Robust Parametric Quantum Gate Against Stochastic Time-Varying Noise
- URL: http://arxiv.org/abs/2603.24345v1
- Date: Wed, 25 Mar 2026 14:23:30 GMT
- Title: Robust Parametric Quantum Gate Against Stochastic Time-Varying Noise
- Abstract summary: Filter function-enhanced QCRL integrates filter function formalism into the quantum control robustness landscape.<n>A representative single-qubit setting confirms the effectiveness of the proposed method.
- Score: 4.975274985832929
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The performance of quantum processors in the noisy intermediate-scale quantum (NISQ) era is severely constrained by environmental noise and other uncertainties. While the recently proposed quantum control robustness landscape (QCRL) offers a powerful framework for generating robust control pulses for parametric gate families, its application has been practically restricted to quasi-static noise. To address the spectrally complex, time-varying noise prevalent in reality, we propose filter function-enhanced QCRL (FF-QCRL), which integrates filter function formalism into the QCRL framework. The resulting FF-QCRL algorithm minimizes a generalized robustness metric that faithfully encodes the impact of stochastic processes, enabling robust pulse-family generation for parametric gates under realistic time-varying noise. Numerical validation in a representative single-qubit setting confirms the effectiveness of the proposed method.
Related papers
- Control-centric quantum noise spectroscopy of time-ordered polyspectra [13.127624971317536]
Non-parametric quantum noise spectroscopy provides a general-purpose, model-agnostic framework for estimating the spectral properties of an environment.<n>We show how adopting a control-centric point of view allows one to recast the noise spectroscopy problem in such a way that the central objects are now the time-ordered polyspectra.
arXiv Detail & Related papers (2026-04-09T01:02:14Z) - OptiQKD: A Machine Learning-Optimized Framework for Real-Time Parameter Tuning in Quantum Key Distribution [0.0]
We propose OptiQKD, a protocol-agnostic machine learning framework specifically engineered to maximize the Secure Key Rate (SKR) and minimize the Quantum Bit Error Rate (QBER) for the BB84, E91, and COW protocols.<n>We evaluate the framework by simulating critical environmental stressors, including depolarizing and amplitude-damping noise, under realistic device constraints.
arXiv Detail & Related papers (2026-03-04T15:43:31Z) - Adaptive Fidelity Estimation for Quantum Programs with Graph-Guided Noise Awareness [30.900274564864223]
QuFid is an adaptive and noise-aware framework that determines measurement budgets online.<n>We show that QuFid significantly reduces measurement cost compared to fixed-shot and learning-based baselines.
arXiv Detail & Related papers (2026-01-21T07:04:05Z) - Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
arXiv Detail & Related papers (2026-01-10T02:36:03Z) - Variational noise mitigation in quantum circuits: the case of Quantum Fourier Transform [35.18016233072556]
We perform numerical simulations for two qubits under both coherent and incoherent noise.<n>Our results show that the variational circuit can reproduce the QFT with higher fidelity in scenarios dominated by coherent noise.<n>This demonstrates the potential of the approach as an effective error-mitigation strategy for small- to medium-scale quantum systems.
arXiv Detail & Related papers (2025-11-07T14:35:55Z) - Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures [2.005299372367689]
We introduce a quantum reinforcement learning (QRL) controller that dynamically adjusts CFG at each denoising step.<n>The controller adopts a hybrid quantum--classical actor--critic architecture.<n> Experiments on CIFAR-10 demonstrate that our QRL policy improves perceptual quality.
arXiv Detail & Related papers (2025-09-17T16:47:04Z) - TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing [50.95799256262098]
We introduceHyper-VQC, a novel tensor-train (TT)-guided hypernetwork framework for quantum machine learning.<n>Our framework delegates the generation of quantum circuit parameters to a classical TT network, effectively decoupling optimization from quantum hardware.<n>These results positionHyper-VQC as a scalable and noise-resilient framework for advancing practical quantum machine learning on near-term devices.
arXiv Detail & Related papers (2025-08-01T23:37:55Z) - Dynamic Estimation Loss Control in Variational Quantum Sensing via Online Conformal Inference [39.72602887300498]
Current variational quantum sensing methods lack rigorous performance guarantees.<n>This paper proposes an online control framework for VQS that dynamically updates the variational parameters while providing deterministic error bars on the estimates.<n> Experiments on a quantum magnetometry task confirm that the proposed dynamic VQS approach maintains the required reliability over time, while still yielding precise estimates.
arXiv Detail & Related papers (2025-05-29T12:19:07Z) - Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise [49.97673761305336]
Noise remains a major obstacle to achieving reliable quantum algorithms.<n>We present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers.
arXiv Detail & Related papers (2025-05-24T02:51:34Z) - Fidelity-Enhanced Variational Quantum Optimal Control [0.0]
We propose a new method for creating robust pulses based on the Schr"odinger equation.<n>By accounting for both environmental noise sources as well as noise sources inherent to the control system, highly significant increases in fidelity are noted for both single and multiqubit state preparations.
arXiv Detail & Related papers (2025-01-29T14:59:34Z) - Traversing Quantum Control Robustness Landscapes: A New Paradigm for Quantum Gate Engineering [0.0]
We introduce the Quantum Control Robustness Landscape (QCRL), a conceptual framework that maps control parameters to noise susceptibility.<n>By navigating through the level sets of the QCRL, our Robustness-Invariant Pulse Variation (RIPV) algorithm allows for the variation of control pulses while preserving robustness.<n> Numerical simulations demonstrate that our single- and two-qubit gates exceed the quantum error correction threshold even with substantial noise.
arXiv Detail & Related papers (2024-12-27T05:56:38Z) - Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal
Abstractions [59.605246463200736]
We present a novel controller synthesis method that does not rely on any explicit representation of the noise distributions.
First, we abstract the continuous control system into a finite-state model that captures noise by probabilistic transitions between discrete states.
We use state-of-the-art verification techniques to provide guarantees on the interval Markov decision process and compute a controller for which these guarantees carry over to the original control system.
arXiv Detail & Related papers (2023-01-04T10:40:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.