India, which is known as the “diabetic capital,” is seeing an increase in cases of diabetic retinopathy (DR), which is expected to double over the next two to three decades. This disease, which results from diabetes, can cause blindness, and drastically shorten a person’s life expectancy. The main characteristic of DR is micro-vascular retinal alterations. To tackle this disease, early diagnosis by vision-based screening and classification algorithms is essential, but it requires significant computational resources. One promising approach to this problem is quantum computing. There are theoretical benefits and viability to combining quantum computing with traditional image categorization techniques. However, obtaining high accuracy has proven to be a challenge for the picture categorization methods now in use. Our work presents a quantum-based deep convolutional neural network as a solution to this. For the categorization of DR, our hybrid model combines quantum-assisted deep learning with convolutional neural networks (CNNs). The quantum component processes complex and high-dimensional quantum data obtained from these scans, providing deeper insights into the molecular and cellular alterations associated with the disease, while the CNN evaluates retinal images, extracting relevant aspects. An extensive investigation of diabetic retinopathy is made possible by this integration. The model helps with early diagnosis by properly predicting the severity of DR. We provide encouraging findings that demonstrate this hybrid model’s potential to improve the precision and effectiveness of DR diagnosis. Our research intends to address the pressing healthcare issue of diabetic retinopathy diagnosis by utilizing state-of-the-art CNN technology and quantum-assisted deep learning, providing substantial advantages to patients and physicians alike.