Getting started

Use the following command to complete the installation of Qcover

pip install Qcover

or

git clone https://github.com/BAQIS-Quantum/Qcover
pip install -r requirements.yml
python setup.py install

More example codes and tutorials can be found in the tests folder here on GitHub.

Examples

1. Using algorithm core module to generate the ising random weighted graph and calculate it's Hamiltonian expectation

   from Qcover.core import Qcover
   from Qcover.backends import CircuitByQulacs
   from Qcover.optimizers import COBYLA
   
   node_num, edge_num = 6, 9
   p = 1
   nodes, edges = Qcover.generate_graph_data(node_num, edge_num)
   g = Qcover.generate_weighted_graph(nodes, edges)
   qulacs_bc = CircuitByQulacs()
   optc = COBYLA(options={'tol': 1e-3, 'disp': True})
   qc = Qcover(g, p=p, optimizer=optc, backend=qulacs_bc)
   res = qc.run()
   print("the result of problem is:\n", res)
   qc.backend.visualization()

2. Solving specific binary combinatorial optimization problems, Calculating the expectation value of the Hamiltonian of the circuit which corresponding to the problem. for example, if you want to using Qcover to solve a max-cut problem, just coding below:

   import numpy as np
   from Qcover.core import Qcover
   from Qcover.backends import CircuitByQiskit
   from Qcover.optimizers import COBYLA
   from Qcover.applications.max_cut import MaxCut
   node_num, degree = 6, 3
   p = 1
   mxt = MaxCut(node_num=node_num, node_degree=degree)
   ising_g = mxt.run()
   qiskit_bc = CircuitByQiskit(expectation_calc_method="statevector")
   optc = COBYLA(options={'tol': 1e-3, 'disp': True})
   qc = Qcover(ising_g, p=p, optimizer=optc, backend=qiskit_bc)
   res = qc.run()
   print("the result of problem is:\n", res)
   qc.backend.visualization()

3. If you want to customize the Ising weight graph model and calculate the ground state expectation with Qcover, you can use the following code

   import numpy as np
   import networkx as nx
   from Qcover.core import Qcover
   from Qcover.backends import CircuitByTensor
   from Qcover.optimizers import COBYLA
   
   ising_g = nx.Graph()
   nodes = [(0, 3), (1, 2), (2, 1), (3, 1)]
   edges = [(0, 1, 1), (0, 2, 1), (3, 1, 2), (2, 3, 3)]
   for nd in nodes:
      u, w = nd[0], nd[1]
      ising_g.add_node(int(u), weight=int(w))
   for ed in edges:
       u, v, w = ed[0], ed[1], ed[2]
   ising_g.add_edge(int(u), int(v), weight=int(w))
   
   p = 2
   optc = COBYLA(options={'tol': 1e-3, 'disp': True})
   ts_bc = CircuitByTensor()
   qc = Qcover(ising_g, p=p, optimizer=optc, backend=ts_bc)
   res = qc.run()
   print("the result of problem is:\n", res)
   qc.backend.visualization()

How to contribute

For information on how to contribute, please send an e-mail to members of developer of this project.

Please cite

When using Qcover for research projects, please cite

• Wei-Feng Zhuang, Ya-Nan Pu, Hong-Ze Xu, Xudan Chai, Yanwu Gu, Yunheng Ma, Shahid Qamar, Chen Qian, Peng Qian, Xiao Xiao, Meng-Jun Hu, and Done E. Liu, "Efficient Classical Computation of Quantum Mean Value for Shallow QAOA Circuits", arXiv:2112.11151 (2021).

Authors

The first release of Qcover was developed by the quantum operating system team in Beijing Academy of Quantum Information Sciences.

Qcover is constantly growing and many other people have already contributed to it in the meantime.

License

Qcover is released under the Apache 2 license.