Compressing and storing images presents a significant challenge for quantum computing, demanding efficient methods to represent visual data using limited qubits. Sahil Tomar and Sandeep Kumar, both from the Central Research Laboratory, BEL, address this problem with a novel approach to quantum image compression. Their research introduces a technique that transforms images into a histogram of intensity values, then encodes this information as the amplitudes of a quantum state. This method achieves a crucial advantage by maintaining a constant qubit requirement, determined only by the desired level of detail in the image, and not by its overall size, offering a substantial improvement over traditional pixel-based quantum encoding schemes. The team demonstrates high-fidelity image reconstructions using remarkably few qubits, between five and seven, validating the potential of this technique for practical application on near-term quantum devices.
Histogram Embedding for Quantum Image Compression
This paper details a novel approach to quantum image compression, presenting a compelling argument for its potential advantages over existing techniques. The method demonstrates a significant departure from traditional pixel-wise encoding, offering a potentially more efficient use of qubits and avoiding limitations associated with fixed-size images. The detailed comparison with existing methods is particularly helpful, and the authors provide strong experimental validation through simulations and results obtained on real quantum hardware. A key advantage of this method is its ability to handle images of arbitrary size and aspect ratio, alongside its minimal quantum resource requirements, making it potentially suitable for near-term quantum devices. While the authors demonstrate performance on standard test images, expanding the dataset to include more diverse image types would strengthen the results, and a more detailed analysis of performance under different noise conditions would also be beneficial.
Image Compression via Quantum Amplitude Embedding
Scientists have developed a new method for compressing color images using near-term quantum devices, focusing on efficient qubit usage and practical application. The approach divides an image into fixed-size blocks, termed “bixels”, and calculates the total intensity within each block to summarise local image information. A global histogram, representing the distribution of these block intensities, is then constructed, effectively condensing the image’s tonal distribution into a compact statistical form. This amplitude embedding is performed using the PennyLane software framework and executed on real IBM Quantum hardware, allowing researchers to test the method on currently available quantum systems.
The resulting quantum state is measured, enabling reconstruction of the histogram and subsequent approximate recovery of the original block intensities, ultimately facilitating full image reassembly. Crucially, the method maintains a constant qubit requirement dependent solely on the number of histogram bins, independent of the image’s resolution, offering a significant advantage over conventional pixel-level encodings. Researchers demonstrate high-quality image reconstructions using as few as 5 to 7 qubits, showcasing the method’s potential for resource-constrained quantum devices. By adjusting the number of histogram bins, users can precisely control the trade-off between image fidelity and the number of qubits required, optimising performance for specific applications. This innovative approach adapts classical histogram quantization techniques for quantum compatibility, leveraging the benefits of superposition and statistical encoding to achieve compact data representation and novel processing capabilities for high-dimensional images.
Few-Qubit Image Encoding via Amplitude Embedding
Scientists have developed a new method for compressing color images using a small number of qubits, demonstrating a significant advancement in quantum image processing. The team successfully encoded images by dividing them into fixed-size blocks, termed “bixels”, and calculating the total intensity within each block before constructing a global histogram. This histogram, representing the distribution of block intensities, is then encoded into the amplitudes of a quantum state using a technique called amplitude embedding. Experiments reveal that this approach maintains a constant qubit requirement, determined solely by the number of histogram bins, and is independent of the image’s resolution.
The method delivers high-quality image reconstructions using as few as 5 to 7 qubits, a substantial improvement over conventional pixel-level encodings. By adjusting the number of histogram bins, users can precisely control the trade-off between image fidelity and the resources required for compression. The team executed the amplitude embedding process and subsequent measurements on real IBM Quantum hardware, successfully reconstructing images from the encoded quantum state. Data confirms that this deterministic, no-training pipeline effectively balances fidelity and resource use, offering a practical solution for current noisy intermediate-scale quantum (NISQ) systems. The key innovation lies in the bixel-based encoding and histogram-driven compression, which reduces dimensionality and enables qubit usage independent of image size, paving the way for efficient quantum image processing on near-term quantum devices.
Histogram Quantum Image Compression Demonstrates Efficiency
This work introduces a new method for compressing color images using quantum systems, focusing on encoding the distribution of image intensities rather than individual pixel values. The approach divides images into blocks and creates a histogram of these block intensities, then encodes this histogram into a quantum state using a minimal number of qubits. Results demonstrate high-quality image reconstruction with remarkably few qubits, outperforming conventional quantum image encoding methods in terms of efficiency and suitability for current quantum hardware. The method achieves compression by representing the image with a number of qubits determined solely by the number of histogram bins, independent of image resolution.
Experiments on real quantum hardware confirm the viability of the circuit, achieving high peak signal-to-noise ratios (PSNR) and low mean squared errors (MSE) with only 5 to 7 qubits. This demonstrates a significant reduction in quantum resource requirements compared to other approaches. The authors acknowledge that further research could explore adaptive histogram binning to optimise encoding precision, investigate the method’s robustness under realistic noise conditions, and combine quantum embedding with classical decoding techniques for real-time reconstruction. Future work could also tailor the approach to specific image types to exploit inherent redundancies and improve performance.