The noise and complexity inherent to quantum communication networks leads to technical challenges in designing quantum network protocols using classical methods. We address this issue with a hybrid variational quantum optimization framework (VQO) that simulates quantum networks on quantum hardware and optimizes the simulation using differential programming. We maximize nonlocality in noisy quantum networks to showcase our VQO framework. Using a classical simulator we investigate the noise robustness of quantum nonlocality. Our VQO methods reproduce known results and uncover novel phenomena. We find that maximally entangled states maximize nonlocality in the presence of unital qubit channels, while nonmaximally entangled states can maximize nonlocality in the presence of nonunital qubit channels. Thus, we show VQO to be a practical design tool for quantum networks even when run on a classical simulator. Finally, using IBM quantum computers we demonstrate that our VQO framework can maximize nonlocality on noisy quantum hardware. In the long-term, our VQO techniques show promise of scaling beyond classical approaches and can be deployed on quantum network hardware to optimize network protocols against their inherent noise.
The noise and complexity inherent to quantum communication networks leads to technical challenges in designing quantum network protocols using classical methods. We address this issue with a hybrid variational quantum optimization framework (VQO) that simulates quantum networks on quantum hardware and optimizes the simulation using differential programming. We maximize nonlocality in noisy quantum networks to showcase our VQO framework. Using a classical simulator we investigate the noise robustness of quantum nonlocality. Our VQO methods reproduce known results and uncover novel phenomena. We find that maximally entangled states maximize nonlocality in the presence of unital qubit channels, while nonmaximally entangled states can maximize nonlocality in the presence of nonunital qubit channels. Thus, we show VQO to be a practical design tool for quantum networks even when run on a classical simulator. Finally, using IBM quantum computers we demonstrate that our VQO framework can maximize nonlocality on noisy quantum hardware. In the long-term, our VQO techniques show promise of scaling beyond classical approaches and can be deployed on quantum network hardware to optimize network protocols against their inherent noise.
In the field of robotics, optimization problems with high computational costs, such as motion planning, multi-robot cooperation, and manipulation, are constrained by the exponential growth of the complexity of classical algorithms. This paper discusses practical methods for robot optimization, specifically using noise-aware variational quantum algorithms (VQAs) on current noisy intermediate-scale quantum hardware. To address the inherent errors of quantum devices, a specific framework has been proposed. The framework clearly defines noise modeling, adjusts error mitigation methods, and creates quantum-classical workflows. Using actual quantum computers and high-fidelity simulations, this paper will systematically evaluate four benchmark robotic domains. The statistical data from numerous experiments (up to 100 trials per scenario) indicate that, in the presence of significant hardware noise, noise-aware VQAs can improve the target value by up to 14% compared to standard quantum methods and reduce the number of convergence iterations by 45%. Cross-platform tests indicate that all six robotic tasks run stably, with an accuracy rate exceeding 90% for all devices. In real-world environments, this trajectory generation method performs excellently under noisy conditions when using physical robots. In order to ensure the accuracy of quantum accelerated optimization in robotics, it is necessary to reduce noise. According to the aforementioned research, reducing noise can be practically applied outside of laboratory conditions.
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.
Quantum resources, such as entanglement or quantum communication, offer significant communication advantages in information processing. We develop an operational framework for realizing these communication advantages in resource-constrained quantum networks. The framework computes linear bounds on the input/output probabilities of classical networks with limited communication and globally shared randomness. Since the violation of these classical bounds witnesses nonclassicality, a measurable communication advantage, the framework maximizes the violation of the classical bound using variational quantum optimization methods tailored to the communication network and quantum resources. This operational framework for nonclassicality can be scaled on quantum computers or deployed in the field to optimize noisy quantum networks for communication advantages. Applying this framework, we investigate the nonclassicality of communication networks that are assisted by quantum resources. We find that entanglement between communication-constrained parties is sufficient for nonclassicality to be found, whereas in networks with multiple senders, quantum communication with no entanglement-assistance is sufficient for nonclassicality to be found. As a result, entanglement is necessary for nonclassicality when a single sender broadcasts to multiple receivers.
Abstract Noisy Intermediate-Scale Quantum (NISQ) devices are near-term quantum computing devices that inherently suffer from imperfect gate implementation, decoherence, and measurement noise. The main type of variational quantum algorithms(VQAs) is based on Parameterized Quantum Circuits (PQCs), which, however, face practical challenges due to optimization instability of optimization caused by noise and reduced performance. In this paper, we suggest a comprehensive noise-resilient optimization framework for PQCs, which explicitly incorporates noise-resilient circuit architecture design, incorporating noise-aware training strategies into the variational optimization loop. The framework uses circuit structures that are efficient in depth and are hardware compatible in addition to adaptive optimizer choice, stochastic optimization, and averaging as a part of repeated measurement to reduce the impacts of realistic noise. Large-scale experiments are performed with different amounts of qubits, depths of the circuit and noise strengths with realistic noise models. The findings indicate that the convergence rate is faster, the stability is increased, the variability of run-to-run variability is minimized, and other robustness is better than the noise-unaware optimization techniques. Moreover, systematic studies determine optimal operating regions that optimally trade-off expressibility and noise tolerance to offer useful design advice on trusted implementation of VQAs with near-term quantum hardware.
Hybrid quantum classical algorithms offer a promising approach for exploiting Noisy Intermediate Scale Quantum devices; however, their practical deployment remains limited by excessive circuit depth, hardware noise, and inefficient utilization of quantum resources. This paper presents a Hybrid Variational Optimization (HVO) framework that reduces quantum algorithmic complexity by integrating adaptive circuit pruning with classical feedback-driven optimization and a unified Quantum Complexity Metric (QCM). Unlike prior adaptive variational approaches such as RE-ADAPT-VQA, the proposed framework explicitly optimizes the trade-off between gate count, fidelity, and qubit resources while remaining modular and hardware-agnostic. To ensure a fair and transparent evaluation, the proposed method is compared against a well-tuned shallow Variational Quantum Eigensolver (VQE), QAOA, and RE-ADAPT-VQA. Experimental validation on IBM Quantum simulators and real NISQ hardware demonstrates up to a 35% reduction in circuit complexity, a 30% reduction in execution time, and improved fidelity of 0.94. Furthermore, performance evaluation using accuracy, precision, recall, and F1-score confirms the robustness and reliability of the proposed framework. These results demonstrate that HVO provides a scalable and noise-resilient solution for near term quantum computing applications.
Simulating large quantum systems is the ultimate goal of quantum computing. Variational quantum simulation (VQS) gives us a tool to achieve the goal in near-term devices by distributing the computation load to both classical and quantum computers. However, as the size of the quantum system becomes large, the execution of VQS becomes more and more challenging. One of the most severe challenges is the drastic increase in the number of measurements; for example, the number of measurements tends to increase by the fourth power of the number of qubits in a quantum simulation with a chemical Hamiltonian. This work aims to dramatically decrease the number of measurements in VQS by recently proposed shadow-based strategies such as classical shadow and derandomization. Even though previous literature shows that shadow-based strategies successfully optimize measurements in the variational quantum optimization (VQO), how to apply them to VQS was unclear due to the gap between VQO and VQS in measuring observables. In this paper, we bridge the gap by changing the way of measuring observables in VQS and propose an algorithm to optimize measurements in VQS by shadow-based strategies. Our theoretical analysis not only reveals the advantage of using our algorithm in VQS but theoretically supports using shadow-based strategies in VQO, whose advantage has only been given numerically. Additionally, our numerical experiment shows the validity of using our algorithm with a quantum chemical system.