In order to reduce the computational cost for online calculation of the quantum filter, in this paper, a linear quantum projection filtering scheme is developed by projecting the time evolution trajectory of quantum system state onto a lower-dimensional linear submanifold spanned by a set of basis matrices. The lower-dimensional submanifold is obtained through an extended singular value decomposition algorithm. Compared with the existing works on quantum projection filtering, the proposed approach requires no prior knowledge on the structure of the submanifold, and thus can be used to deal with general open quantum systems. Simulation results demonstrate that the proposed linear projection filter is able to approximate the high-dimensional quantum filter within acceptable accuracy.

IEEE Symposium Series on Computational Intelligence (SSCI)Dec 1, 20202 citations

Development of a Novel Quantum Pre-processing Filter to Improve Image Classification Accuracy of Neural Network Models

Farina Riaz, Shahab Abdulla, Hajime Suzuki +3 more

This paper proposes a novel quantum pre-processing filter (QPF) to improve the image classification accuracy of neural network (NN) models. A simple four qubit quantum circuit that uses Y rotation gates for encoding and two controlled NOT gates for creating correlation among the qubits is applied as a feature extraction filter prior to passing data into the fully connected NN architecture. By applying the QPF approach, the results show that the image classification accuracy based on the MNIST (handwritten 10 digits) and the EMNIST (handwritten 47 class digits and letters) datasets can be improved, from 92.5% to 95.4% and from 68.9% to 75.9%, respectively. These improvements were obtained without introducing extra model parameters or optimizations in the machine learning process. However, tests performed on the developed QPF approach against a relatively complex German Traffic Sign Recognition Benchmark dataset with 43 distinct class real-life traffic sign images showed a degradation in the classification accuracy. Considering this result, further research into the understanding and the design of a more suitable quantum circuit approach for image classification NNs could be explored utilizing the baseline method proposed in this paper. Received: 2 September 2024 | Revised: 13 January 2025 | Accepted: 7 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in GitHub at https://github.com/hajimesuzuki999/qpf. Author Contribution Statement Farina Riaz: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Shahab Abdulla: Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Supervision. Hajime Suzuki: Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Srinjoy Ganguly: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Ravinesh C. Deo: Investigation, Resources, Data curation, Writing – review & editing, Supervision. Susan Hopkins: Writing – review & editing, Supervision.

arXiv:2308.11112Journal of Data Science and Intelligent SystemsMay 11, 20251 citation