardware fAult injectOr System for gem5

Fault injectors are essential tools for evaluating the reliability and resilience of computing systems. They enable the simulation of hardware and software faults to analyze system behavior under error conditions and assess its ability to operate correctly despite disruptions. Such analysis is critical for identifying vulnerabilities and improving system robustness. CHAOS is a modular, open-source, and fully configurable fault injection framework designed for the gem5 simulator. It facilitates precise and systematic fault injection across multiple architectural levels, supporting comprehensive evaluations of fault tolerance mechanisms and resilience strategies. Its high configurability and seamless integration with gem5 allow researchers to explore a wide range of fault models and complex scenarios, making CHAOS a valuable tool for advancing research in dependable and high-performance computing systems.

cs.AR↗

arXiv2025

Anomalous heat flow and quantum Otto cycle with indefinite causal order

The principle that heat spontaneously flows from higher temperature to lower temperature is a cornerstone of classical thermodynamics, often assumed to be independent of the sequence of interactions. While this holds true for macroscopic systems at equilibrium, here we show that, when the order of interactions between two identical thermalization channels is indefinite, an anomalous heat flow emerges, whereby heat can sometime flow from a colder entity to a hotter one. Taking advantage of this anomalous heat flow, we design a quantum Otto cycle with indefinite causal order, which not only achieves refrigeration but also generates work. The anomalous heat flow and the quantum Otto cycle are experimentally simulated in a photonic quantum setup, which provides a proof-of-principle demonstration of the theory.

quant-ph↗

arXiv2025

Instruction-Directed MAC for Efficient Classical Communication in Scalable Multi-Chip Quantum Systems

Scalable quantum computing requires modular multi-chip architectures integrating multiple quantum cores interconnected through quantum-coherent and classical links. The classical communication subsystem is critical for coordinating distributed control operations and supporting quantum protocols such as teleportation. In this work, we consider a realization based on a wireless network-on-chip for implementing classical communication within cryogenic environments. Traditional token-based medium access control (MAC) protocols, however, incur latency penalties due to inefficient token circulation among inactive nodes. We propose the instruction-directed token MAC (ID-MAC), a protocol that leverages the deterministic nature of quantum circuit execution to predefine transmission schedules at compile time. By embedding instruction-level information into the MAC layer, ID-MAC restricts token circulation to active transmitters, thereby improving channel utilization and reducing communication latency. Simulations show that ID-MAC reduces classical communication time by up to 70% and total execution time by up to 30-70%, while also extending effective system coherence. These results highlight ID-MAC as a scalable and efficient MAC solution for future multi-chip quantum architectures.

quant-ph↗

arXiv2025

Assessing the Role of Communication in Modular Multi-Core Quantum Systems

The scalability of quantum computing is constrained by the physical and architectural limitations of monolithic quantum processors. Modular multi-core quantum architectures, which interconnect multiple quantum cores (QCs) via classical and quantum-coherent links, offer a promising alternative to address these challenges. However, transitioning to a modular architecture introduces communication overhead, where classical communication plays a crucial role in executing quantum algorithms by transmitting measurement outcomes and synchronizing operations across QCs. Understanding the impact of classical communication on execution time is therefore essential for optimizing system performance. In this work, we introduce \qcomm, an open-source simulator designed to evaluate the role of classical communication in modular quantum computing architectures. \qcomm{} provides a high-level execution and timing model that captures the interplay between quantum gate execution, entanglement distribution, teleportation protocols, and classical communication latency. We conduct an extensive experimental analysis to quantify the impact of classical communication bandwidth, interconnect types, and quantum circuit mapping strategies on overall execution time. Furthermore, we assess classical communication overhead when executing real quantum benchmarks mapped onto a cryogenically-controlled multi-core quantum system. Our results show that, while classical communication is generally not the dominant contributor to execution time, its impact becomes increasingly relevant in optimized scenarios -- such as improved quantum technology, large-scale interconnects, or communication-aware circuit mappings. These findings provide useful insights for the design of scalable modular quantum architectures and highlight the importance of evaluating classical communication as a performance-limiting factor in future systems.

quant-ph↗

arXiv2025

TeleSABRE: Layout Synthesis in Multi-Core Quantum Systems with Teleport Interconnect

Quantum circuit compilation and, in particular, efficient qubit layout synthesis is a critical challenge in modular, multi-core quantum architectures with constrained interconnects. In this work, we extend the SABRE heuristic algorithm to develop TeleSABRE, a layout synthesis approach tailored for architectures featuring teleportation-based interconnects. Unlike standard SABRE, which only introduces SWAP operations for qubit movement, TeleSABRE integrates both intracore SWAPs and teleportation-based techniques leveraging qubit teleportation and gate teleportation across cores. This enables more efficient circuit execution by reducing both inter-core communication overhead and the number of intra-core SWAPs required to allow teleportation protocols and local gate executions. Experimental results demonstrate that TeleSABRE achieves 28% reduction across various benchmarks in terms of inter-core operations while also taking into account the logistics of the teleport protocols.

quant-ph↗

arXiv2024

A Data-Driven Approach to Dataflow-Aware Online Scheduling for Graph Neural Network Inference

Graph Neural Networks (GNNs) have shown significant promise in various domains, such as recommendation systems, bioinformatics, and network analysis. However, the irregularity of graph data poses unique challenges for efficient computation, leading to the development of specialized GNN accelerator architectures that surpass traditional CPU and GPU performance. Despite this, the structural diversity of input graphs results in varying performance across different GNN accelerators, depending on their dataflows. This variability in performance due to differing dataflows and graph properties remains largely unexplored, limiting the adaptability of GNN accelerators. To address this, we propose a data-driven framework for dataflow-aware latency prediction in GNN inference. Our approach involves training regressors to predict the latency of executing specific graphs on particular dataflows, using simulations on synthetic graphs. Experimental results indicate that our regressors can predict the optimal dataflow for a given graph with up to 91.28% accuracy and a Mean Absolute Percentage Error (MAPE) of 3.78%. Additionally, we introduce an online scheduling algorithm that uses these regressors to enhance scheduling decisions. Our experiments demonstrate that this algorithm achieves up to $3.17\times$ speedup in mean completion time and $6.26\times$ speedup in mean execution time compared to the best feasible baseline across all datasets.

cs.LG↗

arXiv2024

Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures

Modular, distributed and multi-core architectures are currently considered a promising approach for scalability of quantum computing systems. The integration of multiple Quantum Processing Units necessitates classical and quantum-coherent communication, introducing challenges related to noise and quantum decoherence in quantum state transfers between cores. Optimizing communication becomes imperative, and the compilation and mapping of quantum circuits onto physical qubits must minimize state transfers while adhering to architectural constraints. The compilation process, inherently an NP-hard problem, demands extensive search times even with a small number of qubits to be solved to optimality. To address this challenge efficiently, we advocate for the utilization of heuristic mappers that can rapidly generate solutions. In this work, we propose a novel approach employing Deep Reinforcement Learning (DRL) methods to learn these heuristics for a specific multi-core architecture. Our DRL agent incorporates a Transformer encoder and Graph Neural Networks. It encodes quantum circuits using self-attention mechanisms and produce outputs through an attention-based pointer mechanism that directly signifies the probability of matching logical qubits with physical cores. This enables the selection of optimal cores for logical qubits efficiently. Experimental evaluations show that the proposed method can outperform baseline approaches in terms of reducing inter-core communications and minimizing online time-to-solution. This research contributes to the advancement of scalable quantum computing systems by introducing a novel learning-based heuristic approach for efficient quantum circuit compilation and mapping.

quant-ph↗

arXiv2024

Assessing the Role of Communication in Scalable Multi-Core Quantum Architectures

Multi-core quantum architectures offer a solution to the scalability limitations of traditional monolithic designs. However, dividing the system into multiple chips introduces a critical bottleneck: communication between cores. This paper introduces qcomm, a simulation tool designed to assess the impact of communication on the performance of scalable multi-core quantum architectures. Qcomm allows users to adjust various architectural and physical parameters of the system, and outputs various communication metrics. We use qcomm to perform a preliminary study on how these parameters affect communication performance in a multi-core quantum system.

quant-ph↗

arXiv2024

Deep Reinforcement Learning based Online Scheduling Policy for Deep Neural Network Multi-Tenant Multi-Accelerator Systems

Currently, there is a growing trend of outsourcing the execution of DNNs to cloud services. For service providers, managing multi-tenancy and ensuring high-quality service delivery, particularly in meeting stringent execution time constraints, assumes paramount importance, all while endeavoring to maintain cost-effectiveness. In this context, the utilization of heterogeneous multi-accelerator systems becomes increasingly relevant. This paper presents RELMAS, a low-overhead deep reinforcement learning algorithm designed for the online scheduling of DNNs in multi-tenant environments, taking into account the dataflow heterogeneity of accelerators and memory bandwidths contentions. By doing so, service providers can employ the most efficient scheduling policy for user requests, optimizing Service-Level-Agreement (SLA) satisfaction rates and enhancing hardware utilization. The application of RELMAS to a heterogeneous multi-accelerator system composed of various instances of Simba and Eyeriss sub-accelerators resulted in up to a 173% improvement in SLA satisfaction rate compared to state-of-the-art scheduling techniques across different workload scenarios, with less than a 1.5% energy overhead.

cs.AR↗

arXiv2024

Towards Fair and Firm Real-Time Scheduling in DNN Multi-Tenant Multi-Accelerator Systems via Reinforcement Learning

This paper addresses the critical challenge of managing Quality of Service (QoS) in cloud services, focusing on the nuances of individual tenant expectations and varying Service Level Indicators (SLIs). It introduces a novel approach utilizing Deep Reinforcement Learning for tenant-specific QoS management in multi-tenant, multi-accelerator cloud environments. The chosen SLI, deadline hit rate, allows clients to tailor QoS for each service request. A novel online scheduling algorithm for Deep Neural Networks in multi-accelerator systems is proposed, with a focus on guaranteeing tenant-wise, model-specific QoS levels while considering real-time constraints.

cs.AR↗

arXiv2024

Bilateral photon emission from a vibrating mirror and multiphoton entanglement generation

Entanglement plays a crucial role in the development of quantum-enabled devices. One significant objective is the deterministic creation and distribution of entangled states, achieved, for example, through a mechanical oscillator interacting with confined electromagnetic fields. In this study, we explore a cavity resonator containing a two-sided perfect mirror. Although the mirror separates the cavity modes into two independent confined electromagnetic fields, the radiation pressure interaction gives rise to high-order effective interactions across all subsystems. Depending on the chosen resonant conditions, which are also related to the position of the mirror, we study $2n$-photon entanglement generation and bilateral photon pair emission. Demonstrating the non-classical nature of the mechanical oscillator, we provide a pathway to control these phenomena, opening potential applications in quantum technologies. Looking ahead, similar integrated devices could be used to entangle subsystems across vastly different energy scales, such as microwave and optical photons.

quant-ph↗

arXiv2023

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

Given their increasing size and complexity, the need for efficient execution of deep neural networks has become increasingly pressing in the design of heterogeneous High-Performance Computing (HPC) and edge platforms, leading to a wide variety of proposals for specialized deep learning architectures and hardware accelerators. The design of such architectures and accelerators requires a multidisciplinary approach combining expertise from several areas, from machine learning to computer architecture, low-level hardware design, and approximate computing. Several methodologies and tools have been proposed to improve the process of designing accelerators for deep learning, aimed at maximizing parallelism and minimizing data movement to achieve high performance and energy efficiency. This paper critically reviews influential tools and design methodologies for Deep Learning accelerators, offering a wide perspective in this rapidly evolving field. This work complements surveys on architectures and accelerators by covering hardware-software co-design, automated synthesis, domain-specific compilers, design space exploration, modeling, and simulation, providing insights into technical challenges and open research directions.

cs.AR↗

arXiv2023

Automating the Generation of Cyber Range Virtual Scenarios with VSDL

A cyber range is an environment used for training security experts and testing attack and defence tools and procedures. Usually, a cyber range simulates one or more critical infrastructures that attacking (red) and defending (blue) teams must compromise and protect, respectively. The infrastructure can be physically assembled, but much more convenient is to rely on the Infrastructure as a Service (IaaS) paradigm. Although some modern technologies support the IaaS, the design and deployment of scenarios of interest is mostly a manual operation. As a consequence, it is a common practice to have a cyber range hosting few (sometimes only one), consolidated scenarios. However, reusing the same scenario may significantly reduce the effectiveness of the training and testing sessions. In this paper, we propose a framework for automating the definition and deployment of arbitrarily complex cyber range scenarios. The framework relies on the virtual scenario description language (VSDL), i.e., a domain-specific language for defining high-level features of the desired infrastructure while hiding low-level details. The semantics of VSDL is given in terms of constraints that must be satisfied by the virtual infrastructure. These constraints are then submitted to an SMT solver for checking the satisfiability of the specification. If satisfiable, the specification gives rise to a model that is automatically converted to a set of deployment scripts to be submitted to the IaaS provider.

cs.CR↗