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Digital-Analog Emulation

From theory to implementation

Qadence includes primitives for the construction of Ising-like Hamiltonians to account for custom qubit interaction. This allows to simulate systems close to real quantum computing platforms such as neutral atoms. The general form for time-independent Ising Hamiltonians is

\[ \mathcal{H} = \sum_{i} \frac{\hbar\Omega}{2} \hat\sigma^x_i - \sum_{i} \hbar\delta \hat n_i + \mathcal{H}_{\textrm{int}}, \]

where \(\Omega\) is the Rabi frequency, \(\delta\) is the detuning, \(\hat n = \frac{1-\hat\sigma_z}{2}\) is the number operator, and \(\mathcal{H}_{\textrm{int}}\) a pair-wise interaction term. Two central operations implement this Hamiltonian as blocks:

The wait operation can be emulated with an \(ZZ\)- (Ising) or an \(XY\)-interaction:

from qadence import Register, wait, add_interaction, run, Interaction

block = wait(duration=3000)

reg = Register.from_coordinates([(0,0), (0,5)])  # Dimensionless.
emulated = add_interaction(reg, block, interaction=Interaction.XY)  # or Interaction.ZZ for Ising.

block = WaitBlock(t=3000.0, support=(<QubitSupportType.GLOBAL: 'global'>,)) 

emulated.generator = 

AddBlock(0,1)
└── [mul: 29.600] 
    └── AddBlock(0,1)
        ├── KronBlock(0,1)
        │   ├── X(0)
        │   └── X(1)
        └── KronBlock(0,1)
            ├── Y(0)
            └── Y(1)

The AnalogRot constructor can be used to create a fully customizable ConstantAnalogRotation instances:

import torch
from qadence import AnalogRot, AnalogRX

# Implement a global RX rotation by setting all parameters.
block = AnalogRot(
    duration=1000., # [ns]
    omega=torch.pi, # [rad/μs]
    delta=0,        # [rad/μs]
    phase=0,        # [rad]
)

# Or use the shortcut.
block = AnalogRX(torch.pi)

AnalogRot = ConstantAnalogRotation(α=3.14159265358979, t=1000.00000000000, support=(<QubitSupportType.GLOBAL: 
'global'>,), Ω=3.14159265358979, δ=0, φ=0)

AnalogRX = ConstantAnalogRotation(α=3.14159265358979, t=1000.00000000000, support=(<QubitSupportType.GLOBAL: 
'global'>,), Ω=3.14159265358979, δ=0, φ=0)

Automatic emulation in the PyQTorch backend

All analog blocks are automatically translated to their emulated version when running them with the PyQTorch backend:

import torch
from qadence import Register, AnalogRX, sample

reg = Register.from_coordinates([(0,0), (0,5)])
sample = sample(reg, AnalogRX(torch.pi))

sample = [Counter({'00': 40, '10': 32, '01': 28})]

To compose analog blocks, the regular chain and kron operations can be used under the following restrictions:

  • The resulting AnalogChain type can only be constructed from AnalogKron blocks or globally supported primitive analog blocks.
  • The resulting AnalogKron type can only be constructed from non-global analog blocks with the same duration.
import torch
from qadence import AnalogRot, kron, chain, wait

# Only analog blocks with a global qubit support can be composed
# using chain.
analog_chain = chain(wait(duration=200), AnalogRot(duration=300, omega=2.0))

# Only blocks with the same `duration` can be composed using kron.
analog_kron = kron(
    wait(duration=1000, qubit_support=(0,1)),
    AnalogRot(duration=1000, omega=2.0, qubit_support=(2,3))
)

Analog Chain block = AnalogChain(t=500.000000000000, support=(<QubitSupportType.GLOBAL: 'global'>,))
├── WaitBlock(t=200.0, support=(<QubitSupportType.GLOBAL: 'global'>,))
└── ConstantAnalogRotation(α=0.600000000000000, t=300, support=(<QubitSupportType.GLOBAL: 'global'>,), 
    Ω=2.00000000000000, δ=0, φ=0)
Analog Kron block = AnalogKron(t=1000, support=(0, 1, 2, 3))
├── WaitBlock(t=1000.0, support=(0, 1))
└── ConstantAnalogRotation(α=2.00000000000000, t=1000, support=(2, 3), Ω=2.00000000000000, δ=0, φ=0)

Composing digital & analog blocks

It is possible to compose digital and analog blocks where the additional restrictions for chain and kron only apply to composite blocks which contain analog blocks only. For further details, see AnalogChain and AnalogKron.

Fitting a simple function

Analog blocks can be parametrized in the usual Qadence manner. Like any other parameters, they can be optimized. The next snippet examplifies the creation of an analog and paramertized ansatze to fit a sine function. First, define an ansatz block and an observable:

import torch
from qadence import Register, FeatureParameter, VariationalParameter
from qadence import AnalogRX, AnalogRZ, Z
from qadence import wait, chain, add

pi = torch.pi

# A two qubit register.
reg = Register.from_coordinates([(0, 0), (0, 12)])

# An analog ansatz with an input time parameter.
t = FeatureParameter("t")

block = chain(
    AnalogRX(pi/2.),
    AnalogRZ(t),
    wait(1000 * VariationalParameter("theta", value=0.5)),
    AnalogRX(pi/2),
)

# Total magnetization observable.
obs = add(Z(i) for i in range(reg.n_qubits))
Plotting functions plot and scatter 

def plot(ax, x, y, **kwargs):
    xnp = x.detach().cpu().numpy().flatten()
    ynp = y.detach().cpu().numpy().flatten()
    ax.plot(xnp, ynp, **kwargs)

def scatter(ax, x, y, **kwargs):
    xnp = x.detach().cpu().numpy().flatten()
    ynp = y.detach().cpu().numpy().flatten()
    ax.scatter(xnp, ynp, **kwargs)

Next, define the dataset to train on and plot the initial prediction. The differentiation mode can be set to either DiffMode.AD or DiffMode.GPSR.

import matplotlib.pyplot as plt
from qadence import QuantumCircuit, QuantumModel, DiffMode

# Define a quantum model including digital-analog emulation.
circ = QuantumCircuit(reg, block)
model = QuantumModel(circ, obs, diff_mode=DiffMode.GPSR)

# Time support dataset.
x_train = torch.linspace(0, 6, steps=30)
# Function to fit.
y_train = -0.64 * torch.sin(x_train + 0.33) + 0.1
# Initial prediction.
y_pred_initial = model.expectation({"t": x_train})
2023-10-16T14:57:57.821781 image/svg+xml Matplotlib v3.7.3, https://matplotlib.org/

Finally, the classical optimization part is handled by PyTorch:

# Use PyTorch built-in functionality.
mse_loss = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=5e-2)

# Define a loss function.
def loss_fn(x_train, y_train):
    return mse_loss(model.expectation({"t": x_train}).squeeze(), y_train)

# Number of epochs to train over.
n_epochs = 200

# Optimization loop.
for i in range(n_epochs):
    optimizer.zero_grad()

    loss = loss_fn(x_train, y_train)
    loss.backward()
    optimizer.step()

# Get and visualize the final prediction.
y_pred = model.expectation({"t": x_train})
2023-10-16T14:58:00.921006 image/svg+xml Matplotlib v3.7.3, https://matplotlib.org/