Learn STRAWBERRY-FIELDS with Real Code Examples

Research grants and collaborations

Quantum algorithm consulting

Education and training in photonic quantum computing

Hybrid quantum-classical optimization solutions

Scientific publications and workshops

Enhanced multi-mode photonic hardware support

Improved non-Gaussian gate libraries

Better integration with TensorFlow and PyTorch

Advanced quantum machine learning modules

Expanded tutorials and educational resources

If only qubit-based quantum computation is required

For users unfamiliar with photonic or continuous-variable systems

When hardware access to non-photonic devices is needed

For small, classical quantum simulations where qubits suffice

If Python integration with ML frameworks is not desired

Strawberry Fields is a Python library for photonic continuous-variable quantum computing.

Supports circuit construction, simulation, and execution on photonic hardware.

Integrates with ML frameworks for hybrid quantum-classical algorithms.

Offers Gaussian and Fock simulators, along with resource and state analysis tools.

Widely used for research, quantum algorithm prototyping, and photonic quantum machine learning.

Is Strawberry Fields free?

Yes - open-source under Apache 2.0 license.

Which quantum devices does SF support?

Simulators locally; Xanadu photonic hardware backends.

Can SF simulate large photonic circuits?

Yes - Fock and Gaussian simulators for various circuit sizes.

Does SF support quantum machine learning?

Yes - integrates with PennyLane, TensorFlow, and PyTorch.

Is SF suitable for beginners?

Yes, with Python experience and some understanding of photonic quantum computing.