Tensor Chain Contraction Demo: REINFORCE and Brute Force

We have developed a training demo that utilizes REINFORCE and a brute force baseline to find the best contraction order for a tensor chain. We welcome any suggestions or feedback on this demo and environment!

# Update Jan 10, 2023, Extend the environment design from tensor train to tensor networks.
@spicywei Wei, @Yonv1943 Jiahao, and Shixun extend the formulation of the tensor train environment to the tensor network.
[classical_simulation_01102023.pptx](https://github.com/AI4Finance-Foundation/RLSolver/files/10388754/classical_simulation_01102023.pptx)

# Update Jan 09, 2023

- #7 Thanks to Wei @spicywei and Shixun @shixun404 developed a tensor train demo that achieves optimal for the tensor train with 4 tensors.

# Update Jan 06, 2023
- [REINFORCE single file demo](https://github.com/AI4Finance-Foundation/RLSolver/blob/cbbc2376f77e26e6803b384dacec0e9a53fb04b7/rlsolver/rlsolver_quantum_circuits/demo_tensor_chain.py)
- [Brute force baseline](https://github.com/AI4Finance-Foundation/RLSolver/blob/cbbc2376f77e26e6803b384dacec0e9a53fb04b7/rlsolver/rlsolver_quantum_circuits/baseline_brute_force.py)
- Environment design draft: [01032023_classical_simulation.pptx](https://github.com/AI4Finance-Foundation/RLSolver/files/10369796/01032023_classical_simulation.pptx)