Construct circuits in the SDK - Amazon Braket

Construct circuits in the SDK

This section provides examples of defining a circuit, viewing available gates, extending a circuit, and viewing gates that each device supports. It also contains instructions on how to manually allocate qubits, instruct the compiler to run your circuits exactly as defined, and build noisy circuits with a noise simulator.

Gates and circuits

Quantum gates and circuits are defined in the SDK’s braket.circuits class. From the SDK, you can instantiate a new circuit object by calling Circuit().

Example: Define a circuit

The example starts by defining a sample circuit of four qubits (labelled q0, q1, q2, and q3) consisting of standard, single-qubit Hadamard gates and two-qubit CNOT gates. You can visualize this circuit by calling the print function, as the example shows.

# import the circuit module from braket.circuits import Circuit # define circuit with 4 qubits my_circuit = Circuit().h(range(4)).cnot(control=0, target=2).cnot(control=1, target=3) print(my_circuit)
T : |0| 1 | q0 : -H-C--- | q1 : -H-|-C- | | q2 : -H-X-|- | q3 : -H---X- T : |0| 1 |

Example: See all available gates

The following example shows how to look at all the available gates in Amazon Braket.

import string from braket.circuits import Gate # print all available gates in Amazon Braket gate_set = [attr for attr in dir(Gate) if attr[0] in string.ascii_uppercase] print(gate_set)
['CCNot', 'CNot', 'CPhaseShift', 'CPhaseShift00', 'CPhaseShift01', 'CPhaseShift10', 'CSwap', 'CY', 'CZ', 'H', 'I', 'ISwap', 'PSwap', 'PhaseShift', 'Rx', 'Ry', 'Rz', 'S', 'Si', 'Swap', 'T', 'Ti', 'Unitary', 'V', 'Vi', 'X', 'XX', 'XY', 'Y', 'YY', 'Z', 'ZZ']

Any of these gates can be appended to a circuit by calling the method for that type of circuit. For example, you’d call circ.h(0), to add a Hadamard gate to the first qubit.

Note

Gates are appended in place, and the example that follows adds all of the gates listed in the previous example to the same circuit.

circ = Circuit() # toffoli gate with q0, q1 the control qubits and q2 the target. circ.ccnot(0, 1, 2) # cnot gate circ.cnot(0, 1) # controlled-phase gate that phases the |11> state, cphaseshift(phi) = diag((1,1,1,exp(1j*phi))), where phi=0.15 in the examples below circ.cphaseshift(0, 1, 0.15) # controlled-phase gate that phases the |00> state, cphaseshift00(phi) = diag([exp(1j*phi),1,1,1]) circ.cphaseshift00(0, 1, 0.15) # controlled-phase gate that phases the |01> state, cphaseshift01(phi) = diag([1,exp(1j*phi),1,1]) circ.cphaseshift01(0, 1, 0.15) # controlled-phase gate that phases the |10> state, cphaseshift10(phi) = diag([1,1,exp(1j*phi),1]) circ.cphaseshift10(0, 1, 0.15) # controlled swap gate circ.cswap(0, 1, 2) # swap gate circ.swap(0,1) # phaseshift(phi)= diag([1,exp(1j*phi)]) circ.phaseshift(0,0.15) # controlled Y gate circ.cy(0, 1) # controlled phase gate circ.cz(0, 1) # X rotation with angle 0.15 circ.rx(0, 0.15) # Y rotation with angle 0.15 circ.ry(0, 0.15) # Z rotation with angle 0.15 circ.rz(0, 0.15) # Hadamard gates applied to q0, q1, q2 circ.h(range(3)) # identity gates applied to q0, q1, q2 circ.i([0, 1, 2]) # iswap gate, iswap = [[1,0,0,0],[0,0,1j,0],[0,1j,0,0],[0,0,0,1]] circ.iswap(0, 1) # pswap gate, PSWAP(phi) = [[1,0,0,0],[0,0,exp(1j*phi),0],[0,exp(1j*phi),0,0],[0,0,0,1]] circ.pswap(0, 1, 0.15) # X gate applied to q1, q2 circ.x([1, 2]) # Y gate applied to q1, q2 circ.y([1, 2]) # Z gate applied to q1, q2 circ.z([1, 2]) # S gate applied to q0, q1, q2 circ.s([0, 1, 2]) # conjugate transpose of S gate applied to q0, q1 circ.si([0, 1]) # T gate applied to q0, q1 circ.t([0, 1]) # conjugate transpose of T gate applied to q0, q1 circ.ti([0, 1]) # square root of not gate applied to q0, q1, q2 circ.v([0, 1, 2]) # conjugate transpose of square root of not gate applied to q0, q1, q2 circ.vi([0, 1, 2]) # exp(i(XX+YY) theta/4), where theta=0.15 in the examples below circ.xx(0, 1, 0.15) # exp(-iXY theta/2) circ.xy(0, 1, 0.15) # exp(-iYY theta/2) circ.yy(0, 1, 0.15) # exp(-iZZ theta/2) circ.zz(0, 1, 0.15)

Apart from the pre-defined gate set, you also can apply self-defined unitary gates to the circuit. These can be single-qubit gates (as shown in the following source code) or multi-qubit gates applied to the qubits defined by the targets parameter.

import numpy as np # apply a general unitary my_unitary = np.array([[0, 1],[1, 0]]) circ.unitary(matrix=my_unitary, targets=[0])

Example: Extend existing circuits

You can extend existing circuits by adding instructions. An Instruction is a quantum directive that describes the task to perform on a quantum device. Instruction operators include objects of type Gate only.

# import the Gate and Instruction modules from braket.circuits import Gate, Instruction # add instructions directly. circ = Circuit([Instruction(Gate.H(), 4), Instruction(Gate.CNot(), [4, 5])]) # or with add_instruction/add functions instr = Instruction(Gate.CNot(), [0, 1]) circ.add_instruction(instr) circ.add(instr) # specify where the circuit is appended circ.add_instruction(instr, target=[3, 4]) circ.add_instruction(instr, target_mapping={0: 3, 1: 4}) # print the instructions print(circ.instructions) # if there are multiple instructions, you can print them in a for loop for instr in circ.instructions: print(instr) # instructions can be copied new_instr = instr.copy() # appoint the instruction to target new_instr = instr.copy(target=[5]) new_instr = instr.copy(target_mapping={0: 5})

Example: View the gates that each device supports

Simulators support all gates in the Braket SDK, but QPU devices support a smaller subset. You can find the supported gates of a device in the device properties.

# import the device module from braket.aws import AwsDevice device = AwsDevice("arn:aws:braket:::device/qpu/ionq/ionQdevice") # get device name device_name = device.name # show supportedQuantumOperations (supported gates for a device) device_operations = device.properties.dict()['action']['braket.ir.jaqcd.program']['supportedOperations'] print('Quantum Gates supported by {}:\n {}'.format(device_name, device_operations))
Quantum Gates supported by IonQ Device: ['x', 'y', 'z', 'rx', 'ry', 'rz', 'h', 'cnot', 's', 'si', 't', 'ti', 'v', 'vi', 'xx', 'yy', 'zz', 'swap', 'i']
device = AwsDevice("arn:aws:braket:::device/qpu/rigetti/Aspen-10") # get device name device_name = device.name # show supportedQuantumOperations (supported gates for a device) device_operations = device.properties.dict()['action']['braket.ir.jaqcd.program']['supportedOperations'] print('Quantum Gates supported by {}:\n {}'.format(device.name, device_operations))
Quantum Gates supported by Aspen-10: ['cz', 'xy', 'ccnot', 'cnot', 'cphaseshift', 'cphaseshift00', 'cphaseshift01', 'cphaseshift10', 'cswap', 'h', 'i', 'iswap', 'phaseshift', 'pswap', 'rx', 'ry', 'rz', 's', 'si', 'swap', 't', 'ti', 'x', 'y', 'z']

Supported gates may need to be compiled into native gates before they can run on quantum hardware. When you submit a circuit, Amazon Braket performs this compilation automatically.

Manual qubit allocation

When you run a quantum circuit on quantum computers from Rigetti, you can optionally use manual qubit allocation to get control over which qubits are used for your algorithm. The Amazon Braket Console and the Amazon Braket SDK help you to inspect the most recent calibration data of your selected quantum processing unit (QPU) device, so you can select the best qubits for your experiment.

Manual qubit allocation enables you to run circuits with greater accuracy and to investigate individual qubit properties. Researchers and advanced users optimize their circuit design based on the latest device calibration data, and thus can obtain more accurate results.

Here’s an example of how to allocate qubits explicitly:

circ = Circuit().h(0).cnot(0, 7) # Indices of actual qubits in the QPU my_task = device.run(circ, s3_location, shots=100, disable_qubit_rewiring=True)

For more information, see the Amazon Braket examples on GitHub, or more specifically, this notebook: Allocating Qubits on QPU Devices.

Verbatim compilation

When you run a quantum circuit on quantum computers from Rigetti, you have the ability to direct the compiler to run your circuits exactly as defined, without any modifications. Using verbatim compilation, you can specify either that an entire curcuit be preserved precisely as specified or that only specific parts of it be preserved. When developing algorithms for hardware benchmarking or error mitigation protocols, you need to be able to specify the gates and circuit layouts that are to be executed on the hardware exactly. Verbatim compilation gives you direct control over the compilation process by disabling certain optimization steps, thereby ensuring that your circuits are executed exactly as designed.

Verbatim compilation is currently supported on Rigetti devices and requires the use of native gates. When using verbatim compilation, it is advisable to check the topology of the device to ensure that gates are called on connected qubits and that the circuit uses the native gates supported on the hardware. The following example shows how to programmatically access the list of native gates supported by a device:

rigetti.properties.paradigm.nativeGateSet

Also, qubit rewiring must be disabled by setting disableQubitRewiring=True for use with verbatim compilation. If disableQubitRewiring=False is set when using verbatim boxes in a compilation, the quantum circuit will fail validation and not run.

If verbatim compilation is enabled for a circuit and run on a QPU that does not support it, an error is generated indicating that an unsupported operation has caused the task to fail. As more quantum hardware natively support compiler functions, this feature will be expanded to include these devices. Devices that support verbatim compilation include it as a supported operation when queried with the following code.

from braket.aws import AwsDevice from braket.device_schema.device_action_properties import DeviceActionType device = AwsDevice("arn:aws:braket:::device/qpu/rigetti/Aspen-10") device.properties.action[DeviceActionType.JAQCD].supportedOperations

There is no additional cost associated with using verbatim compilation. You will continue to be charged for tasks executed on Braket QPU devices, notebook instances, and managed simulators based on our current rates as specified on the Amazon Braket Pricing page. For more information, see the Verbatim compilation example notebook.

Noise simulation

To instantiate the local noise simulator you can change the backend as follows:

device = LocalSimulator(backend="braket_dm")

You can build noisy circuits in two ways: (i) Build the noisy circuit from the bottom up. (ii) Take an existing, noise-free circuit and inject noise throughout. The following shows the approaches using a simple circuit with depolarizing noise and a custom Kraus channel:

# Bottom up approach # apply depolarizing noise to qubit 0 with probability of 0.1 circ = Circuit().x(0).x(1).depolarizing(0, probability=0.1) # create an arbitrary 2-qubit Kraus channel E0 = scipy.stats.unitary_group.rvs(4) * np.sqrt(0.8) E1 = scipy.stats.unitary_group.rvs(4) * np.sqrt(0.2) K = [E0, E1] # apply a two-qubit Kraus channel to qubits 0 and 2 circ = circ.kraus([0,2], K)
# Inject noise approach # define phase damping noise noise = Noise.PhaseDamping(gamma=0.1) # the noise channel is applied to all the X gates in the circuit circ = Circuit().x(0).y(1).cnot(0,2).x(1).z(2) circ_noise = circ.copy() circ_noise.apply_gate_noise(noise, target_gates = Gate.X)

Running a circuit is the same user experience as before:

task = device.run(circ, s3_location)

Or

task = device.run(circ_noise, s3_location)

For more examples, see the Braket introductory noise simulator example