Variational quantum algorithms
Variational algorithms on noisy devices and quantum machine learning (QML)[^1] in particular are one of the main
target applications for Qadence. For this purpose, the library offers both flexible symbolic expressions for the
quantum circuit parameters via sympy (see here for more details) and native automatic
differentiation via integration with PyTorch deep learning framework.
Furthermore, Qadence offers a wide range of utilities for helping building and researching quantum machine learning algorithms, including:
- a set of constructors for circuits commonly used in quantum machine learning such as feature maps and ansatze
- a set of tools for training and optimizing quantum neural networks and loading classical data into a QML algorithm
Some simple examples
Qadence symbolic parameter interface allows to create arbitrary feature maps to encode classical data into quantum circuits with an arbitrary non-linear function embedding for the input values:
import qadence as qd
from qadence.operations import *
import torch
from sympy import acos
n_qubits = 4
# Example feature map, also directly available with the `feature_map` function
fp = qd.FeatureParameter("phi")
fm = qd.kron(RX(i, acos(fp)) for i in range(n_qubits))
# the key in the dictionary must correspond to
# the name of the assigned to the feature parameter
inputs = {"phi": torch.rand(3)}
samples = qd.sample(fm, values=inputs)
The constructors.feature_map module provides
convenience functions to build commonly used feature maps where the input parameter
is encoded in the single-qubit gates rotation angle. This function will be further
demonstrated in the QML constructors tutorial.
Furthermore, Qadence is natively integrated with PyTorch automatic differentiation engine thus Qadence quantum models can be used seamlessly in a PyTorch workflow.
Let's create a quantum neural network model using the feature map just defined, a
digital-analog variational ansatz (also explained here) and a
simple observable \(X(0) \otimes X(1)\). We use the convenience QNN quantum model abstraction.
ansatz = qd.hea(n_qubits, strategy="sDAQC")
circuit = qd.QuantumCircuit(n_qubits, fm, ansatz)
observable = qd.kron(X(0), X(1))
model = qd.QNN(circuit, observable)
# NOTE: the `QNN` is a torch.nn.Module
assert isinstance(model, torch.nn.Module)
Differentiation works the same way as any other PyTorch module:
values = {"phi": torch.rand(10, requires_grad=True)}
# the forward pass of the quantum model returns the expectation
# value of the input observable
out = model(values)
# you can compute the gradient with respect to inputs using
# PyTorch autograd differentiation engine
dout = torch.autograd.grad(out, values["phi"], torch.ones_like(out), create_graph=True)[0]
print(f"First-order derivative w.r.t. the feature parameter: \n{dout}")
# you can also call directly a backward pass to compute derivatives with respect
# to the variational parameters and use it for implementing variational
# optimization
out.sum().backward()
Quantum model output:
tensor([[0.1721],
[0.2299],
[0.1625],
[0.0259],
[0.0802],
[0.1546],
[0.1035],
[0.0992],
[0.2069],
[0.1700]], grad_fn=<CatBackward0>)
First-order derivative w.r.t. the feature parameter:
tensor([-0.2725, -0.1569, -0.2825, 0.7773, -0.3014, -0.2894, -0.3087, -0.3082,
-0.2185, -0.2749], grad_fn=<MulBackward0>)
To run QML on real devices, Qadence offers generalized parameter shift rules (GPSR) 1
for arbitrary quantum operations which can be selected when constructing the
QNN model:
model = qd.QNN(circuit, observable, diff_mode="gpsr")
out = model(values)
dout = torch.autograd.grad(out, values["phi"], torch.ones_like(out), create_graph=True)[0]
print(f"First-order derivative w.r.t. the feature parameter: \n{dout}")
See here for more details on how the parameter shift rules implementation works in Qadence.
References
[^1] Schuld, Petruccione, Machine learning on Quantum Computers, Springer Nature (2021)