A research team at Changsha University of Science and Technology in China has now tackled this question with an unusually comprehensive strategy. In a study published in Cluster Computing, Qiang Tang, Defa Ouyang, Heng Liu, and Lei Yang present a framework called VDDM, short for VM Dynamic partition and DRL-based Delay Minimization. The work targets a scenario with multiple drones serving multiple user equipments, the phones, sensors, and devices that generate computational workloads. The goal is deceptively simple to state and notoriously hard to achieve: minimize the total task delay experienced by all users simultaneously, while juggling virtual machine configuration, user-to-drone assignments, and the flight paths of the aircraft themselves.

The mathematical backbone of the study is a mixed-integer nonlinear programming problem, a class of optimization that combines continuous variables, such as a drone’s position in three-dimensional space, with discrete choices, such as which user connects to which drone and how many virtual machines each drone should run. Problems of this type are computationally brutal; the search space explodes combinatorially as the number of drones, users, and time slots grows. Solving them exactly is often infeasible in real time, which is precisely why the authors decomposed the challenge into three cooperating components rather than attacking it as one monolithic calculation.

The first component addresses a subtle but performance-critical issue that has plagued virtualized systems for years: input-output interference among virtual machines. When several VMs on the same physical host hammer the disk and network subsystems at once, they contend for shared I/O channels, and each one slows the others down. Prior research, including widely cited work on performance interference in virtualized cloud environments, has documented how badly I/O-heavy workloads can degrade when packed onto a single host. The VDDM framework counters this with a dynamic partition algorithm that adjusts the number of virtual machines on each drone according to the sizes of the tasks being processed. Instead of a fixed VM configuration, the partition adapts to the workload, reducing interference and keeping the drone’s parallel computing capacity genuinely usable.

The second component handles the matching problem between users and drones. In a multi-UAV network, every user wants to offload to the drone that will serve it fastest, but if everyone piles onto the same aircraft, that drone becomes a bottleneck while its neighbors idle. The researchers apply matching theory, a mathematical framework originally developed for economics problems like stable marriages and school assignments, to pair each user equipment with the UAV carrying the lowest load. This load-aware assignment spreads the computational demand across the fleet, preventing hotspots and smoothing out the queueing delays that build up when too many tasks land on one server at once.

The third and perhaps most visually striking component is the flight control layer. Where a drone hovers determines the quality of its wireless channel to each user, and therefore how fast data can flow during offloading. Optimizing trajectories jointly with everything else is what elevates this work beyond simple resource scheduling. The authors turn to deep reinforcement learning, specifically the Soft Actor-Critic algorithm, a technique that has become a favorite in control problems because it balances exploration of new actions against exploitation of known good ones. SAC learns a policy that guides each UAV’s flight path over time, continuously repositioning the aircraft to shorten transmission distances, improve channel conditions, and ultimately cut the end-to-end delay of every offloaded task.

Deep reinforcement learning deserves a moment of explanation for readers unfamiliar with it. In this paradigm, an agent, here the drone’s onboard decision system, observes the state of its environment, takes an action such as moving to a new coordinate, and receives a reward signal that reflects how well that action served the overall objective of minimizing delay. Over many training episodes, the neural network behind the agent learns which maneuvers and configurations yield the best long-term outcomes, even in environments too complex and dynamic for hand-written rules. SAC adds entropy to the reward, encouraging the agent to keep trying diverse actions rather than collapsing prematurely onto a mediocre strategy, which is one reason the approach tends to converge reliably in high-dimensional problems like multi-UAV trajectory planning.

The three components of VDDM do not operate in isolation; they form a pipeline in which each stage feeds the next. Task sizes inform the virtual machine partition, the partition shapes each drone’s effective capacity, capacity determines the sensible user-to-drone matches, and the resulting pattern of communication demands shapes the trajectories the SAC agent learns to fly. By formulating the whole system as a single MINLP objective before decomposing it, the authors ensure that the pieces pull in the same direction rather than optimizing themselves into conflict. This joint design philosophy reflects a broader trend in edge computing research, where treating offloading, resource allocation, and mobility as separate problems has repeatedly been shown to leave performance on the table.

How well does it actually work? According to the team’s extensive simulation results, VDDM significantly outperforms existing benchmark strategies in the two metrics that matter most for this class of system: total task delay and training convergence speed. The delay reduction follows directly from the framework’s ability to simultaneously cut I/O interference, balance loads across the fleet, and physically move computing power closer to the users who need it most. The faster convergence is a practical bonus with real consequences, because reinforcement learning systems that train slowly burn time and energy before they become useful, and in dynamic environments a policy that converges quickly adapts to changing user distributions sooner. The study reports its findings across a series of figures and algorithm descriptions that trace how each component contributes to the overall gain.

The implications stretch well beyond the simulation lab. Multi-UAV edge computing is a candidate technology for emergency response networks, where terrestrial base stations may be destroyed; for temporary mass gatherings, where cellular capacity buckles under demand; for precision agriculture and infrastructure inspection across vast rural areas; and for maritime and border surveillance, where earlier work by some of the same authors has explored latency minimization in aerial networks. In each of these settings, delay is not merely a quality-of-service statistic but often the difference between a useful system and a useless one. A framework that squeezes latency out of every layer of the stack, from the virtual machine scheduler to the flight controller, moves the field measurably closer to deployments that can be trusted under pressure.

The work also highlights a challenge the field is only beginning to confront honestly: the gap between idealized virtual machines and the messy physical reality of shared hardware. Much of the existing literature on UAV-assisted MEC treats a drone’s computing capacity as a clean, divisible quantity, ignoring the contention effects that arise when multiple workloads share the same I/O subsystem. By building I/O interference into the problem formulation and designing the dynamic partition algorithm specifically to mitigate it, the Changsha team grounds its optimization in a more realistic model of what actually happens inside a virtualized host. That realism, combined with learning-based trajectory control, suggests a template for future systems in which flying edge servers are not just a research curiosity but dependable infrastructure, quietly hovering overhead and making sure the computations that matter arrive on time.

Subject of Research: Delay minimization for computation offloading in multi-UAV assisted mobile edge computing using virtual machine partitioning and deep reinforcement learning

Article Title: A VM dynamic partition and drl based delay minimization offloading strategy for Multi-UAV assisted MEC

Article References: Tang, Q., Ouyang, D., Liu, H., & Yang, L. (2026). A VM dynamic partition and drl based delay minimization offloading strategy for Multi-UAV assisted MEC. Cluster Computing, 29(13), Article 737. https://doi.org/10.1007/s10586-026-06512-z

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06512-z

Keywords: mobile edge computing, unmanned aerial vehicles, virtual machines, deep reinforcement learning, Soft Actor-Critic, trajectory optimization, computation offloading, I/O interference, matching theory, mixed-integer nonlinear programming, task delay, UAV networks

To understand why this is more than wordplay, it helps to start with the physics. Quantum energy teleportation, first proposed by Masahiro Hotta in 2008, is a protocol that allows energy to be extracted at a distant location by exploiting entanglement in the ground state of a many-body quantum system. In an ordinary quantum system, the ground state is the lowest-energy configuration, and a local region of it can appear as a so-called passive state, one from which no work can be extracted by local operations alone. Yet the region is not isolated: it is entangled with its surroundings. If a distant party performs a measurement on their part of the system and communicates the result over a classical channel, the recipient can apply a conditional operation tailored to that outcome. The measurement injects energy locally at the sender’s site, and the conditional operation at the receiver’s site then unlocks energy that was previously inaccessible, effectively delivering usable energy without any physical carrier traveling between the two points.

The protocol respects all the usual physical constraints. No energy travels faster than light, because the classical communication of the measurement outcome is essential, and the sender must pay an energy cost that, as Ikeda proves rigorously in the paper, is always at least as large as the total energy delivered to the receivers. The proof relies on the fact that the ground state minimizes the total energy, so any manipulation that raises the energy of the sender’s region must account for the energy extracted elsewhere. What makes the protocol economically interesting is precisely this structure: a supplier pays a cost, consumers receive a benefit, and the accounting between them is governed by the laws of quantum mechanics rather than by contract law. Ikeda demonstrated the protocol experimentally on IBM superconducting quantum processors, using pairs of qubits on the ibm_strasbourg, ibm_sherbrooke and ibm_kyiv devices, with modern error mitigation techniques such as matrix-free measurement mitigation and zero-noise extrapolation to extract clean signals from noisy hardware.

With this physical substrate in place, the paper builds economic machinery on top of it. The central move is to treat teleported energy as a commodity and the entangled ground state as a shared resource, then to ask what happens to classical game-theoretic models when players can additionally choose quantum strategies. In classical game theory, following John von Neumann, Oskar Morgenstern and John Nash, a game is defined by players, strategies and payoffs, and an equilibrium is a set of strategies from which no player benefits by deviating alone. Ikeda extends this framework by allowing strategies to be quantum operations, including measurements and conditional unitary gates, which means the space of possible actions is vastly larger than in the classical setting. New equilibria appear that have no classical counterpart, and in some cases the quantum strategies allow players to escape dilemmas, such as the free-rider problem, that are unavoidable when strategies are restricted to classical choices.

The paper works through a remarkable range of standard industrial organization models. Ikeda constructs quantum versions of the Bertrand and Cournot duopoly models, the two canonical frameworks for describing competition between firms. In Cournot competition, firms choose quantities and the market price adjusts; in Bertrand competition, firms choose prices and consumers buy from the cheapest supplier. When firms can share or trade entangled resources and deploy quantum strategies, the resulting equilibria differ from the classical ones, offering unconventional ways to divide the market or to extract surplus. The analysis extends to monopoly, to Hotelling-style location games in which firms choose where to position themselves along a market, to contestable markets where the threat of entry disciplines incumbents, and to perfect competition. In each case the quantum layer does not simply reproduce the classical results with extra steps; it introduces genuinely new strategic possibilities rooted in the correlation structure of entangled states.

Perhaps the most provocative applications concern public goods and incentive problems. Moral hazard, the situation in which an agent’s effort cannot be perfectly observed by a principal, has been a cornerstone of contract theory since Bengt Holmström’s 1979 work on observability. Free riding on public goods, analyzed classically by Paul Samuelson and others, similarly arises because individual contributions are hard to verify or reward. Ikeda shows that quantum energy teleportation offers a lens on these problems: because the measurement and feedback operations in the protocol are verifiable parts of a physical process, and because entanglement creates correlations that cannot be replicated classically, agents can be given incentives that depend on quantum information. The paper unveils strategies that transcend classical limits, suggesting that in a future quantum economy, the design of contracts and contribution mechanisms might exploit resources that classical mechanism design cannot even describe.

The study also reaches into matching theory, the branch of economics founded on the Gale-Shapley algorithm that underlies everything from school choice to organ exchange. Ikeda incorporates entangled resources into matching models and proves that stable and optimal allocations can be achieved in settings where the participants’ payoffs are tied to quantum states. In his formalism, a matching corresponds to a configuration whose total payoff is encoded in a Hamiltonian, and a stable matching is the ground state of that Hamiltonian, with penalty terms ensuring that no participant has an incentive to block the outcome. The proofs in the paper’s appendix show that the ground state satisfies individual rationality, meaning no participant prefers to opt out, which is exactly the stability condition that market designers care about. This connection between quantum many-body physics and market stability is one of the paper’s most elegant technical contributions.

None of this is pure abstraction. Ikeda’s earlier work demonstrated quantum energy teleportation on real superconducting hardware in 2023, and the new paper includes a detailed appendix on the experimental methods, showing how mid-circuit measurements and conditional operations are implemented, how noise is mitigated, and how the latest Eagle-generation processors run the protocol ten to one hundred times faster than the devices used two years earlier. The programming code for the simulations and experiments is publicly available under an MIT license on the author’s GitHub repository. Related results by other groups, including experimental activation of strongly passive states and proposals for quantum key distribution based on teleportation protocols, indicate a rapidly maturing research area in which the physics of entanglement and the engineering of quantum processors advance together.

The intellectual lineage of the paper is also worth noting. The idea that information is physical traces back through Maxwell’s demon, Leo Szilard’s analysis of measurement and entropy, and the work of Rolf Landauer and Charles Bennett. Quantum money, proposed by Stephen Wiesner in the 1980s and developed by later researchers, showed that quantum states could serve as unforgeable economic objects. Ikeda’s own 2022 paper with Shumpei Aoki on quantum games and quantum economic behavior laid the groundwork for the present synthesis. What is new here is the scale of the ambition: rather than treating one economic object quantum mechanically, the paper rebuilds an entire toolkit of microeconomics, from auctions and mechanism design to repeated games and cooperative solution concepts like the Shapley value, on quantum foundations.

Caveats remain, as they must in any nascent field. The models are theoretical, the demonstrations are small-scale, and no one knows whether entangled energy markets will ever exist outside the laboratory. Yet the paper makes a serious case that the question is worth asking. If quantum networks eventually distribute entanglement as routinely as fiber optics distribute bandwidth, then the economics of that distribution, including who pays for measurements, who profits from teleported energy, and how stable allocations are reached, will need a theory. Quantum Information Economics, as sketched in this work, is an early attempt to provide one, and it suggests that the strategic landscape of a quantum-enabled economy may be richer, and stranger, than anything classical game theory has imagined.

Subject of Research: Quantum energy teleportation as a commodity in quantum game theory and economic models

Article Title: Quantum games and economics through teleportation

Article References: Ikeda, K. (2026). Quantum games and economics through teleportation. Quantum Information Processing, 25(10), Article 331. https://doi.org/10.1007/s11128-026-05341-8

Image Credits: AI Generated

DOI: 10.1007/s11128-026-05341-8

Keywords: quantum energy teleportation, quantum game theory, Quantum Information Economics, entanglement, Bertrand competition, Cournot duopoly, moral hazard, public goods, matching theory, market design, superconducting qubits, quantum networks