Quantum Image Processing

Quantum Computing in the Image Processing

In the context of our focus on »Quantum Image Processing« we investigate to what extent quantum computers (QC) can solve classical image processing problems. In addition to the theoretical results, we focus on the practical implementation on current hardware and the identification of new possibilities.

We start from conventional images, thus the classical image information has to be converted into quantum states. In order to explore the current possibilities, we performed experiments on both, quantum computer simulators and real backends: Classical images were converted to quantum states and back into classical images. In these experiments, we succeeded in practically demonstrating the theoretically promised advantage of exponentially fewer qubits (quantum bits) compared to classical bits.

However, full exploitation of this advantage is hampered by the flaws of current quantum computers, which strongly limit the executability of algorithms. Currently, images of maximum size 16 by 16 pixels can be processed, but only two by two pixel gray value images are still interpretable after a cycle of coding and decoding. Larger images are noisy beyond recognition. For details please check the paper »Improved FRQI on superconducting processors and its restrictions in the NISQ era« by Geng et al. (see below).

Our improvements of the encoding method reduce errors and memory consumption and increase the size of processable images to 32 by 32 pixels. Currently, we focus on developing hybrid image processing algorithms making use of the quantum computing advantages in spite of this size restriction.

Current Projects and Activities within the Competence Center Quantum Computing

In our projects AnQuC-3 and EniQmA we investigate new fields of quantum image processing. In addition to the content component, the continuing education program »Quantum Technology Professional« prepares and teaches basic quantum concepts as well as the current research results for software engineers, data scientists, QC researchers and technology scouts.

Circuit depth for varying image sizes on backend ’ibmq toronto’ with standard implemented method and our improvement.
© Fraunhofer ITWM
Circuit depth for varying image sizes on backend ’ibmq toronto’ with standard implemented method and our improvement.

Overview Example Projects and Workshops

 

Quantum Computing, CT Data and Concrete

In the DAnoBi project (Detection of Anomalies in Image Data), we are developing methods that find and segment cracks in concrete even in images of the size 400GB. In the future, quantum computing should accelerate the analysis of CT data.

AnQuC-3

In the project »AnQuC-3« we focus on Quantum Fourier Transformation, Quantum Machine Learning and Algorithms. 

More information will follow soon.

 

Quantum Technology Professional

In the Project »Quantum Technology Professional« project, we are developing a modular and continuously expandable continuing education program with the topics »Quantum Computing« and »Quantum Technology«.