Qcover is an open source effort to help exploring combinatorial optimization problems in Noisy Intermediate-scale Quantum(NISQ) processor. It is developed by the quantum operating system team in Beijing Academy of Quantum Information Sciences (BAQIS). Qcover supports fast output of optimal parameters in shallow QAOA circuits. It can be used as a powerful tool to assist NISQ processor to demonstrate application-level quantum advantages.

Using the following commands to build the executable environment of Qcover

conda install --yes --file requirements.txt

To start using Qcover, simply run

pip install Qcover

or

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

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

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

   from core import Qcover
   from backends import CircuitByQulacs
   from 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(maxiter=30, tol=1e-6, 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 core import Qcover
   from backends import CircuitByQiskit
   from optimizers import COBYLA
   from 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(maxiter=30, tol=1e-6, disp=True, initial_point=np.asarray([0.5, 0.5]))
   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()

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

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).

The first release of Qcover (v1.0.0) 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.

Qcover is released under the Apache 2 license.