Simulator: State Preparation and Measurement Noise

import trueq as tq
import matplotlib.pyplot as plt
import numpy as np

Adding Measurement Bitflip Noise

The most convenient way to add measurement noise is through the add_readout_error() noise source. There are several ways to specify readout error with this method.

The most basic method is to provide a single number. In the following example, each qubit will get a (symmetric) 1% bitflip error.

sim = tq.Simulator().add_readout_error(0.01)
circuit = tq.Circuit({range(5): tq.Meas()}), n_shots=1000)


Results({'00000': 937, '00001': 14, '00010': 11, '00100': 14, '00110': 1, '01000': 4, '01001': 1, '10000': 16, '10001': 1, '11000': 1})

We can use a dictionary to specify the readout error of particular qubits. Qubits that are not explicity assigned a readout error get the value attached to None which is equal to 0 by default. In this example, all qubits get 1% readout error, except qubit 1 gets a 50% readout error.

sim = tq.Simulator().add_readout_error(0.01, {1: 0.5})
circuit = tq.Circuit({range(5): tq.Meas()}), n_shots=1000)


Results({'00000': 456, '00001': 5, '00010': 6, '00100': 2, '01000': 505, '01001': 7, '01010': 6, '01100': 6, '10000': 2, '11000': 5})

In both of the above examples, any probability can be replaced with a pair of probabilities [p10, p01], where p10 is the probability of flipping a ‘0’ to a ‘1’ right before measurement, and p01 is the probability of flipping a ‘1’ to a ‘0’ right before measurement. In the following example, qubits get 1% readout error, except qubit 1 gets 5% readout error, and qubit 3 get asymmetric readout error of 1% on \(|0\rangle\) and 7% on \(|1\rangle\).

sim = tq.Simulator().add_readout_error(0.01, {1: 0.05, 3: [0.01, 0.07]})
circuit = tq.Circuit({range(5): tq.Meas()}), n_shots=1000)


Results({'00000': 909, '00001': 12, '00010': 5, '00100': 14, '01000': 47, '01001': 1, '10000': 9, '10001': 1, '11000': 2})

Adding Measurement POVM Noise

Measurement noise is applied only when a simulator’s run() method is called. Behind the scenes, measurement noise is impelemented by applying a positive-operator valued measurement (POVM) channel to the quantum state directly before measurement, thereby modifying the probability distribution from which ditstrings are sampled.

As a quick reminder (look elsewhere for a full description, e.g. Wikipedia or the lecture notes of John Watrous), a POVM is a set of positive-semi-definite matrices \(\{P_i\}_{i=1}^N\) that sum to the identity matrix, \(\sum_{i=1}^N P_i = \mathbb{I}\). The probability of observing the outcome \(i\) when measuring a state \(\rho\) is equal to \(p_i:=\operatorname{Tr}(\rho P_i)\). One can also take a POVM defined on a single qudit, and tensor it together to get a POVM on a collection of qudits. For example, if the POVM above describes measurement on a single qudit, the probability of measuring the outcomes \((i_0, i_1, i_2)\) on three qubits is simply \(p_{i_0}p_{i_1}p_{i_2}\). This is why we use the Tensor object as a primitive for specifying POVMs; its purpose is to store tensor project structures sparsely without actually taking kronecker products between subsystems unless necessary.

The add_povm() method can be used to directly add a POVM measurement channel. A POVM is specified as a Tensor object, where the input dimension is the number of POVM elements (per subsystem) and the output dimension pair is the dimension of the system.

In this example, we construct a coherent measurement error on qubit 3.

# Define a set of ideal POVM operators for each subsystem
proj0 = np.array([[1, 0], [0, 0]])  # project onto this one to get a "0"
proj1 = np.array([[0, 0], [0, 1]])  # project onto this one to get a "1"
ideal = np.array([proj0, proj1])

# Define a unitary rotation about X by 20 degrees
u = tq.Gate.from_generators("X", 20).mat

# Define noisy POVM operators by rotating the ideal POVM operators by U, which
# coherently changes the basis of the measurement
twisted = [u @ x @ u.conj().T for x in ideal]

# The spawn is the default POVM, and value customizes the POVM of specific qubit labels
povm = tq.math.Tensor(
    2,  # number of POVMs per subsystem
    (2, 2),  # number of POVMs, (dimensions of qubit subsystems)
    spawn=ideal,  # default measurement operation
    value={(3,): twisted},  # measurement operation on qubit 3
    dtype=np.complex128,  # tensor is real by default

# initialize a simulator with the above POVM specifications and test it
sim = tq.Simulator().add_povm(povm)
circuit = tq.Circuit({range(5): tq.Meas()}), n_shots=1000)


Results({'00000': 961, '00010': 39})

In this example, we add a correlated readout error on qubits (0,1).

# Define a confusion matrix for a 2-qubit system where the readout of 00, 01, 10 is
# ideal, but 11 is flipped to 01 5% of the time
confusion = [[1, 0, 0, 0], [0, 1, 0, 0.05], [0, 0, 1, 0], [0, 0, 0, 0.95]]

# Add the readout error and test the simulator
sim = tq.Simulator().add_readout_error(errors={(0, 1): confusion})
circuit = tq.Circuit([{(0, 1): tq.Gate.x}, {(0, 1): tq.Meas()}]), n_shots=10000)


Results({'01': 498, '11': 9502})

In this example, we add a third measurement label even though the simulation is on qubits, so that the outcomes ‘0’, ‘1’, and ‘2’ are all possible.

# Here we specify that the state |0> results in the outcome '0' 99% of the time, but
# also results in the outcome '2' 1% of the time. Similarly, the state |1> results in
# the outcome '1' 70% of the time, but also results in '2' 25% of the time and '0' 5%
# of the time.
povm = tq.math.Tensor(
    (2, 2),
    spawn=[np.diag([0.99, 0.05]), np.diag([0, 0.7]), np.diag([0.01, 0.25])],
sim = tq.Simulator().add_povm(povm)
circuit = tq.Circuit([{1: tq.Gate.h}, {(0, 1): tq.Meas()}]), n_shots=10000)


Results({'00': 5122, '20': 51, '01': 3446, '21': 31, '02': 1339, '22': 11}, dim=3)

Adding State Preparation Noise

State preparation noise can be added using the add_prep() noise source.


The add_prep() noise source only takes place when a Prep() object is encountered.

We can either enter the noise as the probability of a bitflip during preparation of \(|0\rangle\):

sim = tq.Simulator().add_prep(0.01)
circuit = tq.Circuit([{0: tq.Prep()}])

Or we can specify the density matrix we want to prepare with:

sim = tq.Simulator().add_prep([[0.75, 0], [0, 0.25]])
circuit = tq.Circuit([{0: tq.Prep()}])

We can place different preparations on different qubits. Here, we have a 1% preparation bitflip error by default, but a 20 degree rotation error on qubit 3.

u = tq.Gate.from_generators("Y", 20).mat
sim = tq.Simulator().add_prep({None: 0.01, 3: u @ np.diag([1, 0]) @ u.conj().T})
circuit = tq.Circuit([{0: tq.Prep(), 3: tq.Prep()}])

We can specify preparation states of larger dimension to add leakage levels.

sim = tq.Simulator().add_prep(np.diag([0.97, 0.02, 0.01]))
circuit = tq.Circuit([{0: tq.Prep()}, {0: tq.Gate.x}])

Note that if a preparation takes place mid-circuit, then that system is traced out (with no post-selection) and replaced by the specified preparation. This is a non-unitary action and will generally result in a mixed state.

In the following example we prepare the bell state \(|00\rangle+|11\rangle\) and then prepare the second qubit in \(|1\rangle\). Thus, we end up with the final state \(\frac{\mathbb{I}}{2}\otimes|1\rangle\langle 1|\).

sim = tq.Simulator().add_prep(np.diag([0, 1]))
circuit = tq.Circuit([{0: tq.Gate.h}, {(0, 1): tq.Gate.cnot}, {1: tq.Prep()}])

When the simulator is asked to find the operator of a circuit containing a preparation object, the preparation object is simulated as a projection step.

plt.figure(figsize=(10, 5))
sim = tq.Simulator().add_prep(np.diag([1, 0]))

op1 = sim.operator(tq.Circuit([{0:}])).upgrade().mat()
tq.visualization.plot_mat(op1, ax=plt.subplot("121"))
plt.title("Superoperator without Prep() projection")

op2 = sim.operator(tq.Circuit([{0: tq.Prep()}, {0:}])).mat()
tq.visualization.plot_mat(op2, ax=plt.subplot("122"))
plt.title("Superoperator with Prep() projection")


Text(0.5, 1.0, 'Superoperator with Prep() projection')

Total running time of the script: ( 0 minutes 0.266 seconds)

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