Quantum Databases for Dynamic Data Storage

Project Goal

This collaboration of CERN and Intel is exploring the usage of a quantum database for storage of classical and quantum data which may be initially of unknown length. Thus, we are aiming to create an algorithmic procedure for an efficient dynamic data storage. 

Background

Quantum data itself is fragile, subject to decoherence and collapses when being measured. Due to the collapse of the quantum state upon quantum measurement, for future applications it will be needed to store the quantum state itself in form of a quantum memory for further data processing based on quantum algorithms. The idea of a quantum database is to store the individual quantum states containing the quantum or classical data itself in a superposition correlated with a label state which is used for indexing. In our project, we are mainly concerned with data obtained through experiments over an unknown temporal interval. Thus, such experimental data can be inherently of non-predefined length, e.g., the runtime of the experiment is not given initially. Hence, we are mainly concerned about its resource-efficient storage method and the specific data manipulation operations that are applied within a dynamical database. 

Progress

In the year 2023, for this project we were focusing on the extension of the development of a theoretical model of the quantum database and algorithmic solutions for data manipulation with respect to different use-cases that go beyond high-energy physics. Within a quantum database the quantum state itself is stored instead of the classical data obtained though measurement and hence a quantum state collapse is avoided. Future quantum computing devices working with state-of-the-art quantum algorithms are supposed to make use of a quantum memory and would directly be able to process the stored quantum data. Hence, this technique of storage is also useful as a resource-efficient and compact quantum state preparation technique to obtain initial quantum states of, e.g., quantum machine learning models. Altogether, we are aiming to store as much information in our quantum state resulting from a temporally dynamical experimental setting and provide a set of manipulation techniques to operate within the database.

Next Steps

Current theoretical results are very promising. The goal is to provide a complete algorithmic toolkit of database manipulation operations of the dynamic quantum database which we have started to develop. Furthermore, we are going to explore specific use-cases and further possible applications of the quantum database model. For the future, we aim to add more operations and refine our solution.   

 

Project Coordinator: Sofia Vallecorsa

Technical Team: Michele Grossi, Carla Rieger, Sofia Vallecorsa, Martin Werner (TUM)

Intel Collaboration Liaisons: Gian Giacomo Guerreschi

In partnership with: Intel