The quickest and easiest thing to do is simply to build a (simulated) quantum computer inside an existing machine learning library.
This is the route we followed with our first software offering Strawberry Fields, which features a photonic quantum simulator written entirely using TensorFlow.
It was the first quantum simulator to offer all the machine learning goodies that TensorFlow provides, in particular the automatic differentiation and optimization features.
QuantumFlow, another toolkit released late last year, pursued the same idea.
With these simulators, users can now design and optimize quantum circuits with minimal effort.
The value of these tools is displayed by the speed and volume of new scientific research that they have enabled⁴.
“While the simulator route is useful for quantum machine learning in the short term, it is fundamentally not scalable.
”Unfortunately, this approach is limited.
The whole reason to build a quantum computer is because they will be able to do things that a classical computer can’t match.
A classical simulator, written in TensorFlow, NumPy, C++, or any other framework, will only be able to simulate small, limited, quantum computations.
While the simulator route is useful for quantum machine learning in the short term, it is fundamentally not scalable.
QML 1.
0: Porting machine learning to quantum computingDuring the NISQ era, new quantum devices are regularly coming online.
While imperfect, they are expected to be powerful enough to show quantum advantage or supremacy.
With this hardware now available, the question becomes: what should we do with it?Given the emerging paradigm outlined above, we thought: wouldn’t it be great to train a quantum computer as easily as you would train a deep neural network? And to use the same tools — like PyTorch and TensorFlow — that are so powerful and so popular in deep learning?“Take all the best features from deep learning software and make them truly native to quantum devices.
”Comparison of some available quantum software libraries.
This is the vision we had in building PennyLane.
Instead of simulating a quantum computer within conventional machine learning software, we would take all the best features from deep learning software and make them truly native to quantum devices.
From day one, PennyLane has provided two key features which we believe will be crucial for near-term QML⁵:i) automatic differentiation of quantum circuits; andii) a QNode abstraction for building hybrid quantum-classical and multi-device computations.
The initial release of PennyLane leveraged the autograd library to provide these features, via a NumPy-like interface.
In the latest release, we complete the vision, providing seamless integration of PennyLane with PyTorch and TensorFlow.
You can now place tensor objects from these libraries on real-world quantum hardware, extending the multi-device paradigm to encompass CPUs, GPUs, and now QPUs.
Complex multi-stage models can now be built and trained, combining quantum circuits smoothly with deep nets.
At the same time, we’ve further expanded the number of quantum devices accessible via PennyLane.
PennyLane now interfaces with Xanadu’s Strawberry Fields, Rigetti’s Forest/Quantum Cloud Service, IBM’s Qiskit, and ProjectQ from ETH Zürich.
Big thanks to Keri McKiernan and Carsten Blank for contributing to this effort.
Example: Training a quantum circuit with PennyLane and PyTorchTime to see everything in action.
In the simple code stub below, we connect a single-qubit quantum circuit (running on the Forest device) with PyTorch, which processes the circuit’s output.
Combining quantum computations and classical machine learning with PennyLane and PyTorch.
The cost function will try to match the qubit’s state — the direction it points on the Bloch sphere — to a target value, initially at the south pole.
Using PennyLane’s automatic differentiation features and the built-in PyTorch optimizers, we can adjust the circuit’s parameters until the qubit matches the target.
As an added challenge, every 100 iterations, the target switches poles, and the qubit state has to adjust in real-time.
Here’s what the whole process looks like in action:Training a qubit state (coloured arrow) to match a target point (red x).
This is just the tip of the iceberg for combining quantum computing with machine learning.
Try it out yourself!.If you create something cool with PennyLane, be sure to enter it in our software competition, for a chance to win $1000!Footnotes:[1] And coming soon, support for TPUs.
[2] Exactly which vector space depends on the type of quantum computer.
For example, a single qubit lives on a vector space of dimension 2.
On the other hand, photonic quantum computers work on L², the infinite-dimensional space of square-integrable functions.
[3] Sometimes called parametric (quantum) circuits.
[4] See, for example, the following works: [a], [b], [c], [d], [e], [f], [g].
[5] The key trick is to use the quantum device itself to evaluate gradients of quantum circuits.
For more technical details how PennyLane accomplishes this, check out the documentation and these papers: [h], [i].
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