We present Q-Seg, a novel unsupervised image segmentation method based on quantum
annealing, tailored for existing quantum hardware. We formulate the pixelwise segmentation …

Existing methods for learning 3D representations are deep neural networks trained and
tested on classical hardware. Quantum machine learning architectures, despite their …

Gate quantum computers generate significant interest due to their potential to solve certain
difficult problems such as prime factorization in polynomial time. Computer vision …

We present, QP-SBGD, a novel layer-wise stochastic optimiser tailored towards training
neural networks with binary weights, known as binary neural networks (BNNs), on quantum …

Regarded as a template-matching task for a long time, visual object tracking has witnessed
significant progress in space-wise exploration. However, since tracking is performed on …

As fifth-generation (5G) and next-generation communication systems advance and find
widespread application in critical infrastructures, the importance of vulnerability detection …

Classical max pooling plays a crucial role in reducing data dimensionality among various
well-known deep learning models, yet it often leads to the loss of vital information. We …

Quantum computing is expected to transform a range of computational tasks beyond the
reach of classical algorithms. In this work, we examine the application of variational quantum …

Field-Programmable Gate Arrays (FPGAs) have become indispensable components in
modern computing, providing adaptable and reconfigurable hardware platforms. FPGA …

Field-Programmable Gate Arrays (FPGAs) have asserted themselves as vital assets in
contemporary computing by offering adaptable, reconfigurable hardware platforms. FPGA …