This study explores high-fidelity control methods for superconducting qubits, focusing on their potential applications in addressing technological challenges in Africa. We evaluate three pulse optimization techniques—Gaussian-shaped pulses, Gradient Ascent Pulse Engineering (GRAPE), and Chopped Random-Basis (CRAB)—for achieving robust quantum gate operations. Through numerical simulations, CRAB emerges as the most balanced approach, delivering high fidelity (0.927) with moderate computational cost (9 seconds), while Gaussian pulses demonstrate superior noise resilience (1.000 fidelity under noise). These findings highlight the feasibility of adapting advanced quantum control methods for resource-constrained settings, with implications for quantum-enhanced solutions in African healthcare, agriculture, and logistics. The study underscores the need for localized quantum research infrastructure to bridge the global technological divide.
This study explores high-fidelity control methods for superconducting qubits, focusing on their potential applications in addressing technological challenges in Africa. We evaluate three pulse optimization techniques—Gaussian-shaped pulses, Gradient Ascent Pulse Engineering (GRAPE), and Chopped Random-Basis (CRAB)—for achieving robust quantum gate operations. Through numerical simulations, CRAB emerges as the most balanced approach, delivering high fidelity (0.927) with moderate computational cost (9 seconds), while Gaussian pulses demonstrate superior noise resilience (1.000 fidelity under noise). These findings highlight the feasibility of adapting advanced quantum control methods for resource-constrained settings, with implications for quantum-enhanced solutions in African healthcare, agriculture, and logistics. The study underscores the need for localized quantum research infrastructure to bridge the global technological divide.
This study investigates high-fidelity control methods for superconducting qubits, emphasizing their potential to address Africa’s unique technological challenges in healthcare, agriculture, and logistics. We systematically evaluate three pulse optimization techniques—Gaussian-shaped pulses, Gradient Ascent Pulse Engineering (GRAPE), and Chopped Random-Basis (CRAB)—for robust quantum gate operations through numerical simulations. CRAB emerges as the most balanced method, achieving high fidelity (0.927) with moderate computational overhead (9 seconds per optimization), making it suitable for resource-constrained environments. Gaussian pulses, while less computationally efficient, demonstrate exceptional noise resilience (1.000 fidelity under simulated decoherence), critical for maintaining coherence in imperfect hardware. GRAPE, though theoretically optimal, proves less practical due to its high computational demand (≥30 seconds) and sensitivity to initial conditions. These results highlight the adaptability of advanced quantum control techniques to settings with limited infrastructure, provided they are tailored to local constraints. The study also identifies key implementation barriers, including the need for cryogenic facilities and specialized expertise, while proposing modular quantum research hubs as a scalable solution. By benchmarking these methods against real-world African technological priorities—such as drug discovery optimization, crop yield prediction, and supply chain logistics—we demonstrate how quantum-enhanced solutions could leapfrog classical limitations. Our findings underscore the urgency of localized quantum research investments to bridge the global technological divide, with pulse optimization serving as a foundational tool for Africa’s emerging quantum ecosystem. This work provides a roadmap for adapting quantum control strategies to diverse socioeconomic contexts while maintaining scientific rigor.
The efficient implementation of quantum computing is contingent upon high-fidelity quantum operations. Nevertheless, the fidelity of these operations is constrained by the precision of quantum system evolution control. The optimization of quantum control pulses is essential for improving the manipulation accuracy of superconducting qubits. Traditional optimization methods, including gradient descent and the gradient ascent pulse engineering algorithm, frequently encounter challenges such as slow convergence and susceptibility to local optima in pulse optimization problems. This study introduces an adaptive open-loop optimization algorithm based on the adaptive moment estimation (Adam) optimizer, capitalizing on the benefits of momentum and adaptive learning rate adjustments inherent in Adam. We conduct experimental analysis on the algorithm’s hyperparameters to achieve optimal solutions with a higher fidelity range at a faster convergence speed, effectively improving the fidelity and optimization efficiency of quantum gate operations. Through numerical simulations on the QuTiP platform, we validate the excellent performance of the Adam algorithm in quantum gate optimization. In optimizing the X and SWAP gates, its fidelity improved by 0.03% and 0.0016%, respectively, compared to the GRAPE algorithm. Compared to the CRAB method, the initial convergence speed of the Adam method increased fivefold, enabling it to achieve the target fidelity more rapidly. Future research will investigate closed-loop optimization strategies, utilizing feedback derived from the fidelity results of actual quantum computers to further augment quantum control performance
We study the impact of static disorder on a globally-controlled superconducting quantum computing architecture based on a quasi-two-dimensional ladder geometry [R. Menta et al., Phys. Rev. Research 7, L012065 (2025)]. Specifically, we examine how fabrication-induced inhomogeneities in qubit resonant frequencies and coupling strengths affect quantum state propagation and the fidelity of fundamental quantum operations. Using numerical simulations, we quantify the degradation in performance due to disorder and identify single-qubit rotations, two-qubit entangling gates, and quantum information transport as particularly susceptible. To address this challenge, we rely on pulse optimization schemes, and, in particular, on the GRAPE (Gradient Ascent Pulse Engineering) algorithm. Our results demonstrate that, even for realistic levels of disorder, optimized pulse sequences can achieve high-fidelity operations, exceeding 99.9% for the three quantum operations, restoring reliable universal quantum logic and robust information flow. These findings highlight pulse optimization as a powerful strategy to enhance the resilience to disorder of solid-state globally-driven quantum computing platforms.
Quantum optimal control, a powerful toolbox for engineering an optimal control field modulation that most precisely implements a desired quantum operation in the best way possible, has evolved into one of the cornerstones for enabling quantum technologies. The gradient ascent pulse engineering (GRAPE) algorithm is a widely used method in quantum optimal control, which has achieved great success in different physical platforms. However, its computational complexity increases exponentially with the number of qubits, making it challenging to be implemented for large-scale quantum systems. To mitigate this issue, we present the iterative GRAPE algorithm (iGRAPE), which reduces the optimization problem into a series of lower-dimensional subproblems by incorporating disentanglement operations. Our numerical simulations on physical platforms such as nuclear magnetic resonance and superconducting quantum systems demonstrate that iGRAPE significantly enhances state preparation speed. Specifically, compared to GRAPE, iGRAPE achieves up to a five-fold acceleration in preparing Greenberger--Horne--Zeilinger states using a 12-qubit implementation, and up to a 13-fold acceleration for arbitrary state preparation with eight qubits. To further validate our findings, we conduct experimental validation of iGRAPE on a four-qubit nuclear magnetic resonance system. Overall, iGRAPE offers an efficient solution for implementing optimal control in large-scale quantum systems, holding great potential for advancing quantum technologies during the noisy intermediate-scale quantum era.
Optimal control problems arise in a wide range of scientific disciplines, but the corresponding optimization algorithms often display a strong dependence on hyperparameters that significantly influence performance and convergence. For the optimal implementation of quantum algorithms, these challenges are further amplified by high-dimensional control landscapes and the need for high-fidelity operations. Here, we propose an algorithm for optimal control problems in quantum computing to efficiently generate high-fidelity control protocols for multi-qubit systems in a hyperparameter and gradient free manner. The method, referred to as Pulse Engineering via Projection of response functions at infinite nonlinear order (PEPRino), leverages the framework of response theory to navigate the control landscape to find high-fidelity implementations. This is achieved by determining the control landscape via response functions to infinite order, efficiently evaluated by resummation in terms of the first and second order response function. To demonstrate the approach, we apply it to quantum systems consisting of two and three qubits for the optimal implementation of the Quantum Fourier Transform (QFT). We benchmark the proposed algorithm against the Chopped Random Basis (CRAB) algorithm utilizing the Nelder-Mead method, focusing on the 2-qubit scenario. The results indicate faster convergence regarding iteration steps and computational time, highlighting the advantages of our approach.
This research investigates the possibility of using quantum optimal control techniques to co-optimize the energetic cost and the process fidelity of a quantum unitary gate. The energetic cost is theoretically defined, and thereby, the gradient of the energetic cost for pulse engineering is derived. We empirically demonstrate the Pareto optimality in the trade-off between process fidelity and energetic cost. Thereafter, two novel numerical quantum optimal control approaches are proposed: (i) energy-optimized gradient ascent pulse engineering (EO-GRAPE) as an open-loop gradient-based method, and (ii) energy-optimized deep reinforcement learning for pulse engineering (EO-DRLPE) as a closed-loop method. The performance of both methods is probed in the presence of increasing noise. We find that the EO-GRAPE method performs better than the EO-DRLPE methods with and without a warm start for most experimental settings. Additionally, for one qubit unitary gate, we illustrate the correlation between the Bloch sphere path length and the energetic cost.