Quantum State Classifier to Optimize the Transmission of Highly Entangled States

[pdc-quantum]

Description

The aim is to optimize the quantum computing resource during the transmission of highly entangled states pertaining to a finite set. For this purpose, a quantum state classifier based on distance matrix computation uses a predetermined reference probability distribution matrix (PDM), either empirically observed or corresponding to an ideal device. A Monte Carlo simulation determines the minimal number of copies of any state at transmission allowing to maintain the detection error below a given threshold. Current data are presently obtained on eight real devices available through the IBM quantum experience using a set of 20 highly entangled separable 5-qubit states. Three works in progress are proposed for the trainee(s). They may involve defining theory, experimentation, programming and statistics:

• Improve the quantum state classifier and the Monte Carlo simulation process
• Adapt the program to any set of highly entangled states with variable qubit number.
• Demonstrate the superiority of the empirical PDM on the ideal one.

Mentor/s

Pierre Decoodt

Type of participant

What are the profiles of the ideal participants for this idea?:

In addition to programming in Qiskit, familiarity with one of these domains will be helpful for the team: Optimization, Machine Learning, Statistics and Probabilities

Number of participants

3 (1 to 4)

Deliverable

• Notebooks in Qiskit
• A paper (or maybe more)
• Contribution(s) to Qiskit Medium