This project focuses on the development of quantum algorithms for advanced image and video processing, with applications in areas such as climate modeling, medical imaging, satellite data analysis, and weather forecasting. The goal is to harness the unique capabilities of quantum computing to optimize data representation, compression, and processing, ultimately setting new benchmarks for efficiency and accuracy in data handling.

Quantum image processing (QIP) refers to the use of quantum computing principles to process and manipulate images. The motivation for exploring QIP lies in the growing computational complexity of classical image processing tasks, especially when handling large datasets such as in high-resolution imaging, video processing. Quantum computing promises to offer advantages in speed and efficiency over classical methods, particularly in dealing with large-scale data.

Why Do We Use Quantum Image Processing?

• Exponential Speedup: Classical image processing algorithms often face limitations when the dataset grows large, both in terms of time and memory usage. Quantum computing, through superposition and entanglement, can provide exponential speedups for certain tasks. For example, quantum algorithms can process an entire image in a superposition of states simultaneously, potentially leading to faster image transformations.

• Handling Complex Transformations: Tasks such as image compression, edge detection, filtering, and segmentation can become computationally expensive as the complexity of images increases. Quantum algorithms might offer more efficient methods to perform these operations, using quantum gates and states to achieve results faster.

• Quantum Advantage in Data Representation: Quantum image representations (like FRQI, NEQR, or BRQI) are designed to store images more compactly in quantum bits (qubits) as opposed to classical bits. These representations use fewer resources and could allow for high-quality image storage with lower memory requirements on quantum memory.

Applications of Quantum Image Processing:

• Medical Imaging: Quantum image processing could enhance techniques like MRI, CT scans, or X-rays by improving image resolution, compressing data, or providing faster processing.

• Remote Sensing and Satellite Imaging: For environmental monitoring or military surveillance, quantum algorithms could process large volumes of satellite data more efficiently, improving the speed and quality of image analysis.

• Computer Vision: Quantum image processing can assist in applications requiring feature extraction, pattern recognition, and object detection, which are integral to autonomous vehicles, robotics, and augmented reality systems.

• Climate Modeling: Quantum image processing may improve the analysis of satellite imagery used in climate research by accelerating the processing of large datasets and enabling better predictions of climate patterns.

In summary, quantum image processing promises to revolutionize fields that rely on processing large and complex image datasets by offering exponential speedup, improved data compression, and enhanced feature extraction, all of which can lead to more accurate and efficient processing. However, practical implementations are still in their early stages, and much of the potential remains theoretical as quantum computing hardware and algorithms continue to mature.

Climate Modelling and Weather Forecasting:

• Quantum image processing algorithms may enhance climate and weather models by providing advanced data compression and analysis, enabling faster and more accurate forecasting.

Satellite and Remote Sensing Image Analysis:

• Quantum encoding methods may enable efficient storage and analysis of large satellite image datasets, supporting applications in environmental monitoring, disaster management, and resource tracking in the future.

Medical Imaging Data Compression:

• Quantum algorithms can help medical images classification, such as MRIs and CT scans, or preserving crucial details while reducing storage needs and processing times in healthcare.

Quantum-Enhanced Autonomous Vehicle Vision Systems: 

• Quantum video compression allows real-time processing of high-definition video feeds in autonomous vehicles, reducing latency and improving object detection and decision-making.

Next-Generation Media Streaming:

• Quantum video encoding and compression can transform media streaming, reducing bandwidth requirements and improving streaming quality for high-resolution content in constrained networks when quantum channels is available in the near future.

• Innovative Quantum Encoding Algorithms: Novel quantum encoding algorithms for efficient image and video representation, optimizing data storage and fidelity in quantum systems.

• Quantum Neural Network (QNN) Integration: Specialized QNN layers trained on quantum-encoded image states to enhance compression quality and processing speed.

• High-Resolution Quantum Media Processing: Quantum encoding methods designed for high-resolution images and videos, supporting complex data sets in climate and environmental analysis.

• Real-world applications in Climate Modeling and Forecasting: State-of-the-art use cases for weather forecasting and climate modeling, leveraging quantum image processing for more accurate and efficient environmental predictions.

• Optimization and Deployment Readiness: A robust pipeline for algorithm testing, refinement, and optimization, ensuring that the quantum encoding and processing algorithms are ready for deployment in real-world environments.

• Capacity Building and Knowledge Transfer: Comprehensive documentation, capacity building, and training to equip users and partners with the skills to adopt, deploy, and innovate upon developed quantum solutions.

For Quantum Simulations 

CPU requirements 

Key Specifications of Intel® Core™ i9-13900KS Processor :

• Total Cores: 24 cores, with 8 performance cores and 16 efficient cores, designed for multitasking and handling mixed workloads.

• Total Threads: 32 threads, enabling substantial parallelism for multi-threaded applications.

• Frequencies:

 o Max Turbo Frequency: Up to 6.00 GHz, using Intel’s Thermal Velocity Boost for peak performance.

 o Base Frequencies: Performance cores at 3.20 GHz, efficient cores at 2.40 GHz.

GPU Requirements 

Key Specifications of NVIDIA V100 :

• Performance: 7.8 teraFLOPS for double-precision, 15.7 teraFLOPS for single-precision, and 125 teraFLOPS for deep learning 

• Interconnect bandwidth: 300 GB/s bi-directional with NVIDIA NVLink and 32 GB/s PCIe 

• Memory: 16 or 32 GB of CoWoS Stacked HBM2 with 900 GB/s bandwidth  

• Other features: 5,120 CUDA cores, 640 Tensor Cores, passive bidirectional heat sink, unified virtual memory, migration engine, and Maximum Performance (Max-P) and Maximum Efficiency (Max-Q) modes

•  IBM Quantum SDK- Qiskit

•  Xanadu Quantum Technologies SDK- Pennylane

Abhishek Tiwari, Scientist E.

Embedded system Group,

CDAC Noida

abhishek@cdac.in

Contact no. : +91 99718 11440