On the Characterization of Quantum Flip Stars with Quantum Network Tomography
July 12, 2023 Β· Declared Dead Β· π International Conference on Quantum Computing and Engineering
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Matheus Guedes de Andrade, Jake Navas, Inès Montaño, Don Towsley
arXiv ID
2307.05854
Category
quant-ph: Quantum Computing
Cross-listed
cs.NI
Citations
11
Venue
International Conference on Quantum Computing and Engineering
Last Checked
5 months ago
Abstract
The experimental realization of quantum information systems will be difficult due to how sensitive quantum information is to noise. Overcoming this sensitivity is central to designing quantum networks capable of transmitting quantum information reliably over large distances. Moreover, the ability to characterize communication noise in quantum networks is crucial in developing network protocols capable of overcoming the effects of noise in quantum networks. In this context, quantum network tomography refers to the characterization of channel noise in a quantum network through end-to-end measurements. In this work, we propose network tomography protocols for quantum star networks formed by quantum channels characterized by a single, non-trivial Pauli operator. Our results further the end-to-end characterization of quantum bit-flip star networks by introducing tomography protocols where state distribution and measurements are designed separately. We build upon previously proposed quantum network tomography protocols, as well as provide novel methods for the unique characterization of bit-flip probabilities in stars. We introduce a theoretical benchmark based on the Quantum Fisher Information matrix to compare the efficiency of quantum network protocols. We apply our techniques to the protocols proposed, and provide an initial analysis on the potential benefits of entanglement for Quantum Network Tomography. Furthermore, we simulate the proposed protocols using NetSquid to assess the convergence properties of the estimators obtained for particular parameter regimes. Our findings show that the efficiency of protocols depend on parameter values and motivate the search for adaptive quantum network tomography protocols.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Quantum Computing
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Quantum machine learning: a classical perspective
R.I.P.
π»
Ghosted
Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers
R.I.P.
π»
Ghosted
ProjectQ: An Open Source Software Framework for Quantum Computing
R.I.P.
π»
Ghosted
Quantum Recommendation Systems
R.I.P.
π»
Ghosted
Traffic flow optimization using a quantum annealer
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted