For simulation, it is useful to have
Gate objects that enact noisy quantum evolution.
Cirq supports modeling noise via operator sum representations of noise (these evolutions are also known as quantum operations, quantum dynamical maps, or superoperators).
This formalism models evolution of the density matrix via:
Where Ak are Kraus operators. These operators are not necessarily unitary and must satisfy the trace-preserving property:
As a noisy channel, Kraus operators are not unique. For more details of these operators, see John Preskill’s notes.
Gate can represent an operator sum representation by supporting the
channel protocol. Alternatively, for channels that represent probabilistic mixtures of unitaries, one can implement the
cirq.channel and def channel¶
To represent an operator sum evolution, a
Gate should implement the
SupportsChannel protocol. To do this, the
Gate should implement the
_channel_(self) -> Sequence[np.ndarray]: method.
This method should return the sequence of
numpy matrices corresponding to the Kraus operators. The basis in which this matrix is expressed is always implicit with respect to the object being called.
For example, in
GateOperations, these matrices must be ordered with respect to the list of qubits that the channel is applied to. The qubit-to-amplitude order mapping matches the ordering of
numpy.kron(A, B), where
A is a qubit earlier in the list than the qubit
If one has defined
_channel_, then that
Gate and any
GateOperation that uses that gate can be used as an argument to
cirq.channel will return this sequence of matrices.
Besides objects that support
cirq.channel will also fall back to other objects that can be interpreted as channels. For example, if a channel is a probabilistic mixture of unitary gates (see below), then
cirq.channel will fall back to seeing if the object supports
_mixture_ is not supported, then
cirq.channel checks to see if
_unitary_ is supported.
In addition to supporting
_channel_, objects that are channels should also implement
_has_channel_(self) -> bool to return
True. This method is used to determine whether an object has a
_channel_ or not without having to do the potentially expensive creation of the matrices for the channel.
cirq.mixture and def mixture¶
Some channels can be interpreted as probabilistically selecting between different unitary evolutions:
In this case, it is possible to perform Monte Carlo simulations of these gates using a wave function based simulator (and not a density matrix based simulator).
Instead of implementing the
SupportsChannel protocol, one should implement the
SupportsMixture protocol. To do this, one should implement the
_mixture_(self) -> Sequence[Tuple[float, np.ndarray]] protocol. This returns a sequence of tuples.
The first element of each tuple is the probability of the unitary, and the second element is the unitary. Like the
_channel_ method described above, the basis for these matrices is implicit with respect to the object being called. One should also make
True to indicate to callers that the object supports the mixture protocol.
If one wants to get the mixture channel directly, one can call
Cirq supports many commonly used quantum channels out of the box, see
AsymmetricDepolarizingChannel, DepolarizingChannel, BitFlipChannel, and PhaseFlipChannel¶
The asymmetric depolarizing channel represents probabilistically selecting one of three Pauli gates to apply or doing nothing to the state. This is implemented via a
_mixture_ method so that a Monte Carlo simulation with a wave function simulator can be used.
This channel implements the evolution:
Here px is the probability that the X Pauli gate is applied and no other gate is applied, and similarly for py and pz.
A particular case of the asymmetric depolarizing channel is the case when each of the different Paulis occur with the same probability. This is encapsulated in the
DepolarizingChannel gate, which takes a probability
p such that each Pauli gate occurs with probability
To construct channels, useful helpers are provided
Another common case is when only a Pauli X (bit flip) can occur, or when only a Pauli Y (phase flip) can occur. These correspond to
PhaseFlipChannel with helpers
GeneralizedAmplitudeDampingChannel and AmplitudeDampingChannel¶
The generalized amplitude damping channel models the effect of energy dissipation to a surrounding environment as well as dephasing that does not exchange energy. The amplitude damping channel only models dissipation of energy to a surrounding environment.
Cirq has implementations of both of these channels. The generalized amplitude damping channel corresponds to:
Where γ is the probability of the interaction being dissipative, and
p is the probability that the qubit and environment exchange energy. The amplitude damping channel corresponds to
Cirq provides the helpers
cirq.amplitude_damp to construct these noisy gates.