Noise-Robust Self-Testing: Detecting Non-Locality in Noisy Non-Local Inputs

• URL: http://arxiv.org/abs/2505.13537v1
• Date: Sun, 18 May 2025 16:55:33 GMT
• Title: Noise-Robust Self-Testing: Detecting Non-Locality in Noisy Non-Local Inputs
• Authors: Romi Lifshitz,
• Abstract summary: Non-local games test for non-locality and entanglement in quantum systems.<n>They are used in self-tests for certifying quantum states in untrusted devices.<n>Current literature lacks a standardized measure of noise-robustness.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Non-local games test for non-locality and entanglement in quantum systems and are used in self-tests for certifying quantum states in untrusted devices. However, these protocols are tailored to ideal states, so realistic noise prevents maximal violations and leaves many partially non-local states undetected. Selecting self-tests based on their 'robustness' to noise can tailor protocols to specific applications, but current literature lacks a standardized measure of noise-robustness. Creating such a measure is challenging as there is no operational measure for comparing tests of different dimensionalities and input-output settings. We propose and study three comparative measures: noise-tolerance, convincingness, and an analytic approximation of convincingness called the gapped score. Our computational experiments and analytic framework demonstrate that convincingness provides the most nuanced measure for noise-robustness. We then show that the CHSH game has the highest noise-robustness compared to more complex games (2-CHSH variants and the Magic Square Game) when given equal resources, while with unequal resources, some 2-CHSH variants can outperform CHSH at a high resource cost. This work provides the first systematic and operational framework for comparing noise-robustness in self-testing protocols, laying a foundation for theoretical advances in understanding noise-robustness of self-tests and practical improvements in quantum resource utilization.

Related papers

• Noise Augmented Fine Tuning for Mitigating Hallucinations in Large Language Models [1.0579965347526206]
  Large language models (LLMs) often produce inaccurate or misleading content-hallucinations.<n>Noise-Augmented Fine-Tuning (NoiseFiT) is a novel framework that leverages adaptive noise injection to enhance model robustness.<n>NoiseFiT selectively perturbs layers identified as either high-SNR (more robust) or low-SNR (potentially under-regularized) using a dynamically scaled Gaussian noise.
  arXiv  Detail & Related papers  (2025-04-04T09:27:19Z)
• Bayesian Quantum Amplitude Estimation [49.1574468325115]
  We present BAE, a problem-tailored and noise-aware Bayesian algorithm for quantum amplitude estimation.<n>In a fault tolerant scenario, BAE is capable of saturating the Heisenberg limit; if device noise is present, BAE can dynamically characterize it and self-adapt.<n>We propose a benchmark for amplitude estimation algorithms and use it to test BAE against other approaches.
  arXiv  Detail & Related papers  (2024-12-05T18:09:41Z)
• One-step Noisy Label Mitigation [86.57572253460125]
  Mitigating the detrimental effects of noisy labels on the training process has become increasingly critical. We propose One-step Anti-Noise (OSA), a model-agnostic noisy label mitigation paradigm. We empirically demonstrate the superiority of OSA, highlighting its enhanced training robustness, improved task transferability, ease of deployment, and reduced computational costs.
  arXiv  Detail & Related papers  (2024-10-02T18:42:56Z)
• Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation [91.83820250747935]
  Pseudo-label noise is mainly contained in unstable samples in which predictions of most pixels undergo significant variations during self-training. We introduce the Stable Neighbor Denoising (SND) approach, which effectively discovers highly correlated stable and unstable samples. SND consistently outperforms state-of-the-art methods in various SFUDA semantic segmentation settings.
  arXiv  Detail & Related papers  (2024-06-10T21:44:52Z)
• SoftPatch: Unsupervised Anomaly Detection with Noisy Data [67.38948127630644]
  This paper considers label-level noise in image sensory anomaly detection for the first time. We propose a memory-based unsupervised AD method, SoftPatch, which efficiently denoises the data at the patch level. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset.
  arXiv  Detail & Related papers  (2024-03-21T08:49:34Z)
• Compressed gate characterization for quantum devices with time-correlated noise [0.0]
  We present a general framework for quantum process tomography (QPT) in the presence of time-correlated noise. As an application of our method, we perform a comparative theoretical and experimental analysis of silicon spin qubits. We find good agreement between our theoretically predicted process fidelities and two qubit interleaved randomized benchmarking fidelities of 99.8% measured in recent experiments on silicon spin qubits.
  arXiv  Detail & Related papers  (2023-07-26T18:05:49Z)
• Characterizing and mitigating coherent errors in a trapped ion quantum processor using hidden inverses [0.20315704654772418]
  Quantum computing testbeds exhibit high-fidelity quantum control over small collections of qubits. These noisy intermediate-scale devices can support a sufficient number of sequential operations prior to decoherence. While the results of these algorithms are imperfect, these imperfections can help bootstrap quantum computer testbed development.
  arXiv  Detail & Related papers  (2022-05-27T20:35:24Z)
• Randomized benchmarking for non-Markovian noise [11.164202369517058]
  We combine the randomized benchmarking protocol with a framework describing non-Markovian quantum phenomena. We show that one can identify non-Markovian features of the noise directly from the ASF through its deviations from the Markovian case. Our methods are directly implementable and pave the pathway to better understanding correlated noise in quantum processors.
  arXiv  Detail & Related papers  (2021-07-12T13:10:05Z)
• Learning based signal detection for MIMO systems with unknown noise statistics [84.02122699723536]
  This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly detect signals with unknown noise statistics. In practice, there is little or even no statistical knowledge on the system noise, which in many cases is non-Gaussian, impulsive and not analyzable. Our framework is driven by an unsupervised learning approach, where only the noise samples are required.
  arXiv  Detail & Related papers  (2021-01-21T04:48:15Z)
• Efficient and robust certification of genuine multipartite entanglement in noisy quantum error correction circuits [58.720142291102135]
  We introduce a conditional witnessing technique to certify genuine multipartite entanglement (GME) We prove that the detection of entanglement in a linear number of bipartitions by a number of measurements scales linearly, suffices to certify GME. We apply our method to the noisy readout of stabilizer operators of the distance-three topological color code and its flag-based fault-tolerant version.
  arXiv  Detail & Related papers  (2020-10-06T18:00:07Z)
• Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing [14.684867444153625]
  Quantum machine learning models have the potential to offer speedups and better predictive accuracy compared to their classical counterparts. These quantum algorithms, like their classical counterparts, have been shown to be vulnerable to input perturbations. These can arise either from noisy implementations or, as a worst-case type of noise, adversarial attacks.
  arXiv  Detail & Related papers  (2020-09-21T17:55:28Z)
• Confidence Scores Make Instance-dependent Label-noise Learning Possible [129.84497190791103]
  In learning with noisy labels, for every instance, its label can randomly walk to other classes following a transition distribution which is named a noise model. We introduce confidence-scored instance-dependent noise (CSIDN), where each instance-label pair is equipped with a confidence score. We find with the help of confidence scores, the transition distribution of each instance can be approximately estimated.
  arXiv  Detail & Related papers  (2020-01-11T16:15:41Z)

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.