Quantum computation has the ability to revolutionize the treatment of patients. Quantum computing can help to detect diseases by identifying and forecasting malfunctions. But there's a threat associated here (i.e., healthcare data among the most popular cybercriminal targets, IoT devices notoriously lacking in effective safeguards, and quantum computers on the brink of an encryption/decryption breakthrough). Health agencies need a security prognosis and treatment plan as soon as possible. Healthcare companies recently worry more about the quantum security threats. The biggest threat of healthcare data breaches has come in the form of identity theft. There should be a strong mechanism to combat the security gaps in existing healthcare industry. If the healthcare data are available on the network, an attacker may try to modify, intercept, or even view this data stream. With the use of quantum security, the quantum state of these photons changes alert the security pros that someone is trying to breach the link.

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Content-based image retrieval system nowadays use color histogram as a common color descriptor. We consider color as one of the important features during image representation process. Different transformations such as changing scale of image, rotating an image, and translations of image to other forms does not make any alterations to the color content of image. If we need to focus on differentiation or similarity between two images we usually deal with various color features of image. To extract color features of image we consider on color space, color reduction, color feature extraction process. In image retrieval applications, user specifies desired image as query image and wants to search for the most similar image in database of his interest. Application then identifies similar relevant images from database based on different color features of database images and query image. To achieve this we compute color features of database images and those for query image. We use local color features of different regions and combine them to represent color histogram as a color feature. These color features are compared using Euclidean distance as a metric to define similarity between the query image and the database images. For calculations of local color histogram we divide image into different blocks of size 8 × 8 as fixed, so that for each block of image spatial color feature histogram of image is obtained. Our experimental work shows that local hybrid color histogram produced more accurate image retrieval results than global color moments color histogram.Keywords Color space dimension reduction Á Feature vector quantization Á Low level color feature histogram Á Global and local region color distribution Á Ring-shaped concentric histogram and cornered histogram

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