Quantum State Classifier to Optimize the Transmission of Highly Entangled States #27
Description
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