Abstract

Quantum machine learning (QML) is progressing toward larger and more complex models, characterized by increasing qubit counts, deeper circuits, and tighter integration with classical machine learning workflows. In this context, efficient quantum circuit simulation and rigorous validation are becoming central to the development and assessment of large-scale QML models — the bottleneck is rarely the algorithm itself, but whether a model can be simulated fast enough to train on and validated rigorously enough to trust. This tutorial examines how GPU-optimized simulation enables scalable QML workflows, covering hybrid model architectures, quantum-inspired methods, and the layered GPU software stack behind them: CUDA-Q for expressing quantum kernels, PyTorch-compatible integration, cuQuantum/cuTensorNet for GPU-accelerated tensor-network execution, and cuTensor for advanced contraction optimization.

The material builds a single continuous workflow across five notebooks. It begins with the programming model and GPU environment, then works through four families of QML model, each of which stresses the simulation stack in a different way:

• Variational models and learned optimization: A Transformer meta-optimizer that predicts QAOA parameter updates and generalizes across unseen problem instances, trained end-to-end with CUDA-Q/Pytorch and validated by Qamomile — the case where a circuit is evaluated many thousands of times inside a classical training loop.

• Quantum sequence models: Quantum Fast Weight Programmers, where a compact parameterized circuit reprograms the weights of a classical network, giving temporal modeling without an explicit recurrent hidden state.

• Quantum-inspired architectures: Kolmogorov-Arnold Network blocks whose activations are evaluated as tensor networks, cutting parameter counts in LLM feed-forward layers from function fitting up to GPT scale.

• Quantum kernel methods: A support vector machine whose kernel matrix is produced by a quantum feature map, captured once as a tensor network and then contracted in batch across every pair of training points.

The through-line is the same discipline in each case: express the model, choose the simulation backend that matches its structure, measure honestly, and scale — so that large-scale quantum algorithms can be validated, developed, and optimized prior to deployment on QPUs. All materials are provided as reusable notebook examples, so participants leave with practical tools and workflows they can extend in their own QML research and development.

Target Audience

This tutorial is intended for practitioners and researchers interested in quantum machine learning, efficient quantum circuit simulation, and scalable model design. Prior familiarity with PyTorch and quantum machine learning is helpful, but not required. It is especially well suited for those seeking a practical introduction to QML validation and to scaling quantum-inspired models on modern classical accelerator platforms.

Prerequisite:

• Basic understanding of quantum computing

• Some familiarity with Python and Jupyter notebooks

• Familiarity with machine learning concepts is helpful but not required