Learn PENNYLANE with Real Code Examples

Quantum ML consulting

Hybrid algorithm development for research and industry

Teaching and workshops on quantum machine learning

Cloud-based hybrid model prototyping

Benchmarking hybrid quantum-classical workflows

Expanded device plugin support

Improved gradient methods and differentiation techniques

More hybrid ML model examples and tutorials

Enhanced integration with classical ML frameworks

Community-driven feature additions and research contributions

If you need only low-level quantum gate control

For non-hybrid quantum algorithms not involving ML

When working solely with a single hardware provider without ML integration

For very large-scale simulation beyond classical resources

If automatic differentiation is not required

PennyLane is a versatile library for hybrid quantum-classical machine learning and differentiable quantum programming.

It abstracts quantum hardware through devices and QNodes, supports automatic differentiation, and integrates with major ML frameworks.

Ideal for research, prototyping variational algorithms, and training hybrid models on multiple simulators and hardware backends.

It bridges classical and quantum computing, enabling gradient-based optimization and ML applications.

Is PennyLane free to use?

Yes, it is open-source under Apache 2.0 license.

Which hardware does PennyLane support?

Supports multiple providers via plugins: Qiskit, Cirq, Forest, Braket, Rigetti, etc.

Can I compute gradients automatically?

Yes - PennyLane supports automatic differentiation using parameter-shift or finite-difference methods.

Does PennyLane support hybrid ML workflows?

Yes - integrates seamlessly with PyTorch, TensorFlow, and JAX.

How do I simulate circuits locally?

Use built-in devices like `default.qubit` or `default.mixed`.