Classical Capacity of Arbitrarily Distributed Noisy Quantum Channels
- URL: http://arxiv.org/abs/2306.16102v1
- Date: Wed, 28 Jun 2023 11:14:12 GMT
- Title: Classical Capacity of Arbitrarily Distributed Noisy Quantum Channels
- Authors: Indrakshi Dey, Harun Siljak, Nicola Marchetti
- Abstract summary: We study the impact of a mixture of classical and quantum noise on an arbitrary quantum channel carrying classical information.
We formulate the achievable channel capacity over an arbitrary distributed quantum channel in presence of the mixed noise.
- Score: 11.30845610345922
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the rapid deployment of quantum computers and quantum satellites, there
is a pressing need to design and deploy quantum and hybrid classical-quantum
networks capable of exchanging classical information. In this context, we
conduct the foundational study on the impact of a mixture of classical and
quantum noise on an arbitrary quantum channel carrying classical information.
The rationale behind considering such mixed noise is that quantum noise can
arise from different entanglement and discord in quantum transmission
scenarios, like different memories and repeater technologies, while classical
noise can arise from the coexistence with the classical signal. Towards this
end, we derive the distribution of the mixed noise from a classical system's
perspective, and formulate the achievable channel capacity over an arbitrary
distributed quantum channel in presence of the mixed noise. Numerical results
demonstrate that capacity increases with the increase in the number of photons
per usage.
Related papers
- Quantum-Classical Separations in Shallow-Circuit-Based Learning with and without Noises [5.018448337319583]
We study quantum-classical separations between classical and quantum supervised learning models based on constant depth (i.e., shallow) circuits.
We rigorously prove that any classical neural network with bounded connectivity requires logarithmic depth to output correctly with a larger-than-exponentially-small probability.
arXiv Detail & Related papers (2024-05-01T18:00:01Z) - A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - Power Characterization of Noisy Quantum Kernels [52.47151453259434]
We show that noise may make quantum kernel methods to only have poor prediction capability, even when the generalization error is small.
We provide a crucial warning to employ noisy quantum kernel methods for quantum computation.
arXiv Detail & Related papers (2024-01-31T01:02:16Z) - Routing and wavelength assignment in hybrid networks with classical and
quantum signals [0.0]
We propose a simple method for routing and wavelength assignment in wavelength multiplexed networks in which classical and quantum channels coexist.
Theses reduce the shared path between classical and quantum channels and improve the signal-to-noise ratio in the quantum channels, improving their quantum key rate.
arXiv Detail & Related papers (2023-11-17T12:04:00Z) - Information capacity analysis of fully correlated multi-level amplitude
damping channels [0.9790236766474201]
We investigate some of the information capacities of the simplest member of multi-level Amplitude Damping Channel, a qutrit channel.
We find the upper bounds of the single-shot classical capacities and calculate the quantum capacities associated with a specific class of maps.
arXiv Detail & Related papers (2023-05-08T06:10:56Z) - Quantum-Classical Hybrid Information Processing via a Single Quantum
System [1.1602089225841632]
Current technologies in quantum-based communications bring a new integration of quantum data with classical data for hybrid processing.
We propose a quantum reservoir processor to harness quantum dynamics in computational tasks requiring both classical and quantum inputs.
arXiv Detail & Related papers (2022-09-01T14:33:40Z) - The Entanglement-Assisted Communication Capacity over Quantum
Trajectories [6.836162272841265]
We show that indefinite causal order of quantum channels enables the violation of bottleneck capacity.
We derive capacity expressions of entanglement-assisted classical and quantum communication for arbitrary quantum Pauli channels.
arXiv Detail & Related papers (2021-10-15T13:09:54Z) - Quantum information spreading in a disordered quantum walk [50.591267188664666]
We design a quantum probing protocol using Quantum Walks to investigate the Quantum Information spreading pattern.
We focus on the coherent static and dynamic disorder to investigate anomalous and classical transport.
Our results show that a Quantum Walk can be considered as a readout device of information about defects and perturbations occurring in complex networks.
arXiv Detail & Related papers (2020-10-20T20:03:19Z) - Experimental Quantum Generative Adversarial Networks for Image
Generation [93.06926114985761]
We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor.
Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
arXiv Detail & Related papers (2020-10-13T06:57:17Z) - Quantum noise protects quantum classifiers against adversaries [120.08771960032033]
Noise in quantum information processing is often viewed as a disruptive and difficult-to-avoid feature, especially in near-term quantum technologies.
We show that by taking advantage of depolarisation noise in quantum circuits for classification, a robustness bound against adversaries can be derived.
This is the first quantum protocol that can be used against the most general adversaries.
arXiv Detail & Related papers (2020-03-20T17:56:14Z) - Entanglement Classification via Neural Network Quantum States [58.720142291102135]
In this paper we combine machine-learning tools and the theory of quantum entanglement to perform entanglement classification for multipartite qubit systems in pure states.
We use a parameterisation of quantum systems using artificial neural networks in a restricted Boltzmann machine (RBM) architecture, known as Neural Network Quantum States (NNS)
arXiv Detail & Related papers (2019-12-31T07:40:23Z)
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.