List of result types - Amazon Braket
ObservablesParametersAdjoint gradient

List of result types

Amazon Braket can return different types of results when a circuit is measured using ResultType. A circuit can return the following types of results.

  • AdjointGradient returns the gradient (vector derivative) of the expectation value of a provided observable. This observable is acting on a provided target with respect to specified parameters using the adjoint differentiation method. You can only use this method when shots=0.

  • Amplitude returns the amplitude of specified quantum states in the output wave function. It is available on the SV1 and local simulators only.

  • Expectation returns the expectation value of a given observable, which can be specified with the Observable class introduced later in this chapter. The target qubits used to measure the observable must be specified, and the number of specified targets must equal the number of qubits on which the observable acts. If no targets are specified, the observable must operate only on 1 qubit and it is applied to all qubits in parallel.

  • Probability returns the probabilities of measuring computational basis states. If no targets are specified, Probability returns the probability of measuring all basis states. If targets are specified, only the marginal probabilities of the basis vectors on the specified qubits are returned. Managed simulators and QPUs are limited to 15 qubits maximum, and local simulators are limited to the system's memory size.

  • Reduced density matrix returns a density matrix for a subsystem of specified target qubits from a system of qubits. To limit the size of this result type, Braket limits the number of target qubits to a maximum of 8.

  • StateVector returns the full state vector. It is available on the local simulator.

  • Sample returns the measurement counts of a specified target qubit set and observable. If no targets are specified, the observable must operate only on 1 qubit and it is applied to all qubits in parallel. If targets are specified, the number of specified targets must equal the number of qubits on which the observable acts.

  • Variance returns the variance (mean([x-mean(x)]2)) of the specified target qubit set and observable as the requested result type. If no targets are specified, the observable must operate only on 1 qubit and it is applied to all qubits in parallel. Otherwise, the number of targets specified must equal the number of qubits to which the observable can be applied.

The supported result types for different devices:

Local sim

SV1

DM1

TN1

Rigetti

IonQ

IQM

Adjoint Gradient

N

Y

N

N

N

N

N

Amplitude

Y

Y

N

N

N

N

N

Expectation

Y

Y

Y

Y

Y

Y

Y

Probability

Y

Y

Y

N

Y

Y

Y

Reduced density matrix

Y

N

Y

N

N

N

N

State vector

Y

N

N

N

N

N

N

Sample

Y

Y

Y

Y

Y

Y

Y

Variance

Y

Y

Y

Y

Y

Y

Y

You can check the supported result types by examining the device properties, as shown in the following example.

from braket.aws import AwsDevice

device = AwsDevice("arn:aws:braket:us-west-1::device/qpu/rigetti/Ankaa-3")

# Print the result types supported by this device
for iter in device.properties.action['braket.ir.openqasm.program'].supportedResultTypes:
    print(iter)
name='Sample' observables=['x', 'y', 'z', 'h', 'i'] minShots=10 maxShots=50000
name='Expectation' observables=['x', 'y', 'z', 'h', 'i'] minShots=10 maxShots=50000
name='Variance' observables=['x', 'y', 'z', 'h', 'i'] minShots=10 maxShots=50000
name='Probability' observables=None minShots=10 maxShots=50000

To call a ResultType, append it to a circuit, as shown in the following example.

from braket.circuits import Circuit, Observable

circ = Circuit().h(0).cnot(0, 1).amplitude(state=["01", "10"])
circ.probability(target=[0, 1])
circ.probability(target=0)
circ.expectation(observable=Observable.Z(), target=0)
circ.sample(observable=Observable.X(), target=0)
circ.state_vector()
circ.variance(observable=Observable.Z(), target=0)

# Print one of the result types assigned to the circuit
print(circ.result_types[0])
Note

Different quantum devices provide results in various formats. For example, Rigetti devices return measurements, while IonQ devices provide probabilities. The Amazon Braket SDK offers a measurements property for all results. However, for devices that return probabilities, these measurements are post-computed and based on the probabilities, as per-shot measurements are not available. To determine if a result has been post-computed, check the measurements_copied_from_device on the result object. This operation is detailed in the gate_model_quantum_task_result.py file in the Amazon Braket SDK GitHub repository.

Observables

Amazon Braket's Observable class allows you to measure a specific observable.

You can apply only one unique non-identity observable to each qubit. An error occurs If you specify two or more different non-identity observables to the same qubit. For this purpose, each factor of a tensor product counts as an individual observable. This means you can have multiple tensor products on the same qubit, as long as the factors acting on that qubit remain the same.

An observable can be scaled and add other observables (scaled or not). This creates a Sum which can be used in the AdjointGradient result type.

The Observable class includes the following observables.

import numpy as np

Observable.I()
Observable.H()
Observable.X()
Observable.Y()
Observable.Z()

# Get the eigenvalues of the observable
print("Eigenvalue:", Observable.H().eigenvalues)
# Or rotate the basis to be computational basis
print("Basis rotation gates:", Observable.H().basis_rotation_gates)

# Get the tensor product of the observable for the multi-qubit case
tensor_product = Observable.Y() @ Observable.Z()
# View the matrix form of an observable by using
print("The matrix form of the observable:\n", Observable.Z().to_matrix())
print("The matrix form of the tensor product:\n", tensor_product.to_matrix())

# Factorize an observable in the tensor form
print("Factorize an observable:", tensor_product.factors)

# Self-define observables, given it is a Hermitian
print("Self-defined Hermitian:", Observable.Hermitian(matrix=np.array([[0, 1], [1, 0]])))

print("Sum of other (scaled) observables:", 2.0 * Observable.X() @ Observable.X() + 4.0 * Observable.Z() @ Observable.Z())
Eigenvalue: [ 1. -1.]
Basis rotation gates: (Ry('angle': -0.7853981633974483, 'qubit_count': 1),)
The matrix form of the observable:
 [[ 1.+0.j  0.+0.j]
 [ 0.+0.j -1.+0.j]]
The matrix form of the tensor product:
 [[ 0.+0.j  0.+0.j  0.-1.j  0.+0.j]
 [ 0.+0.j -0.+0.j  0.+0.j  0.+1.j]
 [ 0.+1.j  0.+0.j  0.+0.j  0.+0.j]
 [ 0.+0.j  0.-1.j  0.+0.j -0.+0.j]]
Factorize an observable: (Y('qubit_count': 1), Z('qubit_count': 1))
Self-defined Hermitian: Hermitian('qubit_count': 1, 'matrix': [[0.+0.j 1.+0.j], [1.+0.j 0.+0.j]])
Sum of other (scaled) observables: Sum(TensorProduct(X('qubit_count': 1), X('qubit_count': 1)), TensorProduct(Z('qubit_count': 1), Z('qubit_count': 1)))

Parameters

Circuits can incorporate free parameters. These free parameters only need to be constructed once to run multiple times, and can be used to compute gradients.

Each free parameter uses a string-encoded name that is used to:

  • Set parameter values

  • Identify which parameters to use

from braket.circuits import Circuit, FreeParameter, observables
from braket.parametric import FreeParameter

theta = FreeParameter("theta")
phi = FreeParameter("phi")
circ = Circuit().h(0).rx(0, phi).ry(0, phi).cnot(0, 1).xx(0, 1, theta)

Adjoint gradient

The SV1 device calculates the adjoint gradient of an observable expectation value, including multi-term Hamiltonian. To differentiate parameters, specify their name (in string format) or by direct reference.

from braket.aws import AwsDevice
from braket.devices import Devices

device = AwsDevice(Devices.Amazon.SV1)

circ.adjoint_gradient(observable=3 * Observable.Z(0) @ Observable.Z(1) - 0.5 * observables.X(0), parameters = ["phi", theta])

Passing fixed parameter values as arguments to a parameterized circuit will remove the free parameters. Running this circuit with AdjointGradient produces an error, because the free parameters no longer exist. The follow code example demonstrates the correct and incorrect usage:

# Will error, as no free parameters will be present
#device.run(circ(0.2), shots=0)

# Will succeed
device.run(circ, shots=0, inputs={'phi': 0.2, 'theta': 0.2})