Utilizing probabilistic entanglement between sensors in quantum networks
- URL: http://arxiv.org/abs/2407.15652v1
- Date: Mon, 22 Jul 2024 14:12:30 GMT
- Title: Utilizing probabilistic entanglement between sensors in quantum networks
- Authors: Emily A. Van Milligen, Christos N. Gagatsos, Eneet Kaur, Don Towsley, Saikat Guha,
- Abstract summary: One of the most promising applications of quantum networks is entanglement assisted sensing.
This work outlines when and how to use entanglement, when to store it, and when it needs to be distilled.
- Score: 8.59730790789283
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: One of the most promising applications of quantum networks is entanglement assisted sensing. The field of quantum metrology exploits quantum correlations to improve the precision bound for applications such as precision timekeeping, field sensing, and biological imaging. When measuring multiple spatially distributed parameters, current literature focuses on quantum entanglement in the discrete variable case, and quantum squeezing in the continuous variable case, distributed amongst all of the sensors in a given network. However, it can be difficult to ensure all sensors pre-share entanglement of sufficiently high fidelity. This work probes the space between fully entangled and fully classical sensing networks by modeling a star network with probabilistic entanglement generation that is attempting to estimate the average of local parameters. The quantum Fisher information is used to determine which protocols best utilize entanglement as a resource for different network conditions. It is shown that without entanglement distillation there is a threshold fidelity below which classical sensing is preferable. For a network with a given number of sensors and links characterized by a certain initial fidelity and probability of success, this work outlines when and how to use entanglement, when to store it, and when it needs to be distilled.
Related papers
- Quantum Advantage in Distributed Sensing with Noisy Quantum Networks [37.23288214515363]
We show that quantum advantage in distributed sensing can be achieved with noisy quantum networks.
We show that while entanglement is needed for this quantum advantage, genuine multipartite entanglement is generally unnecessary.
arXiv Detail & Related papers (2024-09-25T16:55:07Z) - Multi-User Entanglement Distribution in Quantum Networks Using Multipath
Routing [55.2480439325792]
We propose three protocols that increase the entanglement rate of multi-user applications by leveraging multipath routing.
The protocols are evaluated on quantum networks with NISQ constraints, including limited quantum memories and probabilistic entanglement generation.
arXiv Detail & Related papers (2023-03-06T18:06:00Z) - Neural networks for Bayesian quantum many-body magnetometry [0.0]
Entangled quantum many-body systems can be used as sensors that enable the estimation of parameters with a precision larger than that achievable with ensembles of individual quantum detectors.
This entails a complexity that can hinder the applicability of Bayesian inference techniques.
We show how to circumvent these issues by using neural networks that faithfully reproduce the dynamics of quantum many-body sensors.
arXiv Detail & Related papers (2022-12-22T22:13:49Z) - Adaptive, Continuous Entanglement Generation for Quantum Networks [59.600944425468676]
Quantum networks rely on entanglement between qubits at distant nodes to transmit information.
We present an adaptive scheme that uses information from previous requests to better guide the choice of randomly generated quantum links.
We also explore quantum memory allocation scenarios, where a difference in latency performance implies the necessity of optimal allocation of resources for quantum networks.
arXiv Detail & Related papers (2022-12-17T05:40:09Z) - Cluster-Promoting Quantization with Bit-Drop for Minimizing Network
Quantization Loss [61.26793005355441]
Cluster-Promoting Quantization (CPQ) finds the optimal quantization grids for neural networks.
DropBits is a new bit-drop technique that revises the standard dropout regularization to randomly drop bits instead of neurons.
We experimentally validate our method on various benchmark datasets and network architectures.
arXiv Detail & Related papers (2021-09-05T15:15:07Z) - Entanglement Rate Optimization in Heterogeneous Quantum Communication
Networks [79.8886946157912]
Quantum communication networks are emerging as a promising technology that could constitute a key building block in future communication networks in the 6G era and beyond.
Recent advances led to the deployment of small- and large-scale quantum communication networks with real quantum hardware.
In quantum networks, entanglement is a key resource that allows for data transmission between different nodes.
arXiv Detail & Related papers (2021-05-30T11:34:23Z) - Integrable quantum many-body sensors for AC field sensing [0.0]
We show that integrable many-body systems can be exploited efficiently for detecting the amplitude of an AC field.
We show that the proposed protocol can also be realized in near-term quantum simulators.
arXiv Detail & Related papers (2021-05-27T23:52:22Z) - Optimizing Entanglement Generation and Distribution Using Genetic
Algorithms [0.640476282000118]
Long-distance quantum communication via entanglement distribution is of great importance for the quantum internet.
Quantum repeaters could in theory be used to extend the distances over which entanglement can be distributed, but in practice hardware quality is still lacking.
We propose a methodology based on genetic algorithms and simulations of quantum repeater chains for optimization of entanglement generation and distribution.
arXiv Detail & Related papers (2020-10-30T17:09:34Z) - Searching for Low-Bit Weights in Quantized Neural Networks [129.8319019563356]
Quantized neural networks with low-bit weights and activations are attractive for developing AI accelerators.
We present to regard the discrete weights in an arbitrary quantized neural network as searchable variables, and utilize a differential method to search them accurately.
arXiv Detail & Related papers (2020-09-18T09:13:26Z) - Semidefinite tests for quantum network topologies [0.9176056742068814]
Quantum networks play a major role in long-distance communication, quantum cryptography, clock synchronization, and distributed quantum computing.
The question of which correlations a given quantum network can give rise to, remains almost uncharted.
We show that constraints on the observable covariances, previously derived for the classical case, also hold for quantum networks.
arXiv Detail & Related papers (2020-02-13T22:36:46Z) - Machine learning transfer efficiencies for noisy quantum walks [62.997667081978825]
We show that the process of finding requirements on both a graph type and a quantum system coherence can be automated.
The automation is done by using a convolutional neural network of a particular type that learns to understand with which network and under which coherence requirements quantum advantage is possible.
Our results are of importance for demonstration of advantage in quantum experiments and pave the way towards automating scientific research and discoveries.
arXiv Detail & Related papers (2020-01-15T18:36:53Z)
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.