Related papers: Profitable entanglement for channel discrimination

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Characterizing quantum channels from local-unitary invariants [0.0]
We introduce averaged local-unitary invariants, referred to as moments, obtained from Haar integrals over input states or unitaries.<n>These moments provide computable descriptions of how a quantum channel can create, preserve, or destroy bipartite entanglement.
arXiv Detail & Related papers (2026-06-02T14:42:14Z)
Entangling power and fidelity diagnostic for bipartite quantum channels [0.0]
We show that the fidelity averaged over any fixed Schmidt-coefficients local-unitary orbit for equal local dimensions is completely determined by average input-output fidelity and its restriction to product inputs.<n>We also introduce concurrence- and negativity-based entangling power for 2-qubit channels, prove their convexity and monotonicity under local postprocessing, and show that, unlike the previously proposed linear-entropy quantity, they vanish for all separable channels.
arXiv Detail & Related papers (2026-05-26T11:25:54Z)
CP Loss: Channel-wise Perceptual Loss for Time Series Forecasting [67.3477355449697]
We propose a Channel-wise Perceptual Loss (CP Loss) for time-series data.<n>We learn a unique perceptual space for each channel that is adapted to its characteristics.<n>Loss is calculated within these perception spaces to optimize the model.
arXiv Detail & Related papers (2026-01-25T15:31:37Z)
Adversarial Hypothesis Testing for Quantum Channels [57.214837874007856]
We study adversarial hypothesis testing for quantum-quantum (QQ) and classical-quantum (CQ) channels.<n>For QQ channels with i.i.d. inputs, Bob's knowledge of the input significantly enhances distinguishability.<n>For CQ channels, Bob being informed provides a consistent advantage over the corresponding entanglement-breaking channels for both i.i.d. and general inputs.
arXiv Detail & Related papers (2026-01-15T10:04:10Z)
Quantum channel discrimination against jammers [5.232225863167099]
We study the problem of quantum channel discrimination between two channels with an adversary input party (a.k.a. a jammer)<n>We introduce the notion of minimax channel divergence and establish several of its key properties.<n>We prove that the optimal type-II exponent error in the regime under parallel strategies is characterized by the regularized minimax channel divergence.
arXiv Detail & Related papers (2025-10-09T09:10:09Z)
String commitment from unstructured noisy channels [53.04878543623513]
Noisy channels are valuable resources for cryptography, enabling primitives like bit commitment and oblivious transfer. We present a protocol for string commitment over such channels that is complete, hiding, and binding, and derive its achievable commitment rate. The commitment rate coincides with previous results when the adversarial channels are the same binary symmetric channel as in the honest case.
arXiv Detail & Related papers (2024-12-31T05:28:05Z)
Resolvability of classical-quantum channels [54.825573549226924]
We study the resolvability of classical-quantum channels in two settings, for the channel output generated from the worst input, and form the fixed independent and identically distributed (i.i.d.) input. For the fixed-input setting, while the direct part follows from the known quantum soft covering result, we exploit the recent alternative quantum Sanov theorem to solve the strong converse.
arXiv Detail & Related papers (2024-10-22T05:18:43Z)
Security for adversarial wiretap channels [4.383205675898942]
We analyze information-theoretically secure coding schemes which use the inverse of an extractor and an error-correcting code. We show that this also holds for certain channel types with memory.
arXiv Detail & Related papers (2024-04-02T09:22:40Z)
Composite Classical and Quantum Channel Discrimination [6.553031877558699]
We study the problem of binary composite channel discrimination in the asymmetric setting, where the hypotheses are given by fairly arbitrary sets of channels. We show that there can be an advantage to channel discrimination strategies with composite hypotheses for classical channels, unlike in general general simple hypotheses.
arXiv Detail & Related papers (2023-03-03T15:31:38Z)
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization [59.038366742773164]
Linears and leaky ReLU trained by gradient flow on logistic loss have an implicit bias towards satisfying the Karush-KuTucker (KKT) conditions. In this work we establish a number of settings where the satisfaction of these conditions implies benign overfitting in linear classifiers and in two-layer leaky ReLU networks.
arXiv Detail & Related papers (2023-03-02T18:24:26Z)
Entanglement Breaking Rank via Complementary Channels and Multiplicative Domains [4.588028371034406]
We introduce a new technique to determine if a channel is entanglement breaking and to evaluate entanglement breaking rank. We show the entanglement breaking and Choi ranks of such channels are equal.
arXiv Detail & Related papers (2022-11-21T23:33:10Z)
Revisiting Random Channel Pruning for Neural Network Compression [159.99002793644163]
Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of neural networks. In this paper, we try to determine the channel configuration of the pruned models by random search. We show that this simple strategy works quite well compared with other channel pruning methods.
arXiv Detail & Related papers (2022-05-11T17:59:04Z)
Bi-PPT channels are entanglement breaking [77.34726150561087]
We show that bi-PPT channels are always entanglement breaking. We also show that degradable quantum channels staying completely positive under composition with a transposition are entanglement breaking.
arXiv Detail & Related papers (2022-04-04T17:53:38Z)
Channel-wise Alignment for Adaptive Object Detection [66.76486843397267]
Generic object detection has been immensely promoted by the development of deep convolutional neural networks. Existing methods on this task usually draw attention on the high-level alignment based on the whole image or object of interest. In this paper, we realize adaptation from a thoroughly different perspective, i.e., channel-wise alignment.
arXiv Detail & Related papers (2020-09-07T02:42:18Z)
Operation-Aware Soft Channel Pruning using Differentiable Masks [51.04085547997066]
We propose a data-driven algorithm, which compresses deep neural networks in a differentiable way by exploiting the characteristics of operations. We perform extensive experiments and achieve outstanding performance in terms of the accuracy of output networks.
arXiv Detail & Related papers (2020-07-08T07:44:00Z)
Optimal measurement preserving qubit channels [1.90365714903665]
We consider the problem of discriminating qubit states that are sent over a quantum channel and derive a necessary and sufficient condition for an optimal measurement to be preserved by the channel. We show how the sets of OMP channels for a given ensemble can be constructed.
arXiv Detail & Related papers (2020-06-29T20:40:35Z)
Coherent Quantum Channel Discrimination [6.345523830122166]
Coherent quantum channel discrimination is a quantum interactive proof system between a verifier and a prover. I prove that this success probability does not increase under the action of a quantum superchannel. I provide an explicit semi-definite program that can compute the success probability.
arXiv Detail & Related papers (2020-01-08T18:47:08Z)

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