Download

Download this file as Jupyter notebook: intro.ipynb.

Simple Compilation Workflow

This guide illustrates how users can translate between programs expressed through high-level quantum circuits and waveform programs that can be run on hardware.

Simple translation tasks for single- and multi-qudit gate and measurements can be automated using a CalibrationSet, as shown in this guide. More complex translations require the explicit definition of Linkers, which is demonstrated in Introduction to linkers and the following guides.

Setting up

To start with, we define our qudit topology through a QuditGraph and instantiate a new CalibrationSet based on this configuration.

[2]:

import keysight.qcs as qcs

# create a topology consisting of 4 qudits with couplings between (0, 2) and (1, 3)
n_qudits = 4
qudits = qcs.Qudits(range(n_qudits))
pairs = qudits.make_connectivity([(0, 2), (1, 3)])

# instantiate a qudit graph with the above couplings
topology = qcs.QuditGraph(qudits, pairs)

# instantiate a new calibration set with this topology
calset = qcs.CalibrationSet(topology)

Next, we define a few basic variables as well as virtual channels for our qudits.

[3]:

qudit_frequencies = qcs.Array(
    "qudit_freqs", value=[4.91e9, 4.92e9, 4.93e9, 4.94e9], dtype=float
)
readout_frequencies = qcs.Array(
    "readout_freqs", value=[5.91e9, 5.92e9, 5.93e9, 5.94e9], dtype=float
)

durations = qcs.Array("multi_qubit_duration", value=[50e-9, 60e-9], dtype=float)
amplitudes = qcs.Array("multi_qubit_amplitudes", value=[0.5, 0.6], dtype=float)


# define virtual channels to represent our qudit channels
channels = qcs.Channels(range(n_qudits), "control")
readout_channels = qcs.Channels(range(n_qudits), "readout")

The virtual control and readout channels that we defined in the last step can later be mapped to physical channels on the hardware using a ChannelMapper as shown in Channel mappers.

Single-qudit gates

Now we specify how single-qudit gates are to be handled. For this example, we will use a simple X gate. We define a waveform to replace this gate with when encountered in a program and call the add_sq_gate() method to add this gate to our calibration set.

[4]:

# define the matching waveform for an X gate
x_waveform = qcs.RFWaveform(50e-9, qcs.GaussianEnvelope(), 0.5, qudit_frequencies)

# add to calibration set
calset.add_sq_gate("x", qcs.GATES.x, x_waveform, channels=channels)

The variables within our x_waveform are automatically added to our calibration set and broadcasted to match the number of single qudits in our topology. Inspecting the variables shows all the variables that were added:

[5]:

list(calset.variables.variables)

[5]:

[Array(name=x_duration, shape=(4,), dtype=float, unit=s),
 Array(name=x_amplitude, shape=(4,), dtype=float, unit=none),
 Array(name=qudit_freqs, shape=(4,), dtype=float, unit=Hz),
 Array(name=x_phase, shape=(4,), dtype=float, unit=rad),
 Array(name=x_post_phase, shape=(4,), dtype=float, unit=rad)]

We can see the qudit_freqs variable that we created above, and in addition the calibration set generated variables for all the other waveform parameters, all with shape (4,). However, since we initialized our waveforms with only a single value for the duration, amplitude and phases, all these arrays will hold those values.

For example, notice how the duration has been broadcasted four times:

[6]:

calset.variables.x_duration.value

[6]:

array([5.e-08, 5.e-08, 5.e-08, 5.e-08])

This allows for individual calibration of each of these parameters for each qudits.

The values stored in a calibration set can also be saved on disk using the export_values() method:

[7]:

# calset.export_values("calibration_values.qcs")

loaded_file = open("calibration_values.qcs").read()
print(loaded_file)

{
  "qudits_0": {
    "x_duration": 5E-08,
    "x_amplitude": 0.5,
    "qudit_freqs": 4910000000.0,
    "x_phase": 0.0,
    "x_post_phase": 0.0
  },
  "qudits_1": {
    "x_duration": 5E-08,
    "x_amplitude": 0.5,
    "qudit_freqs": 4920000000.0,
    "x_phase": 0.0,
    "x_post_phase": 0.0
  },
  "qudits_2": {
    "x_duration": 5E-08,
    "x_amplitude": 0.5,
    "qudit_freqs": 4930000000.0,
    "x_phase": 0.0,
    "x_post_phase": 0.0
  },
  "qudits_3": {
    "x_duration": 5E-08,
    "x_amplitude": 0.5,
    "qudit_freqs": 4940000000.0,
    "x_phase": 0.0,
    "x_post_phase": 0.0
  }
}

The values are stored per qudit and can be edited and loaded back in using the import_values() method:

[8]:

calset.import_values("calibration_values.qcs")

Cross-resonance gate

Next up, we define a parametric gate that represents our cross-resonance gate, which we define here through a parameterized ZX Hamiltonian. We replace this operation with a waveform on the control qudit at the frequency of the target qudits, and a waveform with attenuated amplitude on the target qudits. We map our first two qudits (indexed 0 and 1) to controls and the latter two to targets.

[9]:

# Define the gate operation through a ZX Hamiltonian - the & operator represents
# the Kronecker product
cr_gate = qcs.ParametricGate([qcs.PAULIS.sigma_z & qcs.PAULIS.sigma_x], ["beta"])

angles = qcs.Array(name="beta", value=[0.3, 0.4], dtype=float)
cr_param_gate = qcs.ParameterizedGate(cr_gate, angles)

dur = qcs.Array("multi_qubit_duration", value=[50e-9, 60e-9], dtype=float)
amps = qcs.Array("multi_qubit_amplitudes", value=[1, 1], dtype=float)

# define the waveforms to replace the gate with

# play the control waveform with the frequency of the target qudits
cr_control_waveform = qcs.RFWaveform(
    dur, qcs.GaussianEnvelope(), amps * angles, qudit_frequencies[2:]
)
# play a compensating waveform on the control qudits with smaller amplitude
cr_target_waveform = qcs.RFWaveform(
    dur, qcs.GaussianEnvelope(), amps * angles * 0.2, qudit_frequencies[2:]
)

# add to calibration set
calset.add_cr_gate(
    cr_param_gate,
    pairs,
    "x",
    control_waveform=cr_control_waveform,
    target_waveform=cr_target_waveform,
    name="CR",
)

More details on how to implement multi-qudit linkers can be found in Compiling multi-qubit native gates to waveforms.

Measurements

Lastly, we define how measurements are to be handled. To keep it simple, we only instantiate a single readout drive pulse that is played on the control channels simultaneously to an acquisition. For a more elaborate example of measurement translation, see Compiling measurements to waveforms and acquisitions.

[10]:

readout_drive_pulse = qcs.RFWaveform(
    300e-9, qcs.GaussianEnvelope(), 0.5, readout_frequencies
)

# add to the calibration set
calset.add_measurement(
    readout_drive_pulse,
    name="measure",
    readout_channels=channels,
    acquire_channels=readout_channels,
)

Apply to a quantum circuit

Now we are ready to define our programs in terms of quantum circuit and translate them down to the hardware layer. We create a simple program that contains all the operations used above:

[11]:

program = qcs.Program()

program.add_gate(qcs.GATES.x, qudits[0])
program.add_gate(qcs.GATES.x, qudits[1])

program.add_parametric_gate(cr_gate, angles[:1], (qudits[0], qudits[2]))
program.add_parametric_gate(cr_gate, angles[:1], (qudits[1], qudits[3]))

program.add_measurement(qudits)

program.draw()

  
    keysight-logo-svg
    
      Program
      
    Program
    
                Duration
                undefined
            
                Layers
                3
            
                Targets
                4
            
                Repetitions
                
      Layer #0
      
    Layer #0 
    
                Duration
                undefined
            
      Layer #1
      
    Layer #1 
    
                Duration
                undefined
            
      Layer #2
      
    Layer #2 
    
                Duration
                undefined
            
          qudits
        
            0
          
                      X
                    Gate X on ('qudits', 0)

        Matrix:

                    0
                    1
                
                    1
                    0
                
                      MULTIGATE
                    ParameterizedGate  on (('qudits', 0), ('qudits', 2))

                Parameters
                beta
            
                  Values
                ScalarRef(name=beta, value=0.3, dtype=float, unit=none)
              
Matrices

                    0
                    1
                    0
                    0
                
                    1
                    0
                    0
                    0
                
                    0
                    0
                    0
                    -1
                
                    0
                    0
                    -1
                    0
                
    Measure on ('qudits', 0)
        
        Parameters
        
                            Dim
                            2
                            
            1
          
                      X
                    Gate X on ('qudits', 1)

        Matrix:

                    0
                    1
                
                    1
                    0
                
                      MULTIGATE
                    ParameterizedGate  on (('qudits', 1), ('qudits', 3))

                Parameters
                beta
            
                  Values
                ScalarRef(name=beta, value=0.3, dtype=float, unit=none)
              
Matrices

                    0
                    1
                    0
                    0
                
                    1
                    0
                    0
                    0
                
                    0
                    0
                    0
                    -1
                
                    0
                    0
                    -1
                    0
                
    Measure on ('qudits', 1)
        
        Parameters
        
                            Dim
                            2
                            
            2
          
                      MULTIGATE
                    ParameterizedGate  on (('qudits', 2), ('qudits', 0))

                Parameters
                beta
            
                  Values
                ScalarRef(name=beta, value=0.3, dtype=float, unit=none)
              
Matrices

                    0
                    1
                    0
                    0
                
                    1
                    0
                    0
                    0
                
                    0
                    0
                    0
                    -1
                
                    0
                    0
                    -1
                    0
                
    Measure on ('qudits', 2)
        
        Parameters
        
                            Dim
                            2
                            
            3
          
                      MULTIGATE
                    ParameterizedGate  on (('qudits', 3), ('qudits', 1))

                Parameters
                beta
            
                  Values
                ScalarRef(name=beta, value=0.3, dtype=float, unit=none)
              
Matrices

                    0
                    1
                    0
                    0
                
                    1
                    0
                    0
                    0
                
                    0
                    0
                    0
                    -1
                
                    0
                    0
                    -1
                    0
                
    Measure on ('qudits', 3)
        
        Parameters
        
                            Dim
                            2

We can now compile this program using our calibration set.

[12]:

compiled_program = qcs.LinkerPass(*calset.linkers.values()).apply(program)

compiled_program.draw()

Program

Layer #0

Layer #1

Layer #2

control

0

1

2

3

readout

0

1

2

3

And we can see that all the explicit and implicit variables used by the operations in this program have been added to the calibration set:

[13]:

list(calset.variables.variables)

[13]:

[Array(name=x_duration, shape=(4,), dtype=float, unit=s),
 Array(name=x_amplitude, shape=(4,), dtype=float, unit=none),
 Array(name=qudit_freqs, shape=(4,), dtype=float, unit=Hz),
 Array(name=x_phase, shape=(4,), dtype=float, unit=rad),
 Array(name=x_post_phase, shape=(4,), dtype=float, unit=rad),
 Array(name=beta, shape=(2,), dtype=float, unit=none),
 Array(name=multi_qubit_duration, shape=(2,), dtype=float, unit=s),
 Array(name=multi_qubit_amplitudes, shape=(2,), dtype=float, unit=none),
 Array(name=CR_control_phase, shape=(2,), dtype=float, unit=rad),
 Array(name=CR_control_post_phase, shape=(2,), dtype=float, unit=rad),
 Array(name=CR_target_phase, shape=(2,), dtype=float, unit=rad),
 Array(name=CR_target_post_phase, shape=(2,), dtype=float, unit=rad),
 Array(name=measure_duration, shape=(4,), dtype=float, unit=s),
 Array(name=measure_amplitude, shape=(4,), dtype=float, unit=none),
 Array(name=readout_freqs, shape=(4,), dtype=float, unit=Hz),
 Array(name=measure_phase, shape=(4,), dtype=float, unit=rad),
 Array(name=measure_post_phase, shape=(4,), dtype=float, unit=rad)]

Download

Download this file as Jupyter notebook: intro.ipynb.

On this page