A Computational Framework for Adaptive Circuit Selection in Noisy Quantum Communication Simulations

Quantum communications and quantum key distribution (QKD) occupy a unique position at the intersection of quantum information processing, stochastic noise and distributed network control. Theoretical guarantees for the security of QKD based on quantum mechanics have been developed over the last several years. In order to effectively implement QKD, however, we must understand that practical implementations are limited by factors such as decoherence, imperfection of devices, and non‐homogeneous, varying environmental conditions of our distributed networks. These challenges require the need to develop adaptive computational frameworks that will dynamically adjust to changing levels of noise and to resource availability in order to successfully operate QKD, rather than relying on fixed configurations of protocols. The current study presents a means of adaptively selecting circuits for quantum communication simulated in a noisy environment using a high level of reproducibility in terms of computational frameworks and focuses upon workloads similar to QKD. Quantum communication can be treated as a decentralized, stochastic decision‐making process in which an adapting controller selects between a set of available quantum circuits based on observable network characteristics. Using the decoherence‐free subspace (DFS) encoding method and an entanglement‐based protocol that incorporates purification, the use of modular circuit templates has allowed for the selection of templates guided by the use of learned decision policies as opposed to a fixed protocol being predetermined. Unlike previous works applying reinforcement learning to tune fixed protocol parameters or measurement strategies Yang et al. and Xu et al., we model decoherence‐mitigation primitives—DFS and entanglement purification—as dynamically selectable circuit templates . In this sense, the agent acts on protocol structure instead of tuning parameters, to allow compositional adapting, on heterogeneous and adversarial network conditions. (This amounts to a qualitatively different and under‐explored control abstraction compared to the literature). Adaptive control utilizes a quantum reinforcement learning (QRL) based six qubit, four layer parameterized quantum circuit with 48 trainable parameters and a deep Q‐network (DQN) classical baseline. The agents notice the same three‐dimensional representation of state with channel loss, node‐specific noise and eavesdropping indicators. The framework is compared with more than 50 independent trials on a simulated 10‐node quantum network under a physically enriched noise model with depolarizing noise, collective dephasing, thermal relaxation (, ), amplitude‐damping leakage, nearest‐neighbor crosstalk–yielding non‐trivial von Neumann entropy values to be up to bits that give an in realistic physical depiction of decoherence. The reward function is explicitly decomposed into weighted components that are aligned to security‐critical (QBER, key rate, fidelity, entropy) metrics that are essential security, ensuring sensitivity to the most operationally relevant performance metrics. Numerical tests indicated that with noise models and circuit expressivity, QRL and classical DQN agents consistently perform in the same manner over reported metrics, and do not show any statistically significant difference between mean QBER ( p = 0.2037). Crucially, this result is not so much negative as it suggests a regime in which quantum policy models do not do better than classical baselines, yet, the QRL policy circuit operates within a genuinely quantum hypothesis space with nonzero entanglement entropy and active gradient‐based learning. It indicates that it is circuit expressivity, incentive strategy design and environment complexity which define quantum advantage of quantum communications activities (not only quantum resources by themselves). In this work we focus on the development of a transparent and extensible simulation pipeline in order to combine quantum circuit performance, stochastic noise modeling and learning‐informed adaptive control all within the framework of a one computing platform. It's about reproducibility, controlled benchmarking, and modular design, this investigation gives a concrete proving ground for further study in adaptive workflows, concurrency‐aware decision making and hybrid quantum‐classical control strategies employed in emerging quantum communication networks.