Related Experiment Video
Updated: Jan 25, 2026

05:30
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
1.1K
Randomness certification in a quantum network with independent sources
Giorgio Minati1, Giovanni Rodari1, Emanuele Polino1,2
1Dipartimento di Fisica, Sapienza Università di Roma, P.le Aldo Moro 5, I-00185 Roma, Italy.
Science Advances
|January 23, 2026
Summary
This study demonstrates certifying randomness in quantum networks with multiple entanglement sources, crucial for secure quantum communication. The method bounds an eavesdropper
Area of Science:
- Quantum Information Science
- Quantum Communication Security
- Quantum Networking
Background:
- Randomness certification is vital for quantum communication security.
- Traditional protocols use single entanglement sources, limiting network complexity.
- Scaling quantum networks requires multi-source entanglement validation.
Purpose of the Study:
- To develop a method for randomness certification in multi-source entanglement experiments.
- To address challenges in certifying randomness with complex quantum network configurations.
- To provide bounds on eavesdropper knowledge in quantum networks.
Main Methods:
- Developed a theoretical model for randomness certification in entanglement-teleportation experiments.
- Utilized the scalar extension method to handle non-convex correlation sets.
- Analyzed experimental data from a photonic quantum network for validation.
Main Results:
- Successfully characterized certifiable randomness in a two-source entanglement network.
- Provided effective bounds on an eavesdropper's knowledge of shared secret bits.
- Validated the theoretical model using experimental photonic quantum network data.
Conclusions:
- The developed method enables randomness certification in complex quantum networks.
- This work is a key step towards robust, large-scale quantum networks.
- The findings enhance the security of quantum communication protocols.
Related Concept Videos
Independent and Dependent Sources
2.6K
In electrical circuits, sources play a crucial role in providing power for the operation of the circuit. These sources can be broadly categorized into two types: independent and dependent.
Independent voltage or current sources supply a fixed amount of voltage or current, respectively, which is unaffected by other elements within the circuit. These are represented using specific symbols. Independent voltage sources are symbolized with polarities (+ and -), indicating the direction of the...
Independent voltage or current sources supply a fixed amount of voltage or current, respectively, which is unaffected by other elements within the circuit. These are represented using specific symbols. Independent voltage sources are symbolized with polarities (+ and -), indicating the direction of the...
2.6K
Quantum Numbers
49.5K
It is said that the energy of an electron in an atom is quantized; that is, it can be equal only to certain specific values and can jump from one energy level to another but not transition smoothly or stay between these levels.
49.5K
The Quantum-Mechanical Model of an Atom
56.8K
Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
56.8K
Introduction to Test of Independence
2.9K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
2.9K
Hypothesis Test for Test of Independence
7.4K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
H0: The two variables (factors)...
7.4K
Law of Independent Assortment
62.4K
While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
62.4K

