Robust self-test of the maximally entangled state of two-qubits without assuming unitary observables

• URL: http://arxiv.org/abs/2607.04035v1
• Date: Sat, 04 Jul 2026 21:31:49 GMT
• Title: Robust self-test of the maximally entangled state of two-qubits without assuming unitary observables
• Abstract summary: We establish a robust pure self-test for the singlet and Pauli observables.<n>We regularize the physical non-projective operators and derive an analytic $mathcalO(sqrt)$ robustness bound.
• Score: 46.00263113357165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Standard device-independent self-testing uses Naimark dilation to assume projective measurements, masking the operational limitations of realistic non-unitary observables. We establish a robust pure self-test for the singlet and Pauli observables that entirely circumvents dilation of the measurement apparatus. Assuming a pure state to model an untrusted source, we regularize the physical non-projective operators and derive an analytic $\mathcal{O}(\sqrtε)$ robustness bound. Our results suggest that device-independent certification of real implementations is significantly more demanding than standard projective models imply.

Related papers

• Robust self-testing based on Gisin's arbitrary-input Bell inequality [0.0]
  We demonstrate the self-testing of state and measurements based on the Gisin Bell inequality (GBI)<n>We introduce a systematic and elegant sum-of-squares (SOS) approach that enables the dimension-independent derivation of the optimal quantum violation of GBI.
  arXiv  Detail & Related papers  (2026-06-09T15:20:48Z)
• Sharp Convergence Rates for Masked Diffusion Models [53.117058231393834]
  We develop a total-variation based analysis for the Euler method that overcomes limitations.<n>Our results relax assumptions on score estimation, improve parameter dependencies, and establish convergence guarantees.<n>Overall, our analysis introduces a direct TV-based error decomposition along the CTMC trajectory and a decoupling-based path-wise analysis for FHS.
  arXiv  Detail & Related papers  (2026-02-26T00:47:51Z)
• Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks [1.4671424999873808]
  Uncertainty is critical in safety-sensitive applications but is often omitted from off-the-shelf neural networks due to adverse effects on predictive performance.<n>We propose a theoretically grounded framework for post-hoc uncertainty estimation in regression tasks by fitting an auxiliary model to both original inputs and frozen model outputs.
  arXiv  Detail & Related papers  (2025-06-01T09:13:27Z)
• Certifying classes of $d$-outcome measurements with quantum steering [49.1574468325115]
  We provide a construction of a family of steering inequalities tailored to large classes of $d$-outcomes projective measurements. We prove that the maximal quantum violation of those inequalities can be used for certification of those measurements and the maximally entangled state of two qudits.
  arXiv  Detail & Related papers  (2024-10-27T15:32:53Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• Device-independent certification of desirable properties with a confidence interval [0.0]
  We provide a versatile solution for rigorous device-independent certification. We show how the PBR protocol and the martingale-based protocol often offer similar performance. Our findings also show that the performance of the martingale-based protocol may be severely affected by one's choice of the witness.
  arXiv  Detail & Related papers  (2024-01-12T15:21:21Z)
• A mathematical foundation for self-testing: Lifting common assumptions [0.0]
  We study the phenomenon of self-testing from the first principles, aiming to place this versatile concept on a rigorous mathematical footing. We identify a quantum correlation which is a self-test only if certain assumptions are made. Remarkably, this is also the first example of a correlation that cannot be implemented using projective measurements on a bipartite state of full Schmidt rank.
  arXiv  Detail & Related papers  (2023-10-19T11:41:31Z)
• Provable Robustness for Streaming Models with a Sliding Window [51.85182389861261]
  In deep learning applications such as online content recommendation and stock market analysis, models use historical data to make predictions. We derive robustness certificates for models that use a fixed-size sliding window over the input stream. Our guarantees hold for the average model performance across the entire stream and are independent of stream size, making them suitable for large data streams.
  arXiv  Detail & Related papers  (2023-03-28T21:02:35Z)
• NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks [151.03112356092575]
  We show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. We demonstrate the strong performance of the method in uncertainty estimation tasks on a variety of real-world image datasets.
  arXiv  Detail & Related papers  (2022-02-07T12:30:45Z)
• Self-testing two-qubit maximally entangled states from generalized CHSH tests [0.0]
  We show how the self-testing of two-qubit maximally entangled states is improved by a refined analysis of measurement statistics. Our results not only provide novel insight into the set of quantum correlations suited for self-testing, but also facilitate the experimental implementations of device-independent certifications.
  arXiv  Detail & Related papers  (2020-11-05T19:00:00Z)

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.