qsp 0.0.3

implementation of different methods to prepare quantum states on quantum computer

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 and some benchmarks

Basic Example

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')

prep.sequential_unitary_circuit(num_seq_layers=4)

References

1. Encoding of matrix product states into quantum circuits of one-and two-qubit gates,
   Shi-Ju Ran, Phys. Rev. A 101, 032310 (2020)

2. Variational power of quantum circuit tensor networks,
   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,
   Mohsin Iqbal, David Munoz Ramo and Henrik Dreyer, arXiv preprint arXiv:2209.07106 (2022)

4. Efficient adiabatic preparation of tensor network states,
   Zhi-Yuan Wei, Daniel Malz and Ignacio J. Cirac, Phys. Rev. Research 5, L022037 (2023)