Quantum Databases for Dynamic Data Storage
Project Goal
Exploring the usage of a quantum database with potential quantum indexing and classical or quantum data elements that may be initially of unknown length. By classical, we mean specifically cloneable states in terms of the no-cloning theorem. Thus, we focus on creating an algorithmic procedure for efficiently manipulating such states in quantum software and exploring its use cases and applications.
Overview
Key elements in quantum mechanics include the presence of superposition states and entanglement. In particular, the superposition principle allows access to data in a coherent way. Thus, within the concept of a quantum database, an algorithmic framework for efficiently manipulating structured data in such a superposition state is provided. In our project, we are, for example, 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 data manipulation methods and the specific data operations similar to classical databases that are applied within a dynamical scenario in quantum software.
Highlights in 2024
In 2024, we developed a set of efficient algorithms that implement the protocols mimicking classical database operations in the quantum scenario with respect to quantum indexing and explored their feasibility. This is relevant for future use cases and may serve as a guideline to define which classical database operations are, in general, useful as soon as quantum indexing or quantum data is present. The algorithms were formally defined and implemented as a proof of concept in Python and C++ using the Intel Quantum simulator.
Next Steps
The project’s next steps will be to focus on various applications and precise use cases.
More Information
Project Coordinator: Michele Grossi, Sofia Vallecorsa
Technical Team: Michele Grossi, Gian Giacomo Guerreschi, Carla Sophie Rieger, Sofia Vallecorsa, Martin Werner
Collaboration Liaisons: Gian Giacomo Guerreschi, Martin Werner
In partnership with: Intel, TUM