arXiv subjects

Sangwoo Jeon

Publications and source records attributed to Sangwoo Jeon.

7 recordsLinked to original sources

arXiv2026

Optimal copy complexity of quantum state cloning

Quantum state cloning is the task of approximately producing additional copies of an unknown quantum state from a finite number of input copies. The optimal cloning fidelity is known exactly for pure states, but no comparable characterization is known for general mixed states. We determine the optimal asymptotic copy complexity for $d$-dimensional states of rank at most $r$: producing $M$ additional copies with worst-case fidelity at least $1-\varepsilon$ requires and is achievable with $N=\Theta(Mrd/\varepsilon)$ input copies. Remarkably, the lower bound already holds for a family of states with a fixed flat spectrum, while the matching upper bound is achieved by random purification followed by optimal pure-state cloning. For $M=1$, we further show that high-fidelity tomography can be coherently converted into cloning with comparable error, revealing an operational origin of the matching cloning and tomography complexities.

quant-ph↗

arXiv2026

Bound Entanglement Is Insufficient for an Exponential Quantum Learning Advantage

While entanglement is known to enable exponential improvements in the sample complexity of quantum learning, it remains unclear which properties of entangled resources are responsible for such improvements. We address this question through the reduction criterion, a condition obeyed by all bound-entangled states. In $n$-qubit Pauli-channel learning, we show that restricting either the input states or the measurement effects to satisfy this criterion rules out an exponential advantage for incoherent adaptive protocols. An exponential lower bound persists for the one-sided coherent adaptive protocols considered here, even when the unrestricted side retains quantum correlations across channel uses. Using conditional min-entropy, we further quantify how the sample-complexity lower bounds weaken as larger violations of the reduction criterion are allowed. Finally, we show that the same obstruction appears in conjugate-state learning: restricted joint measurements cannot reproduce the logarithmic-sample advantage of unrestricted joint measurements on $\rho\otimes\rho^*$. These results identify violation of the reduction criterion as a necessary condition for an exponential advantage in the learning tasks considered here.

quant-ph↗

arXiv2026

Quantum target ranging with Hetero-Homodyne detection

Quantum target ranging, which estimates a target position using entangled photon pairs, is known to offer an error-probability advantage over classical ranging strategies. Yet, realizing this advantage in practice remains challenging, as an existing receiver design relies on collective measurements and requires an impractically large number of quantum memories and linear passive components. In this work, we propose the hetero-homodyne receiver, a practically implementable architecture that achieves quantum advantage in target ranging using only local measurements. The receiver requires only one heterodyne setup, a single homodyne setup, and a delay line, making the implementation scalable and experimentally feasible. Our results establish a realistic framework for demonstrating quantum advantage in target ranging and contribute toward practical quantum radar systems.

quant-ph↗

arXiv2026

Integrating Symbolic RL Planning into a BDI-based Autonomous UAV Framework: System Integration and SIL Validation

Modern autonomous drone missions increasingly require software frameworks capable of seamlessly integrating structured symbolic planning with adaptive reinforcement learning (RL). Although traditional rule-based architectures offer robust structured reasoning for drone autonomy, their capabilities fall short in dynamically complex operational environments that require adaptive symbolic planning. Symbolic RL (SRL), using the Planning Domain Definition Language (PDDL), explicitly integrates domain-specific knowledge and operational constraints, significantly improving the reliability and safety of unmanned aerial vehicle (UAV) decision making. In this study, we propose the AMAD-SRL framework, an extended and refined version of the Autonomous Mission Agents for Drones (AMAD) cognitive multi-agent architecture, enhanced with symbolic reinforcement learning for dynamic mission planning and execution. We validated our framework in a Software-in-the-Loop (SIL) environment structured identically to an intended Hardware-In-the-Loop Simulation (HILS) platform, ensuring seamless transition to real hardware. Experimental results demonstrate stable integration and interoperability of modules, successful transitions between BDI-driven and symbolic RL-driven planning phases, and consistent mission performance. Specifically, we evaluate a target acquisition scenario in which the UAV plans a surveillance path followed by a dynamic reentry path to secure the target while avoiding threat zones. In this SIL evaluation, mission efficiency improved by approximately 75% over a coverage-based baseline, measured by travel distance reduction. This study establishes a robust foundation for handling complex UAV missions and discusses directions for further enhancement and validation.

cs.RO↗

arXiv2025

Scaling Up without Fading Out: Goal-Aware Sparse GNN for RL-based Generalized Planning

Generalized planning using deep reinforcement learning (RL) combined with graph neural networks (GNNs) has shown promising results in various symbolic planning domains described by PDDL. However, existing approaches typically represent planning states as fully connected graphs, leading to a combinatorial explosion in edge information and substantial sparsity as problem scales grow, especially evident in large grid-based environments. This dense representation results in diluted node-level information, exponentially increases memory requirements, and ultimately makes learning infeasible for larger-scale problems. To address these challenges, we propose a sparse, goal-aware GNN representation that selectively encodes relevant local relationships and explicitly integrates spatial features related to the goal. We validate our approach by designing novel drone mission scenarios based on PDDL within a grid world, effectively simulating realistic mission execution environments. Our experimental results demonstrate that our method scales effectively to larger grid sizes previously infeasible with dense graph representations and substantially improves policy generalization and success rates. Our findings provide a practical foundation for addressing realistic, large-scale generalized planning tasks.

cs.AI↗

arXiv2025

On the query complexity of unitary channel certification

Certifying the correct functioning of a unitary channel is a critical step toward reliable quantum information processing. In this work, we investigate the query complexity of the unitary channel certification task: testing whether a given $d$-dimensional unitary channel is identical to or $\varepsilon$-far in diamond distance from a target unitary operation. We show that incoherent algorithms-those without quantum memory-require $Ω(d/\varepsilon^2)$ queries, matching the known upper bound. In addition, for general quantum algorithms, we prove a lower bound of $Ω(\sqrt{d}/\varepsilon)$ and present a matching quantum algorithm based on quantum singular value transformation, establishing a tight query complexity of $Θ(\sqrt{d}/\varepsilon)$. On the other hand, notably, we prove that for almost all unitary channels drawn from a natural average-case ensemble, certification can be accomplished with only $O(1/\varepsilon^2)$ queries. This demonstrates an exponential query complexity gap between worst- and average-case scenarios in certification, implying that certification is significantly easier for most unitary channels encountered in practice. Together, our results offer both theoretical insights and practical tools for verifying quantum processes.

quant-ph↗

arXiv2024

Microwave Quantum Illumination with Optical Memory and Single-Mode Phase-Conjugate Receiver

Microwave quantum illumination with entangled pairs of microwave signal and optical idler modes, can achieve the sub-optimal performance with joint measurement of the signal and idler modes. Here, we first propose a testbed of microwave quantum illumination with an optical memory which is simulated with a delay line in the idler mode. It provides how much an input two-mode squeezing is necessary to compensate the loss of the optical memory, while maintaining quantum advantage over coherent state. When the memory is lossy, the input two-mode squeezing has to be higher through high cooperativity in the optical mode. Under the testbed, we propose a single-mode phase conjugate receiver that consists of a low-reflectivity beam splitter, an electro-optomechanical phase conjugator, and a photon number resolving detector. The performance of the newly proposed receiver approaches the maximum quantum advantage for local measurement. Furthermore, the quantum advantage is obtained even with an on-off detection while being robust against the loss of the memory.

quant-ph↗