  To address the computational complexity associated with state-vector
simulation for quantum circuits, we propose a combination of advanced
techniques to accelerate circuit execution. Quantum gate matrix caching reduces
the overhead of repeated applications of the Kronecker product when applying a
gate matrix to the state vector by storing decomposed partial matrices for each
gate. Circuit splitting divides the circuit into sub-circuits with fewer gates
by constructing a dependency graph, enabling parallel or sequential execution
on disjoint subsets of the state vector. These techniques are implemented using
the PyTorch machine learning framework. We demonstrate the performance of our
approach by comparing it to other PyTorch-compatible quantum state-vector
simulators. Our implementation, named Qandle, is designed to seamlessly
integrate with existing machine learning workflows, providing a user-friendly
API and compatibility with the OpenQASM format. Qandle is an open-source
project hosted on GitHub https://github.com/gstenzel/qandle and PyPI
https://pypi.org/project/qandle/ .
