Quantum Green TEA solves the static and time-dependent Schrödinger equation and Lindblad equation, e.g., one can simulate ground states, finite temperature states, and time evolutions.

Quantum Matcha TEA is a quantum computer emulator powered by matrix product states. Its interface is entirely in Python, so the user does not need to care about the backends. You can use it to simulate quantum circuits defined with the Qiskit API or directly from its own API. However, it is possible to simulate qudits systems too: through the Fortran backend, you can emulate bosonic systems defined via strawberry fields. The backends for the simulation are tunable between CPUs and GPUs as well as between Python and FORTRAN and are scalable up to MPI towards running on HPC clusters.

Having access to the final MPS state, you can perform any measurement accessible on a quantum computer, such as projective measurements, local observables, and hamiltonians decomposed in Pauli matrices. But you have access to much more: for example the entanglement entropy between different subsystems, and optimized methods to sample the final state.

Quantum Chai TEA contains the machine learning applications using tensor networks.

Quantum Red TEA contains the tensor libraries on which Quantum green TEA, Quantum matcha TEA, and Quantum chai TEA rely on for their tensor operations. Here, we provide the interfaces to BLAS/LAPACK and CUDA for the higher-level applications.

Auxiliary libraries, Python-FORTRAN interfaces, and Python solutions for common tensor network geometries are combined in this part of the Quantum TEA library.

Quantum Matcha TEA is a quantum computer emulator powered by matrix product states. Its interface is entirely in Python, so the user does not need to care about the backends. You can use it to simulate quantum circuits defined with the Qiskit API or directly from its own API. However, it is possible to simulate qudits systems too: through the Fortran backend, you can emulate bosonic systems defined via strawberry fields. The backends for the simulation are tunable between CPUs and GPUs as well as between Python and FORTRAN and are scalable up to MPI towards running on HPC clusters.

Having access to the final MPS state, you can perform any measurement accessible on a quantum computer, such as projective measurements, local observables, and hamiltonians decomposed in Pauli matrices. But you have access to much more: for example the entanglement entropy between different subsystems, and optimized methods to sample the final state.

Quantum Chai TEA contains the machine learning applications using tensor networks.

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