# Quantum State Preparation
This repository provides an implementation of various methods for preparing tensor network states (specifically, 1D tensor network states) on a quantum computer.
To use the package, you first need to specify a list of NumPy arrays that represent the MPS. You can then
call different routines in the package to prepare the state.
## Installation
```
pip install qsp
```
One can also install the development version directly as
```
pip install git+https://github.com/mohsin-0/qsp.git@main
```
## Tutorial
[Usage tutorial](https://github.com/mohsin-0/qsp/blob/main/examples/state_prep_examples.ipynb) and some [benchmarks](https://github.com/mohsin-0/qsp/blob/main/examples/benchmarks.ipynb)
## Basic Example
```python
from qsp.tsp import MPSPreparation
import numpy as np
bond_dim, phys_dim = 4, 2
L=10
tensor_array = [np.random.rand(bond_dim,bond_dim,phys_dim) for _ in range(L)]
tensor_array[ 0] = np.random.rand(bond_dim,phys_dim) # end points of mps
tensor_array[-1] = np.random.rand(bond_dim,phys_dim)
prep = MPSPreparation(tensor_array, shape='lrp')
overlap, circ = prep.sequential_unitary_circuit(num_seq_layers=4)
```
## References
1. [Encoding of matrix product states into quantum circuits of one-and two-qubit gates](https://arxiv.org/abs/1908.07958),\
Shi-Ju Ran, Phys. Rev. A 101, 032310 (2020)
2. [Variational power of quantum circuit tensor networks](https://arxiv.org/abs/2107.01307),\
Reza Haghshenas, Johnnie Gray, Andrew C Potter, and Garnet Kin-Lic Chan, Phys. Rev. X 12, 011047 (2022)
3. [Preentangling Quantum Algorithms--the Density Matrix Renormalization Group-assisted Quantum Canonical Transformation](https://arxiv.org/abs/2209.07106),\
Mohsin Iqbal, David Munoz Ramo and Henrik Dreyer, arXiv preprint arXiv:2209.07106 (2022)
4. [Efficient adiabatic preparation of tensor network states](https://arxiv.org/abs/2209.01230),\
Zhi-Yuan Wei, Daniel Malz and Ignacio J. Cirac, Phys. Rev. Research 5, L022037 (2023)
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"description": "# Quantum State Preparation\n\nThis repository provides an implementation of various methods for preparing tensor network states (specifically, 1D tensor network states) on a quantum computer. \n\nTo use the package, you first need to specify a list of NumPy arrays that represent the MPS. You can then \ncall different routines in the package to prepare the state.\n\n## Installation\n\n```\npip install qsp\n```\n\nOne can also install the development version directly as \n```\npip install git+https://github.com/mohsin-0/qsp.git@main\n```\n\n## Tutorial\n[Usage tutorial](https://github.com/mohsin-0/qsp/blob/main/examples/state_prep_examples.ipynb) and some [benchmarks](https://github.com/mohsin-0/qsp/blob/main/examples/benchmarks.ipynb)\n\n\n## Basic Example\n\n```python\nfrom qsp.tsp import MPSPreparation\nimport numpy as np\nbond_dim, phys_dim = 4, 2\n\nL=10\ntensor_array = [np.random.rand(bond_dim,bond_dim,phys_dim) for _ in range(L)]\ntensor_array[ 0] = np.random.rand(bond_dim,phys_dim) # end points of mps\ntensor_array[-1] = np.random.rand(bond_dim,phys_dim)\nprep = MPSPreparation(tensor_array, shape='lrp')\n\noverlap, circ = prep.sequential_unitary_circuit(num_seq_layers=4)\n```\n\n## References\n1. [Encoding of matrix product states into quantum circuits of one-and two-qubit gates](https://arxiv.org/abs/1908.07958),\\\n Shi-Ju Ran, Phys. Rev. A 101, 032310 (2020)\n \n2. [Variational power of quantum circuit tensor networks](https://arxiv.org/abs/2107.01307),\\\n Reza Haghshenas, Johnnie Gray, Andrew C Potter, and Garnet Kin-Lic Chan, Phys. Rev. X 12, 011047 (2022)\n \n3. [Preentangling Quantum Algorithms--the Density Matrix Renormalization Group-assisted Quantum Canonical Transformation](https://arxiv.org/abs/2209.07106),\\\n Mohsin Iqbal, David Munoz Ramo and Henrik Dreyer, arXiv preprint arXiv:2209.07106 (2022)\n \n4. [Efficient adiabatic preparation of tensor network states](https://arxiv.org/abs/2209.01230),\\\n Zhi-Yuan Wei, Daniel Malz and Ignacio J. Cirac, Phys. Rev. Research 5, L022037 (2023)\n \n\n",
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