Adaptive Quantum Control through Hardware-Software Co-optimization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum control, termed hardware-software co-optimization, aiming to enhance the efficiency of quantum computation. The core idea revolves around developing a dynamic optimization framework that adapts control pulses based on real-time feedback from the quantum hardware. This framework integrates quantum control algorithms with the specific characteristics of the quantum device, such as coherence time and gate fidelity. The optimization process is achieved through a collaborative system where the optimizer adjusts the control pulses, and the quantum computing software executes the computations. We demonstrate the potential of this approach to improve the overall performance of quantum algorithms by mitigating the impact of hardware imperfections. The proposed methodology offers a pathway to more robust and efficient quantum control, bridging the gap between theoretical algorithms and practical hardware implementation. Key mathematical elements explored include pulse shaping, error mitigation strategies, and adaptive control algorithms.

DOI: 10.5281/zenodo.22244738

Related papers

Adaptive Hardware-Software Co-Design for Quantum Computing

Jincheng Zhang

Quantum computing promises revolutionary advancements across various fields, but its practical realization hinges on overcoming significant challenges in algorithm execution. Traditional hardware-software co-design approaches for quantum computing often rely on pre-defined architectures and compilation strategies, which can be suboptimal for diverse quantum algorithms. This paper proposes an adaptive hardware-software co-design framework that dynamically adjusts the quantum computing system's configuration – encompassing both hardware architecture and software compilation – in response to the specific requirements of the algorithm being executed. The core claim is that optimized co-design is pivotal for efficient quantum algorithm execution. This framework leverages a dynamic optimization loop, continuously monitoring algorithm performance and adjusting parameters such as qubit connectivity, gate set selection, and compilation strategies. The system utilizes a mathematical model to represent the algorithm's computational demands and hardware capabilities, enabling real-time adaptation. This approach moves beyond static design methodologies, offering a pathway towards significantly improved performance and resource utilization in quantum computing systems. The framework's ability to adapt in real-time represents a crucial step towards unlocking the full potential of quantum algorithms.

Sep 5, 20260 citations

自适应量子算法的优化策略与硬件设计

Jincheng Zhang

Quantum computing holds immense potential for scientific discovery and technological advancement, but current limitations in algorithm design and hardware implementation restrict its widespread adoption. This paper investigates a novel optimization strategy for quantum algorithms, leveraging the inherent properties of quantum hardware. We propose a dynamically adaptive algorithm that adjusts quantum gate parameters and entanglement strength based on real-time hardware feedback. The core mechanism centers around a "quantum system state" optimization algorithm, meticulously calibrated to the specific characteristics of the target quantum device. This approach aims to significantly improve algorithm efficiency and enhance quantum hardware performance, offering a pathway towards practical quantum applications.

Sep 6, 20260 citations

Quantum Decoherence-Driven Adaptive Hardware Optimization

Jincheng Zhang

This paper proposes a novel approach to quantum hardware optimization leveraging the inherent characteristics of quantum decoherence. The core idea is to construct an adaptive system that continuously monitors and corrects quantum errors arising from decoherence, ultimately enhancing the performance and stability of quantum computations. The proposed method utilizes a feedback loop where the degree of decoherence experienced by quantum bits is measured and quantified. This measurement serves as an optimization signal, which is then processed by a reinforcement learning algorithm. The algorithm dynamically adjusts hardware parameters – such as pulse shapes, durations, and frequencies – to minimize the detrimental effects of decoherence and maximize the preservation of quantum superposition and coherence. Unlike conventional hardware optimization techniques that rely on offline calibration or fixed algorithms, this approach offers a real-time, adaptive response to decoherence, representing a significant advancement in quantum computing hardware development. The effectiveness of this strategy is predicated on the ability to accurately characterize and respond to decoherence in a manner that directly mitigates its impact on quantum computations. The research delves into the mathematical framework underpinning this adaptive system, detailing the relationships between measurement data, reinforcement learning parameters, and hardware adjustments.

Aug 31, 20260 citations

Dynamically Reconfigurable Quantum Circuits for Algorithm Optimization

Jincheng Zhang

This paper proposes a novel approach to quantum algorithm optimization through dynamically reconfigurable quantum circuits. The core concept involves adapting the structure of a quantum circuit during its execution, responding to real-time feedback derived from quantum state analysis. Traditional quantum circuit design often relies on static, pre-defined architectures, limiting their adaptability and potential for optimization. This work introduces a control system capable of monitoring qubit states and dynamically altering circuit topology – specifically, adjusting gate sequences and potentially even adding or removing qubits – to minimize gate count and maximize coherence, thereby improving overall algorithm efficiency. The theoretical framework presented here outlines the key components and operational principles of such a system, highlighting its potential to overcome the limitations of static circuits and usher in a new era of adaptive quantum computation. We demonstrate, through a mathematical model, how this dynamic reconfiguration can be strategically implemented to address specific algorithmic challenges, focusing on reducing circuit complexity and enhancing the fidelity of quantum computations. The key to this approach lies in continuous feedback loops and a sophisticated control algorithm that leverages quantum state information to guide circuit modifications. This allows for a more tailored and efficient execution of quantum algorithms compared to static designs.

Sep 7, 20260 citations

Adaptive Quantum Feedback Control System

Jincheng Zhang

This paper proposes and analyzes an adaptive quantum feedback control system designed to optimize the performance of quantum devices. The core of the system lies in a real-time monitoring and adjustment mechanism utilizing a quantum sensor to measure the state of the quantum system. This measurement data is then fed back into a control algorithm, which dynamically adjusts the system parameters to achieve precise control. Unlike traditional control systems that are sensitive to parameter uncertainties, this adaptive approach leverages the inherent properties of quantum systems to enhance stability and performance. The theoretical framework presented here establishes a foundation for future research and development in adaptive quantum control, addressing key challenges in quantum device operation and manipulation. The system aims to provide a robust and flexible solution for controlling quantum systems with varying characteristics. The primary focus is on the control loop design and the feedback algorithm implementation, with an emphasis on achieving optimal performance under conditions of parameter uncertainty. This work contributes to the growing field of quantum control by introducing a novel adaptive strategy that can significantly improve the operational capabilities of quantum devices.

Aug 31, 20260 citations

Self-Organizing Quantum Hardware via Adaptive Entanglement Control

Jincheng Zhang

This paper presents a novel approach to quantum hardware design and control, leveraging reinforcement learning to achieve self-organization and optimal performance. The core concept is that quantum processors, rather than relying on pre-determined calibration, can dynamically adapt their entanglement generation and maintenance through real-time feedback. We propose a decentralized control system where reinforcement learning algorithms continuously optimize control parameters – such as laser pulse shapes and magnetic field strengths – based on metrics like entanglement fidelity and coherence time. This adaptive strategy mitigates the challenges associated with traditional, fixed-parameter control schemes, which often struggle to account for the inherent noise and fluctuations in quantum systems. The resulting architecture represents a significant step towards truly autonomous quantum computing, promising enhanced scalability and robustness. This paper details the theoretical framework, the reinforcement learning algorithm employed, and outlines the potential impact of this approach on future quantum hardware development.

Sep 5, 20260 citations