mindquantum.core.gates.GroupedPauliChannel
- class mindquantum.core.gates.GroupedPauliChannel(probs: npt.NDArray[np.float64], **kwargs)[source]
A group of pauli channels.
This quantum channel is equivalent to a list of pauli channels, but will evaluate much faster than apply them one by one. For detail of pauli channel, please refers to
PauliChannel.\[\epsilon(\rho) = \otimes_i \epsilon_\text{pauli}^i(\rho)\]- Parameters
probs (numpy.ndarray) – The error probabilities of pauli channel. It has dimension of (n, 3), where the first dimension n is the qubit number or the number of pauli channels represented by this quantum channel . The second dimension 3 represents the probability of applying \(X\), \(Y\) or \(Z\) gate.
Examples
>>> import numpy as np >>> from mindquantum.core.gates import GroupedPauliChannel >>> from mindquantum.core.circuit import Circuit >>> from mindquantum.simulator import Simulator >>> probs = np.array([[1.0, 0.0, 0.0], [0.0, 0.3, 0.0]]) >>> circ = Circuit([GroupedPauliChannel(probs).on([0, 1])]).measure_all() >>> circ ╭ ╔═══Grouped Pauli Channel ╮ ┍━━━━━━┑ q0: ──┤─╢ PC(px=1, py=0, pz=0) ╟──────├─┤ M q0 ├─── │ ╚══════════════════════╝ │ ┕━━━━━━┙ │ ╔═════════════════════════╗ │ ┍━━━━━━┑ q1: ──┤─╢ PC(px=0, py=3/10, pz=0) ╟───├─┤ M q1 ├─── ╰ ╚═════════════════════════╝ ╯ ┕━━━━━━┙ >>> Simulator('mqvector', circ.n_qubits).sampling(circ, shots=1000, seed=42) shots: 1000 Keys: q1 q0│0.00 0.177 0.355 0.532 0.71 0.887 ───────────┼───────────┴───────────┴───────────┴───────────┴───────────┴ 01│▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ │ 11│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ │ {'01': 710, '11': 290}