Emphasis : Machine Learning and Communications Algorithms

Collaborators: Glen Uehara, GD and ASU PhD program, Kristen Jaskie, Postdoc

Graduate Students:   Leslie Miller, Tanay Patel, Nandika Goyal, Smylena Zebah Dsilva

Graduates of Program: Max Yarter GTRI,  Aradhita Sarma Analog Devices, Rajesh Kumar ASU,

Undergraduate Researchers:  Michael Esposito,  Kaden McGuffie, Niki Kyriakou, Salil Naik, Movinya Gunatilaka

Faculty Advisor: Andreas Spanias, Professor ECEE, Director SenSIP Center

Sponsors: General Dynamics, National Science Foundation, Quantum Collaborative, ASU Knowledge Enterprise, CIA Government Labs

Presentation:  Click Here To View

 

SenSIP Quantum Information Science and Machine Learning Student and Faculty Group

 

 

Current Projects:

1) Quantum Image Fusion Methods for Remote Sensing

Abstract— This project presents algorithms, simulations, and results using machine learning and quantum image fusion algorithms for radar and remote sensing applications. Previous efforts in the classification of synthetic aperture radar (SAR) images using quantum machine learning provided encouraging results but, nevertheless modest accuracy. In this paper, we propose a novel quantum image fusion technique used for identifying and classifying objects obtained from C-band SAR and optical images. More specifically, we design a four-qubit quantum circuit to process the SAR image dataset. This method enhances the spectral details otherwise not seen when using the raw SAR dataset. In addition to the quantum circuit, we design deep neural networks (NN) to improve classification results. The Visual Geometry Group 16 (VGG16), a convolutional neural network that is sixteen layers deep, is customized and used for classification. The merit of quantum fusion as well as the promising results in improving the overall system and lowering size, weight, power, and cost (SWaP-C) is described.

 

 

 

 

 

IEEE Citation: L. Miller, G. Uehara, A. Spanias, “Quantum Image Fusion Methods for Remote Sensing,” 2024 IEEE Aerospace Conference, Big Sky, MT, USA, 2024, pp. 1-8.

 

2) Quantum Linear Prediction For System Identification And Spectral Estimation Applications

Abstract—This project presents the design and implementation of quantum algorithms and circuits for linear prediction. The intended applications are system identification, spectral estimation, and speech processing. A frequency-domain method that uses the quantum Fourier transform is developed to estimate the autocorrelation sequence of the signal and a quantum circuit is designed to estimate the linear prediction parameters. The quantum linear prediction performance is evaluated using in system identification, autoregressive (AR) spectral estimation, and speech analysis simulations. More specifically, we evaluate the performance of the quantum linear predictor in terms of quantum noise effects, qubit precision, and overall computational requirements. Comparisons of quantum versus classical linear prediction are presented.

Keywords—Quantum linear prediction, quantum Fourier transform, HHL algorithm, qubits.

 

IEEE Citation: A Sharma, G. Uehara, and A. Spanias, “Quantum Linear Prediction For System Identification And Spectral Estimation Applications,” 2023 57th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2023.

 

3) Quantum Machine Learning for Photovoltaic Topology Optimization

Abstract—Photovoltaic array topology optimization was shown to improve efficiency in renewable energy plants. Previous studies demonstrated improvements via simulation at the level of 7-12% or more. In this project, we describe solar array topology optimization systems based on quantum machine learning algorithms. The idea of using quantum machine learning can be useful in cases where the objective is to optimize power output in large sites with several thousands of panels. We specifically propose and assess a quantum circuit for a neural network implementation for photovoltaic topology optimization. Results and comparisons are presented using classical and quantum neural network implementations. In addition, solar array topology optimization simulations and comparisons using a quantum neural network are described for different numbers of qubits.

 

IEEE Citation: G. Uehara, V. Narayanaswamy, C. Tepedelenlioglu, A. Spanias, “Quantum Machine Learning for Photovoltaic Topology Optimization,” 2022 IEEE 13th International Conference on Information, Intelligence, Systems & Applications (IISA), Hybrid Conference, July 2022.

 

Publications:

[1] T. Patel, D. Knutson, G. Uehara, C. Jazwin, F. Marfai, J. Larson, A. Spanias, “Image Analysis-Synthesis Using the Quantum Fourier Transform, Accepted, IEEE ISCAS 2025, London May 2025.

[2] S. Naik, N. Vaughn, A. Spanias, K. Jaskie,  “Quantum classification for synthetic aperture radar, SPIE Defense and Commercial Sensing,” National Harbor, Maryland, April 2024    Student Paper Award. – Click Here

[3] S. Naik, G. Uehara, L. Miller, K. Jaskie,  A. Spanias, Quantum Positive Unlabeled Learning Algorithms with Applications to Energy, IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2024,  London, September 2024. – Click Here

[4] L. Miller, G. Uehara, T, Patel, S. Naik, A. Spanias, Quantum Machine Learning and Spectrogram Fusion for Speech Recognition, IEEE Asilomar Conf. On Circuits, Systems and Computers, Monterey, Oct. 2024.

[5] L. Miller, G. Uehara, A. Spanias,  “Quantum Image Fusion Methods for Remote Sensing, ” IEEE Aerospace Conference, Big Sky, Montana, March 2024. – Click Here

[6] L. Miller, G. Uehara, A. Sharma and A. Spanias, “Quantum Machine Learning for Optical and SAR Classification,” 2023 24th International Conference on Digital Signal Processing (DSP), Rhodes (Rodos), Greece, 2023, pp. 1-5, doi: 10.1109/DSP58604.2023.10167979. – Click Here

[7] A. Sharma, G. Uehara, V. Narayanaswamy, L. Miller and A. Spanias, “Signal Analysis-Synthesis Using the Quantum Fourier Transform,” ICASSP 2023 – 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10096524. – Click Here

[8] A. Sharma, G. Uehara and A. Spanias, “Quantum Linear Prediction For System Identification And Spectral Estimation Applications,” IEEE Asilomar Conf. On Circuits, Systems and Computers, Monterey, Oct. 2023. – Click Here

[9] M. Yarter, G. Uehara, A. Spanias, “Investigating a Quantum Cloud Paradigm with Quantum Neural Networks,” IEEE MWCAS 2023, August 2023. – Click Here

[10] M. Esposito, G. Uehara, A. Spanias, Quantum Machine Learning for Audio Classification with Applications to Healthcare, 2022 IEEE 13th International Conference on Information, Intelligence, Systems & Applications (IISA), Hybrid Conference, July 2022. – Click Here

[11] M. Yarter, G. Uehara, A. Spanias, “Implementation and Analysis of Quantum Homomorphic Encryption,” 2022 IEEE 13th International Conference on Information, Intelligence, Systems & Applications (IISA), Hybrid Conference, July 2022. – Click Here

[12] G. Uehara, V. Narayanaswamy, C. Tepedelenlioglu, A. Spanias, “Quantum Machine Learning for Photovoltaic Topology Optimization,” 2022 IEEE 13th International Conference on Information, Intelligence, Systems & Applications (IISA), Hybrid Conference, July 2022. – Click Here

[13] Uehara, Glen S., Andreas Spanias, and William Clark. “Quantum information processing algorithms with emphasis on machine learning.” 2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA). IEEE, 2021. – Click Here

[14] G. Uehara, S. Rao, M. Dobson, C. Tepedelenlioglu, and A. Spanias, “Quantum Neural Network Parameter Estimation for PV Fault Detection.” In 2021 Twelfth IEEE International Conference on Information, Intelligence, Systems & Applications (IISA) 2021. – Click Here

Workforce Development:

J. Larson, D. Pujara, D. Ramirez, L. Miller, T. Patel, N. Babar, A. Spanias, WIP: Building a Research Experience for Undergraduates in Quantum Machine Learning, FIE 2024, DC, Oct. 2024.

Chao. Wang, Aradhita Sharma, Glen Uehara, Jean Larson, Wendy Barnard, Andreas Spanias, “Introducing Quantum Computing in a Sophomore Signals and Systems Course,” IEEE FIE 2023, College Station, Oct. 2023. – Click Here

Glen Uehara, Jean Larson, Wendy Barnard, Michael Esposito, Filippo Posta, Maxwell Yarter, Aradhita Sharma, Niki Kyriacou, Matthew Dobson, Andreas Spanias, “Undergraduate Research and Education in Quantum Machine Learning,” IEEE FIE 2022, Upsala, Oct. 2022. – Click Here

ASU SenSIP REU Site: Quantum Machine Learning Algorithm Design and Implementation – Click Here

 

AWARDS  /  HONORS related to Quantum Information Systems:

Impact Award for Quantum Machine Learning Work

    Mathew Dobson, Quantum Energy & Quantum Edge Detection – More Info

    Leslie Miller,  Quantum Imaging,   – More Info

   Michael Esposito, Quantum COVID-19 Audio, – More Info
    Aradhita Sharma, Quantum Signal Analysis Synthesis, – More Info

 

Thesis:

1. Barrett Honors Thesis in Quantum ML for Covid 19 Detection, M. Esposito, 2022.
2. Barrett Honors Thesis in Quantum ML for SAR Image Fusion, L. Miller, 2023.
3. Masters Thesis, Rajesh Kumar, Measuring Performance of Quantum Simulators for Machine Learning, 2023.
4. Master Thesis Maxwell Yarter, Investigating Quantum Approaches to Algorithm Privacy and Speech Processing, 2023.
5. Master Thesis Aradhita Sharma, Development of Signal Analysis Synthesis Methods Quantum Fourier Transforms and Quantum Linear Prediction Algorithms, 2023.

SenSIP – Tec de Monterrey Workshop, April 2023

The purpose of this workshop is to explore areas of collaboration of SenSIP with Tec de Monterrey on Quantum Machine Learning

 

Reports:  

Quantum Linear Prediction, Aradhita Sharma,Glen Uehara, Andreas Spanias, 2023.

Quantum Support Vector Machines for Energy, Niki Kyriakou,Glen Uehara and Andreas Spanias. 2023.

Quantum Machine Learning for Emotion Recognition, Kayden McGuffie, Glen Uehara and Andreas Spanias. 2023.

Quantum Image Edge Detection, Matthew Dobson, Glen Uehara and Andreas Spanias.2022.

Signal Analysis-Synthesis based on Quantum Fourier Transforms,  Aradhita Sharma,Glen Uehara, Andreas Spanias, 2022

Quantum Cloud-Edge Computing for Audio Recognition, Max Yarter, Glen Uehara and Andreas Spanias. 2022.

Participation in the Quantum Collaborative,