Hybrid Quantum Computing

Quantum computers have the potential to calculate certain computationally intensive problems faster than classical computers. Examples of such problems in the database environment are data tree patterns as a query language for XML trees¹ or keyword searches in (graph) databases². The data to be processed to solve these problems is organized in database models. However, quantum computers cannot access this data directly and require the data to be processed to be encoded in a suitable way.  

The project pursues two main objectives:

• Implementation of a hybrid system that enables the exchange of data between applications, databases and quantum computers. It consists of classic components and quantum hardware and must be able to manage data flows.
• The data and information available in different structures and models must be encoded accordingly before they can be processed on a quantum computer. The aim is to develop suitable encodings.  

Our solution approach: We design and implement a hybrid system, the Hybrid Data Management Architecture (HDMA). We opted for a decentralized approach with a lightweight, asynchronous communication mechanism by adopting the microservice paradigm. In this architecture, databases can serve as data sources for quantum algorithms. This thus serves as a framework for researching and testing suitable encodings. The basic idea is summarized in the following figure:

The architecture consists of a series of services. They enable an application to encode data from a database, initiate a calculation on the encoded data on a quantum computer and return the result of the calculation.  

Example: A graph (A) to be colored is retrieved from the database. To color the graph, a circuit of the Grover algorithm is generated with a problem-specific oracle. (B) shows a small segment of the circuit and encodes graph (A). After executing the circuit on a quantum computer, a solution instance (C) is stored in the database.

Selected publications

• Zajac, M., & Störl, U. (2023, November). Hybrid Data Management Architecture for Present Quantum Computing. In International Conference on Service-Oriented Computing (pp. 174-184). Singapore: Springer Nature Singapore.
• Zajac, M. (2023, August). Encoding and Provisioning Data in different Data Models for Quantum Computing. In Conference on Very Large Data Bases (VLDB 2023).
• Zajac, M., & Störl, U. (2022, July). Towards quantum-based Search for industrial Data-driven Services. In 2022 IEEE International Conference on Quantum Software (QSW) (pp. 38-40). IEEE.

Student theses

We regularly publish new topics for theses. An overview of open topics can be found here: dbis theses

Ongoing

currently none

Completed

• Encoding classical data to address production and logistics problems with gate-based quantum computers (T. Haschke, September 2023)
• Linking data from different NoSQL document stores using Grover’s quantum search algorithm: Towards a quantum-classical hybrid approach (M. Dang, August 2022)

¹ David, C. (2008, August). Complexity of data tree patterns over XML documents.
² Manolescu, I., & Mohanty, M. (2023, August). Full-Power Graph Querying: State of the Art and Challenges