Welcome to SciBits.blog

Here you will find hands-on tutorials on cutting-edge topics in scientific computing and machine and deep learning, from both a classical and a quantum point of view. Whether you’re a researcher, student, or enthusiast, this site is designed to help you explore and understand advanced methods in modern computer science. Use the sidebar to filter posts by category, or browse the latest articles below. Happy learning!

Neural Quantum States

Physics Informed ML
JAX
Flax

The idea of neural quantum states is to use neural networks as a way to parameterize wave functions for quantum many-body systems. It is a powerful approach that sidesteps the exponential growth of the Hilbert space and leverages advances in modern deep learning to one of the hardest problems in physics.

Mar 4, 2026

Introduction to Graph Neural Networks

Geometric DL
PyTorch

Graph Neural Networks (GNNs) extend deep learning to graph-structured data by respecting permutation symmetries. This post explores the mathematical foundations, different GNN flavors (convolutional, attentional, and message-passing), and provides hands-on PyTorch Geometric implementations.

Sep 8, 2025

Grover’s Algorithm

Quantum Computing
Qiskit

Grover’s algorithm is a foundational quantum computing technique that achieves a quadratic speedup for unstructured search problems. This post explores the algorithm’s geometric intuition, circuit implementation, and practical considerations, including a step-by-step Qiskit example.

Jun 22, 2025

Deep Sets

Geometric DL
PyTorch

Dive into Deep Sets, a foundational architecture for machine learning on unordered sets. This post explores the principle of permutation invariance, unveils the universal approximation capabilities of the Deep Sets model, and situates it within the broader Geometric Deep Learning blueprint. The post also includes a hands-on code example using PyTorch.

Jun 14, 2025

Hamiltonian Neural Networks

Physics Informed ML
JAX
Flax

Hamiltonian Neural Networks (HNNs) are a powerful approach to learning physics-preserving dynamics by modeling the Hamiltonian function \(H(q,p)\) of a system, rather than its vector field directly. This tutorial will guide you through the theory and implementation of HNNs using JAX and Flax.

Jun 9, 2025
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