Probing graph topology from local quantum measurements
- URL: http://arxiv.org/abs/2507.23689v2
- Date: Fri, 01 Aug 2025 09:52:43 GMT
- Title: Probing graph topology from local quantum measurements
- Authors: F. Romeo, J. Settino,
- Abstract summary: We show that global properties of an unknown quantum network, such as the average degree, hub density, and the number of closed paths of fixed length, can be inferred from strictly local quantum measurements.<n>We demonstrate that a malicious agent with access to only a small subset of nodes can initialize quantum states locally and, through repeated short-time measurements, extract sensitive structural information about the entire network.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We show that global properties of an unknown quantum network, such as the average degree, hub density, and the number of closed paths of fixed length, can be inferred from strictly local quantum measurements. In particular, we demonstrate that a malicious agent with access to only a small subset of nodes can initialize quantum states locally and, through repeated short-time measurements, extract sensitive structural information about the entire network. The intrusion strategy is inspired by extreme learning and quantum reservoir computing and combines short-time quantum evolution with a non-iterative linear readout with trainable weights. These results suggest new strategies for intrusion detection and structural diagnostics in future quantum Internet infrastructures.
Related papers
- Clustering-induced localization of quantum walks on networks [0.0]
Quantum walks on networks are a paradigmatic model in quantum information theory.<n>We show how localization emerges in highly clustered networks.<n>We then show that localization also occurs in Kleinberg small-world networks and Holme--Kim power-law cluster networks.
arXiv Detail & Related papers (2024-12-05T16:40:57Z) - Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Guarantees on the structure of experimental quantum networks [105.13377158844727]
Quantum networks connect and supply a large number of nodes with multi-party quantum resources for secure communication, networked quantum computing and distributed sensing.
As these networks grow in size, certification tools will be required to answer questions regarding their properties.
We demonstrate a general method to guarantee that certain correlations cannot be generated in a given quantum network.
arXiv Detail & Related papers (2024-03-04T19:00:00Z) - Learning quantum properties from short-range correlations using multi-task networks [3.7228085662092845]
We introduce a neural network model that can predict various quantum properties of many-body quantum states with constant correlation length.
The model is based on the technique of multi-task learning, which we show to offer several advantages over traditional single-task approaches.
arXiv Detail & Related papers (2023-10-18T08:53:23Z) - Quantum information spreading and scrambling in a distributed quantum
network: A Hasse/Lamport diagrammatic approach [14.308249733521182]
Large-scale quantum networks, known as quantum internet, hold great promises for advanced distributed quantum computing and long-distance quantum communication.
We propose a novel diagrammatic way of visualizing information flow dynamics within the quantum network.
We also propose a quantum information scrambling protocol, where a specific node scrambles secret quantum information across the entire network.
arXiv Detail & Related papers (2023-09-19T06:48:42Z) - Practical limitations on robustness and scalability of quantum Internet [0.7499722271664144]
We study the limitations on the scaling and robustness of quantum Internet.
We present practical bottlenecks for secure communication, delegated computing, and resource distribution among end nodes.
For some examples of quantum networks, we present algorithms to perform different quantum network tasks of interest.
arXiv Detail & Related papers (2023-08-24T12:32:48Z) - Overlapping qubits from non-isometric maps and de Sitter tensor networks [41.94295877935867]
We show that processes in local effective theories can be spoofed with a quantum system with fewer degrees of freedom.
We highlight how approximate overlapping qubits are conceptually connected to Hilbert space dimension verification, degree-of-freedom counting in black holes and holography.
arXiv Detail & Related papers (2023-04-05T18:08:30Z) - Inferring Quantum Network Topology using Local Measurements [3.549868541921029]
We propose an efficient protocol for distinguishing and inferring the topology of a quantum network.
We show that the protocol can be entirely robust to noise and can be implemented via quantum variational optimization.
arXiv Detail & Related papers (2022-12-15T17:36:12Z) - QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional
Networks [124.7972093110732]
We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations.
To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections.
Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
arXiv Detail & Related papers (2022-11-09T21:43:16Z) - The Hintons in your Neural Network: a Quantum Field Theory View of Deep
Learning [84.33745072274942]
We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles.
On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
arXiv Detail & Related papers (2021-03-08T17:24:29Z) - 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) - Quantum State Discrimination on Reconfigurable Noise-Robust Quantum
Networks [6.85316573653194]
A fundamental problem in Quantum Information Processing is the discrimination amongst a set of quantum states of a system.
In this paper, we address this problem on an open quantum system described by a graph, whose evolution is defined by a Quantum Walk.
We optimize the parameters of the network to obtain the highest probability of correct discrimination.
arXiv Detail & Related papers (2020-03-25T19:07:03Z) - 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.