AI-Powered Digital Twins and Quantum Computing Must Inform Next-Gen Airspace Management Policy

By Capt. Fahad ibne Masood, MRAeS

In terms of the safety in the national airspace system (NAS), the risk of even a single “guesstimate” would be unacceptable. To differentiate fiction from reality, the Federal Aviation Administration (FAA) has proposed Part 108, which promises routine beyond visual line of sight (BVLOS) drone operations. To help fulfil these promises, the FAA, MITRE, NASA and other innovators have created  little-known digital twin sandboxes. These strategic transformative rule-informing tools, enhanced by AI and advanced Quantum Computing (QC), provide policymakers virtual proving grounds from which to refine, stress test and simulate the impacts of potential regulations before releasing them into the real world. This adaptive approach, which forecasts an airspace system with soaring demand and rising technology, guarantees enhanced safety risk management.

Simulation technology features advances which are centered around capturing sprawling complex dynamic systems in assorted approaches. As part of this, a digital twin provides a high-fidelity, dynamic representation of real-world airspace or aircraft, updated in real time. All of this is made possible by advances in sensor networks, IoT and data analytics.

But with AI layered into the system, these models gain novel predictive capabilities. They can not only recreate past scenarios using historical and live data but, crucially, they can anticipate the cascading effects of new rules, weather events or emerging technologies before they impact actual operations. 

MITRE, NASA, and the FAA now leverage these AI-fueled digital twins to foster their robust, evidence-driven virtual sandboxes. This means simulations aren’t just theoretical. They actively inform regulatory trajectories and enhance operational resilience. By employing AI-driven machine learning, digital twins can model intricate interactions between thousands of flights, between MUM-T (Manned Unmanned Team) or whether independent crewed or uncrewed, and can adjust simulations for real-world disruptions like storms or emergencies. 

NASA’s National Airspace System Digital Twin (NDT) provides a perfect example. It supports historical recreations as well as dynamic model enhancements in real time. NDT’s evaluation of hybrid electric propulsion gauges the requisite competences and skills bottleneck vis-à-vis availability and an unevenly distributed airport demand. It ascribes fuel cut inventory levels as well as supply chain issues to electric support of the engines. NASA also uses AI/ML to optimize paths, configure runways and detect anomalies. This cuts errors in the climb phase by 50% with data-derived performance models. 

In short, AI in digital twins makes dynamic scenario testing even better. Consider 500 flights being diverted by new rules because of a storm. AI models adjust delay, fuel burn, risk and even safety to recommend changes due governing regulations as well as automation principles. AI allows for rapid adaptation because it can recalibrate delay predictions, fuel usage and safety risk management protocols for hundreds of diverted flights in an instant. 

Some operational complexities in future airspace management are so vast that only quantum computing (QC) can solve them efficiently. It resolves some of the computation challenges classical systems struggle with, such as the real-time optimization of extremely large, dynamic variables. 

Cutting-edge research demonstrates that quantum algorithms, embedded in digital twins, can optimize airspace scheduling and resource allocation at speeds exponentially faster than ‘classical’ computing. The Augmented Qubit publication for 2024 discusses the real-time integration of QC applied to complex data sets and predictive analytics to emissions and latency minimization. In a true application of this, the 2024 ESREL study, conducted by the Polish Air Force Institute, demonstrated how quantum algorithms can detect classical shortcomings in air traffic management (ATM). AI reduced scheduling down to variant-optimal plans, even in the presence of constraints such as the changing of dynamic airspaces. 

In a digital twin environment, QC could model very intricate behaviors of thousands of drones operating under Part 108 along with manned air traffic, which classical systems cannot do. It enables comprehensive modeling of thousands of drone and manned aircraft interactions to uncover previously hidden and latent risks to reduce emissions as well as delay latency.

As the airspace sector becomes more multi-layered with drones, eVTOLs and autonomous systems, only a hyper-flexible, risk-based regulatory “hyper-lite” framework, powered by real-time digital twins, infused with AI and QC, can keep up with the pace of change. 

Regulators nowadays still have fundamental problems with developing rules. It’s not that rule concepts can’t be made. Budgets simply are not invested in validating hypotheses. The NDT sandbox offers quantified modeling to assist rule generation and regulation frameworks. With AI-powered digital twins, policy formulation cycles accelerate between 30-50%. This shifts regulators from reactive memos to proactive, data-driven strategies. These models enable stakeholders to observe, triage and iterate policy ideas by merging simulation, observation and real-time analytics for unparalleled oversight and speed. 

Consider, for example, testing Part 108 which foresees the right-of-way for BVLOS drones that weigh and operate up to 1,320 lbs. (and are classified as unregulated) to assert dominance over non-ADS-B manned aircraft. NDT models can run simulations of safe aircraft operation and underutilized sections of airspace with exact estimates. These plug-ins can be partitioned to serve as the core of NDT.

The 2022 report from MITRE “Understanding and Utilizing Digital Twins for NAS Research” speaks of “high fidelity” subsystem models and “scalable” system-of-systems architectures, regarding the “hybrid” digital twin as the key element in the evolution of NAS. A practical approach, MITRE elaborates predicting broad airspace behaviors AI enriched with Time-Based Flow Management (TBFM) simulation granules. It attempts an exercise in regulatory synthesis as it uses weather drones within the proposed regulations under Part 108’s BVLOS protocols. 

Vertical’s “FutureScape” suggests 40% faster cycles in policy formulation by reinforcement of dynamic context. MITRE’s 2025 Aerospace Language Understanding Evaluation (ALUE) benchmark co-developed with the FAA, assesses Large Language Models (LLMs) for aerospace tasks to certify dependability of AI driven twins. 

The FAA’s recent AI Safety Assurance Roadmap lays the groundwork for implementing these technologies to ensure robust validation and mitigate the risks of algorithmic bias as identified by leading industry standards bodies like the SAE G-34 Committee on ‘Artificial Intelligence in Aviation’ and EUROCAE Working Group 114. Specifically, the FAA 2024 Roadmap provides a baseline for stepwise AI implementation to protect against identified biases and hallucinations in safety-critical systems, such as noted in the standards from the G-34 & WG-114. 

Risk-based regulatory hyper-lite framework is the need of the hour, for the hyper-multilayered airspace sector to be crossed with the digital twin of the sky, augmented with AI and quantum. It offers proactive, better-said, predictive wins by transforming industry best-practices to regulatory gains. 

As air travel demand appears set to double by 2040, the imperative for governments and industry is clear: Adopt and Harmonize digital twin AI systems globally. The FAA’s NextGen updates, ICAO global standards collaboration and public-private quantum AI pilot projects exemplify this trend. MITRE’s high-fidelity hybrid digital twin architectures, coupled with new benchmarks like the ALUE, aim to certify the reliability of AI-driven digital twins for critical aerospace tasks.

Gatekeepers, such as the FAA, SAE G-34 committees, should also focus some decent effort on the strategic embedding of quantum mechanics in digital twins for real-time control on complex interdependencies, (e.g., unparalleled fusion-requirement during the integration of AAM).

The US Government must, at the strategic level, impose controlled-testing with the twins to streamline regulations and encourage Public-Private ‘Technology Development’ Partnership. This, for the FAA translates, into embedding twins into the NextGen 2024-2025 updates, ICAO harmonization for global standards, and funding quantum AI pilot projects. Without such sets of sociotechnical systems innovations, Part 108 would suffer deployment delays or safety gaps. On the flipside, with such initiatives, airspace becomes a living entity, responsive to real time data insights.

AI-driven digital twins, when fully realized, shift aviation compliance from a costly obligation to a competitive advantage. They enable evidence-based policymaking, predictive safety assurance, and regulatory agility, a must as drones, AAM, and advanced automation transform our skies. By adopting these tools, airspace managers, operators, and regulators can “freeline” the rules and so the sky can be governed by real-time insights, dynamic adaptation and sustainable, safe, risk-managed growth.