When training Variational Quantum Algorithms we aim to find a point in the parameter space that minimizes a particular cost function, just like in the case of classical deep learning. Using the parameter-shift rule, we are able to compute the gradient of a Parametrized Quantum Circuit (PQC) and can therefore use that gradient descent method proven in classical machine learning. However vanilla gradient descent can face difficulties in practical training which can be circumvented with Quantum Natural Gradient Descent (QNG).

When minimizing a cost function $\mathcal{L}(\Theta)$ the well-known gradient descent iteratively updates the parameters $\Theta$ by descending into the direction of the gradient $$\Theta_{t+1} := \Theta_t - \eta \nabla \mathcal{L}(\Theta)\big|_{\Theta_t}.$$ Here and in the following all gradients are calculated with respect to $\Theta$ In Stochastic Gradient Descent (SGD) specifically the gradient $\nabla \mathcal{L}(\Theta)$ is approximated by the gradient of the cost function over a subset of the training data. With this update rule, gradient descent implicitly assumes a euclidean geometry of the parameter space. This can be seen when writing the update rule as $$\Theta_{t+1} := \argmin_\Theta \left[\big<\Theta - \Theta_t, \nabla \mathcal{L}(\Theta)\big|_{\Theta_t}\big> + \frac{1}{2\eta} \big|\big|\Theta-\Theta_t\big|\big|^2_2 \right],$$ where a proximity term is added, just like in the lagrangian of a spring mass. The equivalence to the gradient descent update rule can immediately be seen when solving the $\argmin$ by setting the derivative equal to zero.

The choice of euclidean geometry does however not necessarily reflect the actual parameter space. Since it gives equal weight to all parameters $\Theta_i$ ill-conditioned situations can arise as e.g. shown below.

The algorithm bounces over the valley and only slowly approaches the minimum. In the shown example the large step size aggravates the problem. For SGD a careful tuning of the learning rate is therefore especially important. Optimizers like Adam can address this problem by adjusting the step size based on previous gradients. A reparameterization of the parameters space on the other hand could lead to a problem way better suited for SGD.

So instead of using the euclidean metric $||\Theta||_2$ a distance measure for an infinitesimal vector $\text{d}\Theta$ on a curved manifold is given by $$||\Theta||_{g} = \sum_{ij}g_{ij}(\Theta)\text{d}\Theta_i\text{d}\Theta_j,$$ where $g_{ij}$ is the Riemannian metric tensor.

For every physicist, this seems very familiar. Of course, the euclidean metric is the special case of $g_{ij}=\delta_{ij}$. Using this general metric for the method of steepest descent S. Amari shows in ( Citation: , (). Natural Gradient Works Efficiently in Learning. Neural Computation, 10(2). 251–276. https://doi.org/10.1162/089976698300017746 ) that the gradient descent update rule becomes $$\Theta_{t+1} := \Theta_t - \eta G^{-1}\nabla\mathcal{L}(\Theta)\big|_{\Theta_{t+1}}\tag{1},$$ where $G^{-1}$ is the inverse of the metric $G = (g_{ij})$.

The question remains on how to determine the metric. In the framework of Information Geometry, instead of considering the parameter space, the optimization is performed on the so-called statistical manifold. A statistical manifold is a Riemannian manifold, where every point corresponds to a probability function.

In our case, we may consider the manifold of likelihoods $p(x|\Theta)$ for the different possible parameters $\Theta$. To measure the similarity between two probability distributions there exist different divergences, the most known one being the Kullback–Leibler (KL) divergence. For two distribution $p(x)$ and $q(x)$ it is defined as $$D_{KL}(p(x)||q(x)) = \sum_x p(x)\log\left(\frac{p(x)}{q(x)}\right)\tag{2}.$$ Note that formally the KL-divergence is not symmetric and thus is not a proper distance measure. However, things work out for infinitesimal distance and thus it can be used to describe the manifold locally ( Citation: , (). New insights and perspectives on the natural gradient method. Retrieved from http://arxiv.org/abs/1412.1193 ) .

Let’s try to rewrite our gradient update from SGD with the KL-divergence instead of the euclidean metric: $$\Theta_{t+1} := \argmin_\Theta \left[\big<\Theta - \Theta_t, \nabla \mathcal{L}(\Theta)\big|_{\Theta_t}\big> + \frac{1}{2\eta}D_{KL}(q(x|\Theta)||q(x|\Theta_t)) \right]$$ To minimize this expression we set the derivative to zero $$\nabla \mathcal{L}(\Theta)\bigg|_{\Theta_t} + \frac{1}{\eta}\nabla D_{KL}\left(q(x|\Theta)||q(x|\Theta_t)\right)\bigg|_{\Theta_{t+1}} = 0\tag{3}.$$ So to solve this we need the gradient of the KL-divergence, which we will approximate by Taylor expanding the $D_{KL}$ around $\Theta_t$. In the following we denote $D_{KL}(\Theta||\Theta_t) := D_{KL}(q(x|\Theta)||q(x|\Theta_t))$ for brevity. In second order we obtain \begin{align*} D_{KL}(\Theta||\Theta_t)\approx D_{KL}(\Theta_t||\Theta_t) + \nabla D_{KL}(\Theta||\Theta_t)\big|_{\Theta_t}(\Theta-\Theta_t)\\ +\frac{1}{2}(\Theta - \Theta_t)^T H_{D_{KL}}\big|_{\Theta_t} (\Theta - \Theta_t), \end{align*} where $H_{D_{KL}}$ denotes the Hessian with respect to $\Theta$. The first term obviously vanishes since the divergence for identical distributions is zero. The second becomes zero as well, which we can see if we insert the definition from Eq. $(2)$: \begin{align*} \nabla D_{KL}(\Theta||\Theta_t)\big|_{\Theta_t}=&\sum_x \nabla p(\Theta)\big|_{\Theta_t}\log\left(\frac{p(\Theta_t)}{p(\Theta_t)}\right) + p(\Theta)\nabla\log\left(\frac{p(\Theta)}{p(\Theta_t)}\right) \\ & =\sum_x \nabla p(\Theta) - p(\Theta) \nabla\log\left(p(\Theta_t)\right) = \nabla 1 = 0. \end{align*} We can now insert the Taylor expression for the KL-divergence in Eq. $(3)$ to obtain $$\nabla\mathcal{L}(\Theta)\bigg|_{\Theta_t}+\frac{1}{\eta}H_{D_{KL}}\bigg|_{\Theta_t}(\Theta - \Theta_t)=0,$$ which leads to our update-rule $$\Theta_{t+1} := \Theta_t - \eta H^{-1}_{D_{KL}}\bigg|_{\Theta_t}\nabla\mathcal{L}(\Theta)\bigg|_{\Theta_t} \tag{4}.$$ Comparing this with Eq. $(1)$ we can identify the metric $G$ with the hessian of the KL-divergence $H_{D_{KL}}$. Rearranging the terms we can bring the hessian of the KL-divergence in the familiar form of the fisher information matrix $${H_{D_{KL}}}_{ij} = g_{ij} = \sum_x p(x|\Theta)\frac{\partial \log p(x|\Theta)}{\partial \Theta_i}\frac{\partial \log p(x|\Theta)}{\partial \Theta_j}.$$ The fisher information matrix thus describes the local curvature of the statistical manifold. With Eq. $(4)$ it constitutes the classical natural gradient descent.

The optimization of PQCs is very similar to classical deep learning. We may have a quantum circuit with parameters $\Theta$. The resulting states of the circuit for fixed input data define a parametrized Hilbert space $\mathcal{H}(\Theta)$. We can define a distance measure $d$ between two states with an infinitesimal distance between the parameters $$d\left(\ket{\psi(\Theta)}, \ket{\psi(\Theta + \text{d}\Theta)}\right) = \sum_{ij} g_{ij}(\Theta)\text{d}\Theta_i\text{d}\Theta_j,$$ where $g_{ij}$ is the Fubini-Study metric ( Citation: , (). On the natural gradient for variational quantum eigensolver. Retrieved from http://arxiv.org/abs/1909.05074 ) $$\text{Re}\left[\braket{\partial_i\psi|\partial_j\psi}-\braket{\partial_i\psi|\psi}\braket{\psi|\partial_j\psi}\right],$$ where $\ket{\partial_i\psi}=\partial\ket{\psi(\Theta)}\big/\partial\Theta_i$. With this metric, we can again write out and simplify our formulation of steepest descent to obtain an update rule for the quantum natural gradient descent proposed in ( Citation: , & al., , , & (). Quantum Natural Gradient. Quantum 4, 269 (2020). https://doi.org/10.22331/q-2020-05-25-269 ) $$\Theta_{t+1} := \argmin_\Theta \left[\big<\Theta - \Theta_t, \nabla \mathcal{L}(\Theta)\big|_{\Theta_t}\big> + \frac{1}{2\eta} \big|\big|\Theta-\Theta_t\big|\big|^2_{g(\Theta_t)} \right],$$ where using our metric $g(\Theta)$ we have the norm as the scalar product $$\big|\big|\Theta-\Theta_t\big|\big|^2_{g(\Theta_t)} = \braket{\Theta - \Theta_t, g(\Theta_t)(\Theta - \Theta_t)}.$$ Setting the derivative to zero like before directly leads to $$\Theta_{t+1} = \Theta_t - \eta g^+(\Theta_t)\nabla\mathcal{L}(\Theta)\big|_{\Theta_t}.$$ Here $g^+$ denotes the pseudo-inverse of the metric tensor which is usually calculated as the Moore-Penrose-Inverse.

Computing the metric tensor can be very expensive, which is why ( Citation: , & al., , , & (). Quantum Natural Gradient. Quantum 4, 269 (2020). https://doi.org/10.22331/q-2020-05-25-269 ) proposes to compute a diagonal or block-diagonal approximation of it.

# Implementation#

Fortunately, the computation of the diagonal and block diagonal approximations of the metric tensor are already implemented in Pennylane

I want to show a little example of the usage of QNG on a real dataset as I struggled a bit with the implementation of the iteration over data. Suppose you have some circuit that takes the parameters and data as arguments. If you want to train the parameters you define some cost function e.g. a simple MSE

def cost(params, x, y):
return (y - circuit(params, x)) ** 2


After initializing the parameters params we optimize them by iterating over the training data x_train, y_train and applying steps to the optimizer QNGOptimizer which is implemented in pennylane. To compute the step, however, we need the metric tensor function, which is also implemented in pennylane. As the metric tensor function can only be obtained for function with a single argument, namely the parameters to be trained, we need to define a lambda function for every data sample that only depends on the parameters. The same goes for the cost function.

opt = qml.QNGOptimizer(learning_rate)

for it in range(epochs):
for j, sample in enumerate(x_train):
cost_fn = lambda p: cost_sample(p, sample, y[j])
metric_fn = lambda p: qml.metric_tensor(circuit, approx="block-diag")(p, sample)
params = opt.step(cost_fn, params, metric_tensor_fn=metric_fn)
print(j, end="\r")

loss = cost(params)

print(f"Epoch: {it} | Loss: {loss} |")


Note that the data needs to be defined in pennylane with requires_grad=False.

The QNG can be quite useful in avoiding Barren Plateaus in training. However of course computing the metric tensor takes time, which makes the QNG especially useful for models with a smaller number of parameters.

Amari (1998)
(). Natural Gradient Works Efficiently in Learning. Neural Computation, 10(2). 251–276. https://doi.org/10.1162/089976698300017746
Martens (2014)