Preparations#
- class Vacuum#
Prepare the system in a vacuum state.
Example usage:
with pq.Program() as program: pq.Q() | pq.Vacuum() ...
Note
This operation can only be used for all modes and is only practical to be used at the beginning of a program declaration.
- class Mean(mean: numpy.ndarray)#
Set the first canonical moment of the state.
Example usage:
with pq.Program() as program: pq.Q() | pq.Mean( mean=np.array(...) ) ...
Can only be applied to the following states:
GaussianState
.Note
The mean vector is dependent on \(\hbar\), but the value of \(\hbar\) is specified later when executed by a simulator. The parameter mean should be specified keeping in mind that it will automatically be scaled with \(\hbar\) during execution.
- Parameters
mean (numpy.ndarray) – The vector of the first canonical moments in xpxp-ordering.
- class Covariance(cov: numpy.ndarray)#
Sets the covariance matrix of the state.
Example usage:
with pq.Program() as program: pq.Q() | pq.Covariance( cov=np.array(...) ) ...
Can only be applied to the following states:
GaussianState
.Note
The covariance matrix is dependent on \(\hbar\), but the value of \(\hbar\) is specified later when executed by a simulator. The parameter cov should be specified keeping in mind that it will automatically be scaled with \(\hbar\) during execution.
- Parameters
cov (numpy.ndarray) – The covariance matrix in xpxp-ordering.
- class Thermal(mean_photon_numbers: Iterable[float])#
Prepares a thermal state.
Example usage:
with pq.Program() as program: pq.Q(0, 1) | pq.Thermal([0.5, 1.5]) ...
The thermal state is defined by
\[\rho = \sum_{\vec{n} = 0}^\infty \frac{\overline{n}^{\vec{n}}}{(1 + \overline{n})^{\vec{n} + 1}} | \vec{n} \rangle \langle \vec{n} |,\]where \(\overline{n} \in \mathbb{R}^d\) is the vector of mean photon numbers. The power on vectors is defined as
\[\vec{a}^{\vec{b}} = \prod_{i=1}^d a_i^{b_i}\]In terms of Gaussian states, the mean vector and covariance matrix is
\[\begin{split}\mu &= 0_{2d} \\ \sigma &= \hbar ( 2 \operatorname{\textit{diag}}(\operatorname{\textit{repeat}} (\overline{n}, 2)) + I_{2d \times 2d} )\end{split}\]Can only be applied to the following states:
GaussianState
.- Parameters
mean_photon_numbers (Iterable[float]) – The sequence of mean photon numbers.
- Raises
InvalidParameter – If the mean photon numbers are not positive real numbers.
- class StateVector(occupation_numbers: Iterable[int], coefficient: complex = 1.0)#
State preparation with Fock basis vectors.
Example usage:
with pq.Program() as program: pq.Q() | ( 0.3 * pq.StateVector([2, 1, 0, 3]) + 0.2 * pq.StateVector([1, 1, 2, 3]) ... ) ...
Can only be applied to the following states:
PureFockState
.- Parameters
- Raises
InvalidState – If the specified occupation numbers are not all natural numbers.
- class DensityMatrix(ket: Iterable[int], bra: Iterable[int], coefficient: complex = 1.0)#
State preparation with density matrix elements.
Example usage:
with pq.Program() as program: pq.Q() | ( 0.2 * pq.DensityMatrix(ket=(1, 0, 1), bra=(2, 1, 0)) + 0.3 * pq.DensityMatrix(ket=(2, 0, 1), bra=(0, 1, 1)) ... ) ...
Note
This only creates one matrix element.
Can only be applied to the following states:
FockState
.- Parameters
- Raises
InvalidState – If the specified “bra” or “ket” vectors are not all natural numbers.
- class Create#
Create a particle on a mode.
This instruction essentially applies a creation operator on the specified mode, then normalizes the state.
Example usage:
with pq.Program() as program: pq.Q(1) | pq.Create() pq.Q(2) | pq.Create() ...
Can only be applied to the following states:
FockState
,PureFockState
.
- class Annihilate#
Annihilate a particle on a mode.
This instruction essentially applies an annihilation operator on the specified mode, then normalizes the state.
Example usage:
with pq.Program() as program: ... pq.Q(0) | pq.Annihilate() ...
Can only be applied to the following states:
FockState
,PureFockState
.