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The PennyLane Guide to Quantum Gradients Hero Image

The PennyLane Guide to Quantum Gradients

Differentiable quantum circuits are at the core of many optimization-based quantum applications, such as Variational Quantum Algorithms and Quantum Machine Learning. The key ingredient is the gradient of a circuit, which can be computed in many different ways.

Discover the different quantum gradient methods in this curated guide.

Differentiable quantum circuits

How do we find a circuit’s gradient? Look into the basics of differentiable quantum circuits and their applications to simple optimizations tasks.


Gradient methods for simulation

If you have no quantum devices at your disposal, simulators are the next best thing. Learn about the gradient methods that utilize the memory storing capacities of classical computers to more efficiently compute gradients of quantum circuits.

Hardware-compatible gradient methods

Some gradient methods are better suited to work on quantum hardware, leveraging properties of quantum operations to compute gradients natively. Here are some well-known examples.

Barren Plateaus

Gradient descent can be used to solve a variety of optimization problems using quantum hardware. However, barren plateaus can get in the way. Learn more about this phenomenon.


Selected applications

What can we do with differentiable quantum circuits? They are a common tool for tackling a number of quantum research challenges.


Documentation

  • qml.gradient
  • PennyLane Optimizers
  • PennyLane JAX interface
  • qml.liealg
  • qml.transforms.mitigate_with_zne
  • qml.pulse

Differentiable quantum circuits

How do we find a circuit’s gradient? Look into the basics of differentiable quantum circuits and their applications to simple optimizations tasks.


Gradient methods for simulation

If you have no quantum devices at your disposal, simulators are the next best thing. Learn about the gradient methods that utilize the memory storing capacities of classical computers to more efficiently compute gradients of quantum circuits.


Hardware-compatible gradient methods

Some gradient methods are better suited to work on quantum hardware, leveraging properties of quantum operations to compute gradients natively. Here are some well-known examples.

Barren Plateaus

Gradient descent can be used to solve a variety of optimization problems using quantum hardware. However, barren plateaus can get in the way. Learn more about this phenomenon.


Selected applications

What can we do with differentiable quantum circuits? They are a common tool for tackling a number of quantum research challenges.

Documentation

  • qml.gradient
  • PennyLane Optimizers
  • PennyLane JAX interface
  • qml.liealg
  • qml.transforms.mitigate_with_zne
  • qml.pulse

Differentiable quantum circuits

How do we find a circuit’s gradient? Look into the basics of differentiable quantum circuits and their applications to simple optimizations tasks.


Gradient methods for simulation

If you have no quantum devices at your disposal, simulators are the next best thing. Learn about the gradient methods that utilize the memory storing capacities of classical computers to more efficiently compute gradients of quantum circuits.


Hardware-compatible gradient methods

Some gradient methods are better suited to work on quantum hardware, leveraging properties of quantum operations to compute gradients natively. Here are some well-known examples.


Barren Plateaus

Gradient descent can be used to solve a variety of optimization problems using quantum hardware. However, barren plateaus can get in the way. Learn more about this phenomenon.


Selected applications

What can we do with differentiable quantum circuits? They are a common tool for tackling a number of quantum research challenges.


Documentation

  • qml.gradient
  • PennyLane Optimizers
  • PennyLane JAX interface
  • qml.liealg
  • qml.transforms.mitigate_with_zne
  • qml.pulse
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