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.
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.
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.
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.
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.
Superconducting qubits are pivotal in advancing quantum computing, poised for scale but limited by the complexity and fidelity of their control and readout systems, relying on RF and signal processing infrastructure. This survey serves as a comprehensive and technically grounded review of control and readout architectures tailored for superconducting qubits. Synthesizing insights from device physics, circuit design, microwave engineering, signal processing, and cryogenic integration, this work details the practicalities of RF pulse generation, signal synthesis, and readout signal analysis for quantum systems. it covers key requirements, parameters, and pulse engineering techniques, including commonly used envelopes like Gaussian and DRAG designs. Moving to the system level, this survey systematically classifies and critically analyses current architectural strategies (covering key areas like frequency conversion, waveform management, and system infrastructure) and technology platforms (including adaptive classical control stacks, cryogenic CMOS circuits, and novel interconnects and interfaces), evaluating their trade-offs in performance. Extensive literature analysis identifies prevailing limitations such as wiring complexity, thermal budget constraints, latency, and power consumption, while highlighting underexplored opportunities for on-chip signal processing and novel interconnects, drawing analogies to advanced communication systems design. By consolidating diverse control paradigms and critically evaluating their trade-offs, this survey provides a unified foundation for designing next-generation quantum control stacks. Finally, a forward-looking roadmap outlines key trends in monolithic integration, cryo-compatible digital architectures, and physics-informed hardware co-design, offering both a retrospective synthesis and a prospective vision for quantum hardware engineering beyond the NISQ era.
An exquisite control over the dynamics of a quantum system is essential for realizing any quantum technology. This thesis presents some practical and efficient strategies for coherent quantum control: a paradigm for controlling quantum dynamics using electromagnetic fields. Coherent control is a common framework for describing the physical implementation of quantum logic gates in several quantum information processing platforms including superconducting qubits and trapped ions. For the purpose of demonstration, however, liquid-state Nuclear Magnetic Resonance (NMR) is used in this thesis as it is an ideal test-bed for developing, testing and benchmarking coherent control tools and techniques. Coherent control was revitalised in 2005 by the development of Gradient Ascent Pulse Engineering (GRAPE), a powerful optimal control technique that is widely used for designing shaped pulses for a variety of tasks. GRAPE, like any other closed-loop optimal control algorithm, requires multiple evaluations of the computationally expensive matrix exponential and the full time-evolution propagator. To this end, I present strategies to sidestep the explicit evaluation of matrix exponentials, thereby offering substantial speed-ups over conventional implementations of the GRAPE algorithm. The results are demonstrated experimentally in NMR, but also in simulations of ultracold molecules and superconducting qubits. Beyond GRAPE, this thesis also considers methods for designing spin-echo sequences for implementing any arbitrary network of controlled-Z gates in a system of qubits with zz-couplings. First, a method based on linear-programming provides near time-optimal sequences in fully-coupled systems having up to a few hundred qubits. Further, an analytic method based on graph colouring finds near time-optimal spin echo sequences in engineered systems with any number of qubits having only nearest and next-nearest neighbour-couplings.