Quantum networks are a backbone of future quantum technologies thanks to their role in communication and scalable quantum computing. However, their performance is challenged by noise and decoherence. We propose a self-configuring approach that integrates superposed quantum paths with variational quantum optimization techniques. By estimating a cost function via repeated network uses, our approach dynamically optimizes the superposition of noisy paths across multiple nodes to establish high-fidelity connections between different parties. Our framework is in principle capable of adapting to unknown noise without assuming parametric noise models or performing full process tomography. We also discuss the role of vacuum coherence, a quantum effect central to path superposition that impacts protocol performance. Additionally, we demonstrate that our approach remains beneficial even in the presence of imperfections in the generation of path superposition.
Quantum networks are a backbone of future quantum technologies thanks to their role in communication and scalable quantum computing. However, their performance is challenged by noise and decoherence. We propose a self-configuring approach that integrates superposed quantum paths with variational quantum optimization techniques. By estimating a cost function via repeated network uses, our approach dynamically optimizes the superposition of noisy paths across multiple nodes to establish high-fidelity connections between different parties. Our framework is in principle capable of adapting to unknown noise without assuming parametric noise models or performing full process tomography. We also discuss the role of vacuum coherence, a quantum effect central to path superposition that impacts protocol performance. Additionally, we demonstrate that our approach remains beneficial even in the presence of imperfections in the generation of path superposition.
Entanglement generation and distribution is a core functionality of quantum networks. Traditional approaches to this functionality rely on chains of quantum repeaters or on manipulation of multipartite states. In this work, we explore an alternative paradigm that explores quantum coherence at propagation level, by enabling genuine quantum instantiations of communication channels, existing in superposed configurations. We demonstrate that this mechanism can be harnessed to generate both bipartite and multipartite entangled states from separable inputs. Specifically, we show that Bell states can be obtained from initially separable two-qubit states, through the superposition of two unitary quantum channels. The proposed approach is further extended to multipartite systems, by generating n-qubit GHZ and W states and thereby revealing the constructive role of superposed quantum operations in entanglement creation. Moreover, we analyze the robustness of the proposed scheme against noise, showing that the resulting states retain high fidelity and concurrence even under realistic imperfections. Our results suggest that, under appropriate quantum control, noisy superposed paths can be effectively harnessed to engineer entanglement generation and distribution in quantum networks.
Quantum networking enables advanced communication protocols, distributed quantum computing, and enhanced sensing. These applications are often hindered by current realizations of quantum network nodes, which utilize noisy and imperfect hardware. Here, we apply variational optimization to counteract the effects of such imperfections. We parameterize network protocols for use with variational methods and then use these methods to optimize simple prepare-and-measure protocols on an optical testbed as a proof-of-principle demonstration. We demonstrate automated polarization compensation, achieving results comparable to conventional methods. We also analyze the effects of hardware imperfections and dark counts on the performance of our optimizations. We compare the performance of various gradient estimation methods and propose natural extensions, including further improvements through the use of global optimization and hybrid gradient estimation methods, as well as the application of variational optimization to nodes deployed on fiber networks.
Entanglement generation and distribution is a core functionality of quantum networks. Traditional approaches to this functionality rely on chains of quantum repeaters or on manipulation of multipartite states. In this work, we explore an alternative paradigm that explores quantum coherence at propagation level, by enabling genuine quantum instantiations of communication channels, existing in superposed configurations. We demonstrate that this mechanism can be harnessed to generate both bipartite and multipartite entangled states from separable inputs. Specifically, we show that Bell states can be obtained from initially separable two-qubit states, through the superposition of two unitary quantum channels. The proposed approach is further extended to multipartite systems, by generating n-qubit GHZ and W states and thereby revealing the constructive role of superposed quantum operations in entanglement creation. Moreover, we analyze the robustness of the proposed scheme against noise, showing that the resulting states retain high fidelity and concurrence even under realistic imperfections. Our results suggest that, under appropriate quantum control, noisy superposed paths can be effectively harnessed to engineer entanglement generation and distribution in quantum networks.
Finding a path with the least overall noise from quantum memories, fibers, and gate operations in a quantum network involves the challenge of acquiring knowledge of these noises, their sources, and the time of occurrences. In this paper, we propose a reinforcement learning-based route selection approach that uses a multi-arm bandit algorithm to find the least noisy path from a transmitter (Tx) to a receiver (Rx), without considering any information on qubit decoherence due to probabilistic noises inherent in quantum memories and imperfect gate operations. It only uses network deployment knowledge to find a set of feasible paths from Tx to Rx. We provide a key finding from a network design perspective which says that performing entanglement swapping on nodes within a path in a non-synchronized and parallel manner not always reduces the decoherence experienced in achieving the end-to-end entanglement on that path. Further, we design and open-source a new simulator for simulating probabilistic noises encountered during entanglement distribution between Tx-Rx on a path, which has supporting callable functions for connecting the unknown network environment required for interaction with the multi-arm bandit agent. The simulation results demonstrate that our proposed route selection approach provides a path up to ~ 33% better fidelity (less noise) compared to conventional, distance-based route selection approach for the considered quantum network.
Quantum computing harnesses the principles of quantum mechanics to perform computations that are intractable for classical computers. However, quantum systems are notoriously sensitive to environmental noise, requiring precise and complex parameter adjustments to achieve optimal performance. Traditional parameter adjustment methods often involve lengthy, iterative processes and can be limited in their ability to effectively explore the parameter space. This paper proposes a novel algorithm based on quantum networks, leveraging quantum entanglement and superposition to dynamically optimize quantum network parameters, ultimately enhancing the efficiency and performance of quantum computations. This research focuses on a systematic approach to parameter adjustment, driven by the unique properties of these quantum phenomena. The core mechanism involves constructing a quantum network and employing quantum algorithms to iteratively adjust parameters based on the network's state evolution. We present a theoretical framework and a preliminary implementation demonstrating the algorithm's potential for improved quantum computation.
Quantum sensing networks (QSNs) are expected to play a critical role in quantum networks by achieving measurement precision unattainable with classical methods, leveraging quantum properties such as superposition and entanglement. Distributed quantum sensing, a key application of QSNs, can reach Heisenberg-limited precision scaling with the number of sensors involved. However, practical implementation faces significant challenges due to noise effects, complicating the optimal selection of sensor configurations. In this paper, we propose applying a variational quantum algorithm (VQA) combined with a genetic algorithm to efficiently mitigate noise in quantum sensing protocols and to identify high-quality sensor configurations. Performance analysis demonstrates that our approach outperforms traditional sensor configuration methods in single- and multi-parameter sensing scenarios under dephasing and amplitude damping noises, significantly improving quantum sensing accuracy and scalability.