By Gopal Ramesh Dahale Barren plateaus make it exceedingly difficult to train the quantum circuits we use in quantum neural networks (QNNs). In this blog post, we take a look at how layerwise learning for QNN training, as introduced in [1], can help. Layerwise learning for QNN training involves the…

Working with large-scale quantum systems presents unique challenges, especially when aiming to utilize a significant number of qubits. The physical constraints of devices, such as lengthy pathways and potential error introduction, demand careful consideration. Lab 5 of the IBM Spring Challenge was to work with 127 qubits on the ibm_sherbrooke…

In the world of quantum computing, where immense computational power and information processing capabilities may await, tensor networks have emerged as another powerful tool for representing and manipulating quantum states. But before we delve into the quantum realm, let’s first explore the motivation behind classical tensor networks. In this blog…

These will make you stand out of the crowd — This article is inspired by the course Deep Learning with PyTorch: Zero to GANs by Jovian.ai. Enrol now to start learning a practical and coding-focused introduction to deep learning using the PyTorch framework. In this article, we will look at five Pytorch tensor functions torch.apply_(callable) torch.apply_() applies the function callable to…