TensorLy-Quantum builds on top of TensorLy and provides the tool to simulate circuits at scale by efficiently using tensor methods. In Variational Quantum Eigensolver, we provide an example of traditional VQE for quantum Ising model Hamiltonian via PyTorch Autograd supported TT-tensors with TensorLy-Quantum.
TensorLy-Quantum provides the tools to use quantum circuit simulation to solve hard problems with tensor-based simulation of variational quantum algorithms. For instance, TensorLy-Quantum users can both find the ground states of quantum Ising models and construct quantum algorithms for NP-hard classical problems, such as MaxCut. In addition to flexibile circuit ansatze and operator (Hamiltonian) building functions, we also provide the framework for novel quantum algorithms, such as Multi-Basis Encoding.
Users can easily build quantum Hamiltonians of interest and solve them using the flexible circuit ansatze of TensorLy-Quantum. In Variational Quantum Eigensolver, we provide an example how to execute such an algorithm.
To be able to scale to larger number of qubits, we developed a new technique called Multi-Basis Encoding  (MBE). In Multi-Basis Encoding, we provide an example of the MBE quantum optimization algorithm for MaxCut via PyTorch Autograd supported TT-tensors with TensorLy-Quantum.
We provide all the tools to solve MaxCut problems at scale,