Gaussian quantum data hiding
- URL: http://arxiv.org/abs/2502.00670v1
- Date: Sun, 02 Feb 2025 04:46:00 GMT
- Title: Gaussian quantum data hiding
- Authors: Yunkai Wang, Graeme Smith,
- Abstract summary: Quantum data hiding encodes a hidden classical bit to a pair of quantum states that is difficult to distinguish using a particular set of measurement, denoted as $M$.
- Score: 4.9684872842145875
- License:
- Abstract: Quantum data hiding encodes a hidden classical bit to a pair of quantum states that is difficult to distinguish using a particular set of measurement, denoted as $M$. In this work, we explore quantum data hiding in two contexts involving Gaussian operations or states. First, we consider the set of measurement $M$ as Gaussian local quantum operations and classical communication, a new set of operations not previously discussed in the literature for data hiding. We hide one classical bit in the two different mixture of displaced two-mode squeezed states. Second, we consider the set of measurement $M$ as general Gaussian measurement and construct the data hiding states using two-mode thermal states. This data hiding scheme is effective in the weak strength limit, providing a new example compared to existing discussions for the set of general Gaussian measurement.
Related papers
- Classical shadow tomography for continuous variables quantum systems [13.286165491120467]
We introduce two experimentally realisable schemes for obtaining classical shadows of CV quantum states.
We are able to overcome new mathematical challenges due to the infinite-dimensionality of CV systems.
We provide a scheme to learn nonlinear functionals of the state, such as entropies over any small number of modes.
arXiv Detail & Related papers (2022-11-14T17:56:29Z) - Deterministic Gaussian conversion protocols for non-Gaussian single-mode
resources [58.720142291102135]
We show that cat and binomial states are approximately equivalent for finite energy, while this equivalence was previously known only in the infinite-energy limit.
We also consider the generation of cat states from photon-added and photon-subtracted squeezed states, improving over known schemes by introducing additional squeezing operations.
arXiv Detail & Related papers (2022-04-07T11:49:54Z) - Efficient Bipartite Entanglement Detection Scheme with a Quantum
Adversarial Solver [89.80359585967642]
Proposal reformulates the bipartite entanglement detection as a two-player zero-sum game completed by parameterized quantum circuits.
We experimentally implement our protocol on a linear optical network and exhibit its effectiveness to accomplish the bipartite entanglement detection for 5-qubit quantum pure states and 2-qubit quantum mixed states.
arXiv Detail & Related papers (2022-03-15T09:46:45Z) - Detecting entanglement in arbitrary two-mode Gaussian state: a
Stokes-like operator based approach [1.3999481573773072]
Detection of entanglement in quantum states is one of the most important problems in quantum information processing.
We present an interferometric scheme to test the same separability criterion in which the measurements are being done via Stokes-like operators.
arXiv Detail & Related papers (2021-03-24T04:54:35Z) - Quantum data hiding with continuous variable systems [8.37609145576126]
We investigate data hiding in the context of continuous variable quantum systems.
We look at the case where $mathcalM=mathrmLOCC$, the set of measurements implementable with local operations and classical communication.
We perform a rigorous quantitative analysis of the error introduced by the non-ideal Braunstein-Kimble quantum teleportation protocol.
arXiv Detail & Related papers (2021-02-01T19:00:14Z) - Bose-Einstein condensate soliton qubit states for metrological
applications [58.720142291102135]
We propose novel quantum metrology applications with two soliton qubit states.
Phase space analysis, in terms of population imbalance - phase difference variables, is also performed to demonstrate macroscopic quantum self-trapping regimes.
arXiv Detail & Related papers (2020-11-26T09:05:06Z) - Limit of Gaussian operations and measurements for Gaussian state
discrimination, and its application to state comparison [0.0]
We consider the so-called constant-$hatp$ displaced states which include mixtures of multimode coherent states arbitrarily displaced along a common axis.
We first show that no global or local Gaussian transformations or generalized Gaussian measurements can lead to a better discrimination method than simple homodyne measurements applied to each mode separately and classical postprocessing of the results.
arXiv Detail & Related papers (2020-08-31T21:17:44Z) - Neural network quantum state tomography in a two-qubit experiment [52.77024349608834]
Machine learning inspired variational methods provide a promising route towards scalable state characterization for quantum simulators.
We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states.
We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states greatly improves the quality of the reconstructed states.
arXiv Detail & Related papers (2020-07-31T17:25:12Z) - Gaussian conversion protocols for cubic phase state generation [104.23865519192793]
Universal quantum computing with continuous variables requires non-Gaussian resources.
The cubic phase state is a non-Gaussian state whose experimental implementation has so far remained elusive.
We introduce two protocols that allow for the conversion of a non-Gaussian state to a cubic phase state.
arXiv Detail & Related papers (2020-07-07T09:19:49Z) - Quantum Gram-Schmidt Processes and Their Application to Efficient State
Read-out for Quantum Algorithms [87.04438831673063]
We present an efficient read-out protocol that yields the classical vector form of the generated state.
Our protocol suits the case that the output state lies in the row space of the input matrix.
One of our technical tools is an efficient quantum algorithm for performing the Gram-Schmidt orthonormal procedure.
arXiv Detail & Related papers (2020-04-14T11:05:26Z) - Quantum embeddings for machine learning [5.16230883032882]
Quantum classifiers are trainable quantum circuits used as machine learning models.
We propose to train the first part of the circuit -- the embedding -- with the objective of maximally separating data classes in Hilbert space.
This approach provides a powerful analytic framework for quantum machine learning.
arXiv Detail & Related papers (2020-01-10T19:00:01Z)
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.