Title: Quantum Error Mitigation via Adaptive Probabilistic Noise

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum error mitigation (QEM) is a crucial technique for enhancing the reliability of quantum computations. Current methods, however, often rely on fixed noise levels, which can be computationally demanding and limit efficiency. This paper introduces a novel QEM algorithm utilizing a Bayesian neural network to dynamically adjust the probabilistic noise applied during error correction. Our design systematically learns the optimal noise level for each quantum state, continuously adapting to the evolving error profile. This adaptive approach promises to significantly improve both error suppression and computational efficiency compared to existing methods. The proposed algorithm offers a fundamentally new way to optimize error mitigation, paving the way for more robust and practical quantum computations.

DOI: 10.5281/zenodo.22276960

Related papers

Title: Quantum Error Mitigation via Adaptive Bias Correction

Jincheng Zhang

Quantum computing holds immense promise for complex computations far exceeding the capabilities of classical computers. However, current quantum hardware suffers from inherent errors, which can severely limit the performance of these systems. Quantum error mitigation (QEM) techniques aim to detect and correct these errors, but they often require significant calibration and can be computationally demanding. This paper proposes a novel quantum error mitigation strategy employing an adaptive bias correction mechanism. Our approach dynamically adjusts bias correction parameters based on the observed error rate, minimizing the impact of errors on computation while maintaining high accuracy. We introduce a 'adaptive bias profile' algorithm that continuously monitors error patterns and optimizes correction strength to achieve optimal error reduction. This innovative strategy offers a more robust and adaptable approach to QEM, potentially reducing the need for extensive calibration and improving overall system performance. The results demonstrate the effectiveness of our method in mitigating errors across a range of quantum algorithms, showcasing its potential to significantly enhance the practical usability of quantum computation.

Sep 2, 20260 citations

Title: Probabilistic Quantum Error Mitigation via Adaptive Noise Injection

Jincheng Zhang

Quantum computing holds immense promise for revolutionary advancements across diverse fields, but its inherent sensitivity to noise necessitates sophisticated error mitigation strategies. Current approaches often rely on fixed, computationally intensive noise injection schemes, limiting scalability and requiring substantial overhead. This paper introduces a novel probabilistic error mitigation strategy leveraging real-time monitoring of quantum circuit error characteristics to dynamically adjust noise injection levels. The algorithm, termed Adaptive Noise Injection (ANI), continuously assesses the error using a simplified error metric and adjusts the noise injection accordingly. We demonstrate the effectiveness of ANI through simulations and preliminary experiments, achieving significant error reduction compared to static mitigation methods while maintaining high accuracy. This research addresses a critical limitation in quantum error mitigation, paving the way for more efficient and practical quantum computations.

Sep 6, 20260 citations

Title: Quantum Error Mitigation with Hybrid Quantum-Classical Techniques

Jincheng Zhang

Quantum error mitigation (QEM) is a crucial step in developing practical quantum algorithms, allowing for the correction of errors introduced during computation. However, the inherent complexity of quantum systems and the need for computationally intensive error correction mechanisms have historically limited the widespread adoption of QEM. This paper proposes a novel hybrid quantum-classical approach to QEM, integrating classical computation for error correction with dynamically adjusting quantum computations to minimize the impact of noise. We demonstrate the efficacy of this strategy through a series of simulations and analysis, showcasing a significant reduction in error rates compared to traditional methods. The core mechanism involves a feedback loop that prioritizes error correction based on real-time error metrics, utilizing classical computation to optimize the quantum circuit's execution. This approach addresses the limitations of existing methods by offering a more efficient and scalable solution for QEM, paving the way for broader quantum computing applications.

Aug 31, 20260 citations

Title: Bayesian Variational Quantum Error Mitigation with Adaptive Kernel Selection (BVQ-AKS)

Jincheng Zhang

Quantum computation promises unprecedented computational power, but inherent quantum errors pose a significant barrier to widespread adoption. Variational Quantum Error Mitigation (VQEM) is a crucial technique for reducing these errors, but its effectiveness is heavily dependent on the specific error profile of the quantum circuit. Traditional VQEM methods often rely on a fixed kernel, failing to adequately address the diverse error characteristics across different quantum systems. This paper proposes a novel Bayesian Variational Quantum Error Mitigation (BVQ-AKS) strategy that dynamically adapts the kernel selection process based on the circuit's error profile. The algorithm learns an optimal kernel for each circuit type, enhancing mitigation effectiveness across a broader range of errors. This approach offers a tailored and adaptive method to address the challenges of VQEM, paving the way for more robust and reliable quantum computations.

Sep 2, 20260 citations

Quantum Error Mitigation via Adaptive Noise Injection

Jincheng Zhang

Quantum computing holds immense promise for solving complex problems intractable for classical computers. However, quantum systems are notoriously susceptible to errors, making accurate computations challenging. Quantum error mitigation (QEM) techniques aim to reduce these errors, but current methods often rely on fixed parameters, limiting their effectiveness. This paper proposes a novel approach to QEM utilizing adaptive noise injection, dynamically adjusting the amount of noise based on the observed error profile. We demonstrate that this method significantly improves the accuracy of quantum computations, offering a more robust and adaptable solution compared to existing methods. The system incorporates a continuous error rate monitoring and noise injection mechanism, optimizing for minimal impact on the computation.

Aug 31, 20260 citations

Title: Quantum Error Mitigation via Dynamic Bias Tuning

Jincheng Zhang

Quantum error correction (QEC) is a critical component of quantum computing, enabling reliable computation by protecting quantum information from noise. However, current QEC codes are highly sensitive to errors, necessitating sophisticated error mitigation strategies. This paper presents a novel approach to QEC, utilizing dynamic bias tuning to dynamically adjust bias parameters within quantum error correction codes. We demonstrate a significant improvement in accuracy and reduced overhead compared to static bias adjustment methods. The proposed system employs a self-tuning algorithm that analyzes the error profile and automatically adjusts bias parameters, offering a highly adaptive and effective means of mitigating errors. This work addresses a key challenge in QEC – the need for continuous optimization – and opens the door to more robust and efficient quantum computation.

Sep 3, 20260 citations