Qaintum: A Julia-based Simulation Framework for Quantum Circuits

We introduce “Qaintum”, a Julia-based software framework for quantum circuit simulation and optimization that can also be integrated into classical machine learning toolboxes. The core concept of Qaintum is flexibility of design: enabling the user to work with different representations, such as circuits or tensor networks.

The core library supports state-vector and density matrix representations; algorithms can be implemented using standard or customized unitary operators. The toolbox also supports search-based circuit optimization and interfaces with QASM. A further optimization option is planned via conversion to and from the diagrammatic ZX calculus.

Qaintum provides gradient calculation by a backward pass through a quantum circuit and integrates with the Flux.jl machine learning library. This facilitates the running of variational algorithms, such as QAOA or VQE.

We plan to utilize the tensor network representations of quantum circuits, in particular, matrix product states and tree tensor networks for simulations which would be unfeasible when using state-vector approaches.