Research Archive

Journal Article Open Access Quantum Computing

Variational Quantum Algorithms for Portfolio Optimization: Benchmarking QAOA and VQE Against Classical Solvers on Near-Term Quantum Hardware

Variational quantum algorithms -- particularly the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) -- have been widely proposed as near-term candidates for achieving quantum advantage in combinatorial optimization problems relevant to finance, including portfolio optimization, risk parity, and credit risk assessment. However, realistic benchmarking of these algorithms on current quantum hardware against state-of-the-art classical solvers has been limited by hardware access constraints and lack of standardized evaluation methodologies. This paper presents the most comprehensive empirical benchmarking study of QAOA and VQE for financial portfolio optimization to date, executing experiments on IBM Quantum Eagle (127-qubit), IBM Quantum Osprey (433-qubit), and IonQ Aria (25-qubit) processors across portfolio sizes from 10 to 50 assets, and comparing solution quality and runtime against Gurobi, D-Wave Advantage (quantum annealing), and simulated annealing baselines. Current QAOA implementations at p=3 circuit depth achieve mean solution quality 18.4% below Gurobi on 30-asset portfolios, with hardware noise as the dominant limiting factor. D-Wave Advantage outperforms gate-model QAOA for portfolio sizes below 40 assets on current hardware. We introduce a Quantum Finance Readiness Index (QFRI) that maps algorithm performance trajectories against projected hardware improvement curves, estimating practical quantum advantage emergence for 100-asset portfolios at approximately 2028-2031 under optimistic noise reduction scenarios. This work provides financial institutions with an honest evidence base for quantum computing investment planning.

Chukwuebuka Aneke, Astrid Svensson, Daisuke Nishimura, Laila Benali· Aug 2025· 89 citations

Journal Article Open Access Software Engineering

Observability 2.0: Continuous Profiling, eBPF-Based Telemetry, and the Emerging Observability Stack for Cloud-Native DevOps at Scale

The three-pillar observability model — logs, metrics, and distributed traces — which has guided cloud-native monitoring strategy for nearly a decade, is showing structural limitations in the face of highly dynamic, ephemeral, and high-cardinality workloads characteristic of modern Kubernetes-native systems. This paper examines the emergence of continuous profiling as the "fourth pillar" of observability and evaluates the broader shift toward eBPF-based kernel-level telemetry as a foundation for low-overhead, always-on instrumentation. We present an empirical evaluation of continuous profiling tools (Pyroscope, Parca, and Grafana Pyroscope) and eBPF-based observability frameworks (Cilium, Tetragon, and Pixie) in a production-equivalent Kubernetes environment running a 120-service microservices benchmark. Continuous profiling reduced mean time to identify CPU and memory regression root causes from 47 minutes to 9 minutes compared to metric-only approaches. eBPF-based network telemetry eliminated the 12–18% performance overhead of sidecar-based service mesh observability while providing equivalent visibility. We introduce the Observability Stack Completeness Score (OSCS) and map 23 production organizations against it, finding that fewer than 15% have adopted continuous profiling despite its measurable impact. We propose the Observability 2.0 Reference Architecture and provide migration guidance from traditional three-pillar stacks.

Kelechi Nwachukwu, Astrid Bergqvist, Ryosuke Kawamoto, Diana Fonseca· Aug 2025· 98 citations

Journal Article Open Access Network Engineering

Intent-Based Networking with Large Language Models: Natural Language Network Policy Specification, Automated Configuration Generation, and Closed-Loop Verification in Enterprise Campus Networks

Intent-Based Networking (IBN) -- which allows network operators to specify desired network behavior in high-level business terms and rely on automation to translate intent into device configurations -- has been a long-standing aspiration of network management research. The emergence of large language models with strong natural language understanding and code generation capabilities has created new opportunities for practical IBN implementation without the manual ontology engineering that constrained earlier approaches. This paper presents NetIntent, an LLM-powered IBN framework that translates natural language network policy statements into vendor-agnostic network configuration artifacts (Ansible playbooks, Cisco IOS-XE, Juniper JunOS) with closed-loop verification against the deployed network state. NetIntent employs a three-stage pipeline: intent parsing and formalization using GPT-4o with network domain-adapted prompting, configuration synthesis using retrieval-augmented generation over a vendor configuration knowledge base, and automated verification using network simulation and configuration diffing. Evaluation is conducted across a 200-device enterprise campus network testbed spanning switching, routing, security policy, and QoS configuration domains. NetIntent correctly translates 87.4 percent of natural language policy statements into syntactically and semantically correct configurations, with the remaining 12.6 percent requiring human clarification due to intent ambiguity. Mean configuration generation time of 23 seconds compares favorably to a baseline of 47 minutes for manual configuration by experienced network engineers. We analyze the failure modes and introduce NetIntent-Guard, a post-generation policy correctness verifier reducing misconfiguration escape rate by 94 percent.

Chidiebere Eze, Maja Karlsson, Takeshi Yamamoto, Nadia Benali· Apr 2025· 98 citations

Journal Article Open Access Artificial Intelligence

AI-Driven Infrastructure Provisioning: Evaluating Natural Language to Infrastructure-as-Code Generation Using Large Language Models in DevOps Workflows

The application of large language models to Infrastructure as Code generation — enabling engineers to describe desired infrastructure in natural language and receive executable IaC manifests — represents a potentially transformative capability for DevOps productivity, but also introduces novel correctness, security, and auditability risks that have not been systematically evaluated. This paper presents the first rigorous empirical evaluation of natural language to IaC generation using GPT-4 Turbo, Claude 3.5 Sonnet, and Gemini 1.5 Pro across a benchmark suite of 840 infrastructure provisioning tasks spanning AWS, Azure, and GCP in both Terraform HCL and Pulumi TypeScript. We evaluate generated configurations across five quality dimensions: syntactic correctness, semantic correctness (does the configuration provision what was requested?), security policy compliance (evaluated against CIS Benchmarks), idempotency, and cost efficiency. Across all models and providers, syntactic correctness averages 91.4%, but semantic correctness drops to 67.8%, with the most significant gaps occurring in complex networking, IAM policy generation, and multi-region configurations. Security policy compliance averages 54.2%, with IAM over-permissioning as the most prevalent deficiency. We introduce an LLM-IaC Quality Score (LIQS) aggregating all five dimensions and propose an LLM-Assisted IaC Workflow incorporating human review gates, automated compliance scanning, and policy-as-code validation to compensate for identified deficiencies. This work establishes the empirical baseline for safe LLM-assisted infrastructure provisioning in DevOps.

Ebuka Okonkwo, Hanna Björk, Daisuke Fujiwara, Rita Cardoso· Apr 2025· 142 citations

Journal Article Open Access Artificial Intelligence

DevOps in the Era of Generative AI: Reimagining Pipeline Automation, Incident Response, and Knowledge Management with Foundation Models

The rapid proliferation of generative AI foundation models — including large language models, code generation systems, and multimodal agents — is poised to fundamentally reshape DevOps practice across the software delivery lifecycle. This paper presents a horizon study examining how generative AI is being integrated into DevOps toolchains in 2024–2025, synthesizing evidence from 23 practitioner organizations, 41 tool vendor disclosures, and a systematic review of 67 preprint and published studies. We identify six primary integration points at which generative AI is creating substantive capability shifts: automated pipeline configuration generation, natural language infrastructure querying, incident narrative summarization, postmortem synthesis, documentation generation from codebase context, and intelligent on-call assistant agents. Through structured case analysis, we find that organizations deploying GenAI-augmented incident response workflows reduce average time-to-mitigation by 34% and decrease escalation rates by 28%. We introduce the GenAI-DevOps Integration Maturity Model (GDIMM), which characterizes organizational readiness across five levels from ad-hoc LLM use to fully agentic delivery pipelines. We also surface emerging risks — including prompt injection in CI/CD contexts, over-reliance on LLM-generated runbooks, and governance gaps in AI-generated infrastructure code — and propose mitigation design patterns. This paper provides the most comprehensive empirical and conceptual treatment of the GenAI-DevOps intersection to date.

Chukwuemeka Obialo, Nina Brandt, Yuki Hashimoto, Rania Saleh· Jan 2025· 274 citations

Journal Article Open Access Augmented and Virtual Reality

Spatial Computing Interfaces for Collaborative Engineering Design: Task Performance, Cognitive Load, and Design Outcome Quality in Mixed Reality Versus Traditional CAD Workflows

Spatial computing platforms -- particularly mixed reality (MR) headsets such as the Apple Vision Pro and Microsoft HoloLens 2 -- have been proposed as transformative tools for engineering design collaboration, enabling co-located and remote teams to interact with three-dimensional CAD models in their physical workspace context. Despite significant industry investment, rigorous empirical evidence comparing MR-assisted design workflows to established 2D screen-based CAD collaboration on task performance, cognitive load, and design outcome quality remains scarce. This paper presents a mixed-methods study combining a within-subjects randomized controlled experiment (n=84 engineers across mechanical, architectural, and product design specialties) with qualitative protocol analysis to evaluate MR versus screen-based CAD collaboration across three design task categories: design review and defect identification, assembly sequence planning, and cross-functional stakeholder communication. MR collaboration reduces defect identification time by 34 percent and increases defect detection rate by 22 percent for complex assembly structures compared to screen-based review, driven by improved spatial reference frame sharing. For assembly sequence planning, MR achieves equivalent outcome quality with 18 percent lower task completion time. However, MR imposes significantly higher cognitive load (NASA-TLX increase of 31 percent) during text-heavy annotation tasks due to current headset text input limitations. We develop the Spatial Computing Task Suitability Matrix (SCSM) mapping engineering task types to expected MR benefit, and provide evidence-grounded adoption guidance for engineering organizations evaluating spatial computing investments.