在线时间:8:00-16:00
迪恩网络APP
随时随地掌握行业动态
扫描二维码
关注迪恩网络微信公众号
开源软件名称:cyclops-community/koala开源软件地址:https://github.com/cyclops-community/koala开源编程语言:Python 100.0%开源软件介绍:KoalaKoala is a quantum circuit/state simulator based on projected entangled–pair states (PEPS) tensor networks. InstallationNumPy and TensorBackends are required.
It is recommended to install Koala and TensorBackends in the editable ( git clone https://github.com/cyclops-community/tensorbackends.git
pip install -e ./tensorbackends
git clone https://github.com/YiqingZhouKelly/koala.git
pip install -e ./koala Parallelization is provided by Cyclops Tensor Framework, and is optional. Testingpython -m unittest Get Startedfrom koala import peps, Observable, Gate
from tensorbackends.interface import ImplicitRandomizedSVD
# initialize a 2 by 3 state with peps approach and numpy backend
qstate = peps.computational_zeros(2, 3, backend='numpy')
# we also provide the state vector approach and a parallel backend
# statevector.computational_zeros(2, 3, backend='ctf')
# apply one gate or a list of gates
qstate.apply_gate(Gate('X', [], [0])) # (name, parameters, qubits)
qstate.apply_circuit([
Gate('R', [0.42], [2]),
Gate('CX', [], [1,4])
], update_option=peps.LocalGramQRUpdate(rank=4))
# choose from a list of update algorithms and optionally specify the cap bond dimension for approximate state evolution
# or apply arbitrary single-site or two-site operators
# qstate.apply_operator(np.array(...), [0])
# compute the amplitude, probability, and expectation value
qstate.amplitude([1,0,0,1,0,0])
qstate.probability([1,0,0,1,0,0])
observable = 1.5 * Observable.sum([
Observable.Z(0),
Observable.XY(3, 4) * 2
])
qstate.expectation(observable,
contract_option=peps.BMPS(ImplicitRandomizedSVD(rank=8)),
use_cache=True
)
# choose from a list of contraction algorithms and optionally specify the cap bond dimension for approximate contraction
# use built-in caching option to trade memory for time in expectation value calculations |
2023-10-27
2022-08-15
2022-08-17
2022-09-23
2022-08-13
请发表评论