Optimal one-shot entanglement sharing
- URL: http://arxiv.org/abs/2301.01781v2
- Date: Fri, 6 Oct 2023 16:13:07 GMT
- Title: Optimal one-shot entanglement sharing
- Authors: Vikesh Siddhu and John Smolin
- Abstract summary: We discuss a practical setting where a quantum channel is used once with the aim of sharing high fidelity entanglement.
For any channel, we provide methods to easily find both this maximum fidelity and optimal inputs that achieve it.
This ensures a complete understanding in the sense that the maximum fidelity and optimal inputs found in our one-shot setting extend even when the channel is used multiple times.
- Score: 3.2634122554914002
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Sharing entanglement across quantum interconnects is fundamental for quantum
information processing. We discuss a practical setting where this interconnect,
modeled by a quantum channel, is used once with the aim of sharing high
fidelity entanglement. For any channel, we provide methods to easily find both
this maximum fidelity and optimal inputs that achieve it. Unlike most metrics
for sharing entanglement, this maximum fidelity can be shown to be
multiplicative. This ensures a complete understanding in the sense that the
maximum fidelity and optimal inputs found in our one-shot setting extend even
when the channel is used multiple times, possibly with other channels. Optimal
inputs need not be fully entangled. We find the minimum entanglement in these
optimal inputs can even vary discontinuously with channel noise. Generally,
noise parameters are hard to identify and remain unknown for most channels.
However, for all qubit channels with qubit environments, we provide a rigorous
noise parametrization which we explain in-terms of no-cloning. This noise
parametrization and a channel representation we call the standard Kraus
decomposition have pleasing properties that make them both useful more
generally.
Related papers
- Flagged Extensions and Numerical Simulations for Quantum Channel Capacity: Bridging Theory and Computation [0.0]
I will investigate the capacities of noisy quantum channels through a combined analytical and numerical approach.<n>I introduce novel flagged extension techniques that embed a channel into a higher-dimensional space.<n>My results refine previous bounds and clarify noise thresholds beyond which quantum transmission vanishes.
arXiv Detail & Related papers (2025-06-03T22:21:08Z) - 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) - Distilling Channels for Efficient Deep Tracking [68.13422829310835]
This paper presents a novel framework termed channel distillation to facilitate deep trackers.
We show that an integrated formulation can turn feature compression, response map generation, and model update into a unified energy minimization problem.
The resulting deep tracker is accurate, fast, and has low memory requirements.
arXiv Detail & Related papers (2024-09-18T08:09:20Z) - Unextendible entanglement of quantum channels [4.079147243688764]
We study the ability of quantum channels to perform quantum communication tasks.
A quantum channel can distill a highly entangled state between two parties.
We generalize the formalism of $k$-extendibility to bipartite superchannels.
arXiv Detail & Related papers (2024-07-22T18:00:17Z) - Accurate and Honest Approximation of Correlated Qubit Noise [39.58317527488534]
We propose an efficient systematic construction of approximate noise channels, where their accuracy can be enhanced by incorporating noise components with higher qubit-qubit correlation degree.
We find that, for realistic noise strength typical for fixed-frequency superconducting qubits, correlated noise beyond two-qubit correlation can significantly affect the code simulation accuracy.
arXiv Detail & Related papers (2023-11-15T19:00:34Z) - Multiparameter estimation with two qubit probes in noisy channels [0.618778092044887]
This work compares the performance of single and two qubit probes for estimating several phase rotations simultaneously.
We compute the quantum limits for this simultaneous estimation using collective and individual measurements.
In sufficiently noisy channels, we show that it is possible for single qubit probes to outperform maximally entangled two qubit probes.
arXiv Detail & Related papers (2023-07-26T03:20:48Z) - Simultaneous superadditivity of the direct and complementary channel
capacities [0.0]
We show that coherent and private information of a channel and its complement can be simultaneously superadditive for arbitrarily many channel uses.
For a varying number of channel uses, we show that these quantities can obey different interleaving sequences of inequalities.
arXiv Detail & Related papers (2023-01-12T16:58:12Z) - Fault-tolerant Coding for Entanglement-Assisted Communication [46.0607942851373]
This paper studies the study of fault-tolerant channel coding for quantum channels.
We use techniques from fault-tolerant quantum computing to establish coding theorems for sending classical and quantum information in this scenario.
We extend these methods to the case of entanglement-assisted communication, in particular proving that the fault-tolerant capacity approaches the usual capacity when the gate error approaches zero.
arXiv Detail & Related papers (2022-10-06T14:09:16Z) - Statistical intrusion detection and eavesdropping in quantum channels
with coupling: Multiple-preparation and single-preparation methods [2.2469167925905777]
Non-quantum communications include configurations with multiple-input multiple-output (MIMO) channels.
Some associated signal processing tasks consider these channels in a symmetric way, i.e. by assigning the same role to all inputs.
We here address asymmetric (blind and non-blind) ones, with emphasis on intrusion detection and additional comments about eavesdropping.
arXiv Detail & Related papers (2021-06-17T07:04:54Z) - Detecting positive quantum capacities of quantum channels [9.054540533394926]
A noisy quantum channel can be used to reliably transmit quantum information at a non-zero rate.
This is because it requires computation of the channel's coherent information for an unbounded number of copies of the channel.
We show that a channel's ability to transmit information is intimately connected to the relative sizes of its input, output, and environment spaces.
arXiv Detail & Related papers (2021-05-13T14:26:45Z) - Channel-wise Knowledge Distillation for Dense Prediction [73.99057249472735]
We propose to align features channel-wise between the student and teacher networks.
We consistently achieve superior performance on three benchmarks with various network structures.
arXiv Detail & Related papers (2020-11-26T12:00:38Z) - Entanglement-assisted entanglement purification [62.997667081978825]
We present a new class of entanglement-assisted entanglement purification protocols that can generate high-fidelity entanglement from noisy, finite-size ensembles.
Our protocols can deal with arbitrary errors, but are best suited for few errors, and work particularly well for decay noise.
arXiv Detail & Related papers (2020-11-13T19:00:05Z) - 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)
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.