Nonclassical traits in multi-copy state discrimination
- URL: http://arxiv.org/abs/2604.26647v1
- Date: Wed, 29 Apr 2026 13:13:21 GMT
- Title: Nonclassical traits in multi-copy state discrimination
- Abstract summary: We consider minimum error discrimination of multi-copy states, where instead of preparing a single system we assume that multiple instances of the same state are prepared.<n>By comparing the average success probabilities in quantum and classical cases, we find a qubit strategy that outperforms all the bit strategies.
- Score: 0.4525084953138771
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum state discrimination is a fundamental information processing task that serves as a building block for numerous applications and provides implications at the foundational level. In this work, we consider minimum error discrimination of multi-copy states, where instead of preparing a single system we assume that multiple instances of the same state are prepared. Now the discrimination allows for measurements from multiple parties with different measurement strategies varying from global measurement strategy to ones restricted to different forms of local operations and classical communication strategies. By comparing the average success probabilities in quantum and classical cases, we find a qubit strategy that outperforms all the bit strategies. However, we find that there are other bit-like operational theories which can outperform the best qubit strategies even with a classical measurement strategy and we are able to identify instances of different theories where different measurement strategies are optimal. In this way, we are able to find instances of nonlocality without entanglement as well as provide general bounds for bit-like operational theories.
Related papers
- Structure of quantum measurements implementable with one round of classical communication [0.0]
We show how to distinguish between adaptive and non-adaptive measurements strategies.<n>We discuss explicit instances of state ensembles where non-projective measurements provide an advantage.
arXiv Detail & Related papers (2025-10-10T13:37:02Z) - Plan before Solving: Problem-Aware Strategy Routing for Mathematical Reasoning with LLMs [49.995906301946]
Existing methods usually leverage a fixed strategy to guide Large Language Models (LLMs) to perform mathematical reasoning.<n>Our analysis reveals that the single strategy cannot adapt to problem-specific requirements and thus overlooks the trade-off between effectiveness and efficiency.<n>We propose Planning and Routing through Instance-Specific Modeling (PRISM), a novel framework that decouples mathematical reasoning into two stages: strategy planning and targeted execution.
arXiv Detail & Related papers (2025-09-29T07:22:41Z) - Global vs. Local Discrimination of Locally Implementable Multipartite Unitaries [0.5872014229110214]
We study single-shot distinguishability of locally implementable multipartite unitaries under Local Operations and Classical Communication (LOCC) and global operations.<n>Our findings uncover three surprising features in the bipartite setting and establish new structural limits for unitary discrimination.
arXiv Detail & Related papers (2025-09-12T17:36:23Z) - Invariant Causal Imitation Learning for Generalizable Policies [87.51882102248395]
We propose Invariant Causal Learning (ICIL) to learn an imitation policy.
ICIL learns a representation of causal features that is disentangled from the specific representations of noise variables.
We show that ICIL is effective in learning imitation policies capable of generalizing to unseen environments.
arXiv Detail & Related papers (2023-11-02T16:52:36Z) - Selecting the suitable resampling strategy for imbalanced data
classification regarding dataset properties [62.997667081978825]
In many application domains such as medicine, information retrieval, cybersecurity, social media, etc., datasets used for inducing classification models often have an unequal distribution of the instances of each class.
This situation, known as imbalanced data classification, causes low predictive performance for the minority class examples.
Oversampling and undersampling techniques are well-known strategies to deal with this problem by balancing the number of examples of each class.
arXiv Detail & Related papers (2021-12-15T18:56:39Z) - Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level
Strategy [75.63022284445945]
We find that the existing works often build their few-shot model based on the image-level feature by mixing all local-level features.
We present (a) a local-agnostic training strategy to avoid the discriminative location bias between the base and novel categories, and (b) a novel local-level similarity measure to capture the accurate comparison between local-level features.
arXiv Detail & Related papers (2021-11-08T08:45:15Z) - Local simultaneous state discrimination [17.243339961137647]
Quantum state discrimination is one of the most fundamental problems studied in quantum information theory.
In this work, we introduce a new variant of this task: Local Simultaneous State Discrimination.
While interesting in its own right, this problem also arises in quantum cryptography.
arXiv Detail & Related papers (2021-11-01T19:03:37Z) - Experimental multi-state quantum discrimination through a Quantum
network [63.1241529629348]
We have experimentally implemented two discrimination schemes in a minimum-error scenario based on a receiver featured by a network structure and a dynamical processing of information.
The first protocol achieves binary optimal discrimination, while the second one provides a novel approach to multi-state quantum discrimination, relying on the dynamical features of the network-like receiver.
arXiv Detail & Related papers (2021-07-21T09:26:48Z) - Quantum process discrimination with restricted strategies [2.030567625639093]
We present a general formulation of the task of finding the maximum success probability for discriminating quantum processes.
We derive necessary and sufficient conditions for an optimal restricted strategy to be optimal within the set of all strategies.
This finding has the potential to provide a deeper insight into the discrimination performance of various restricted strategies.
arXiv Detail & Related papers (2021-04-19T04:02:51Z) - Reinforcement Learning with Neural Networks for Quantum Multiple
Hypothesis Testing [8.006109507455038]
Reinforcement learning with neural networks (RLNN) has recently demonstrated great promise for many problems.
We use RLNN to find locally-adaptive measurement strategies that are experimentally feasible.
We provide a new example which, to our knowledge, is the simplest known state set exhibiting a significant gap between local and collective protocols.
arXiv Detail & Related papers (2020-10-16T18:49:23Z) - Learning explanations that are hard to vary [75.30552491694066]
We show that averaging across examples can favor memorization and patchwork' solutions that sew together different strategies.
We then propose and experimentally validate a simple alternative algorithm based on a logical AND.
arXiv Detail & Related papers (2020-09-01T10:17:48Z)
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.