I normally do up these small and quick projects to help practice technologies I work with. Also to keep me a bit sane.
For this fun little project I thought about creating a chatbot that can translate a simple conversation into a format that can be understood by a quantum computer.
The plan is to build a Grover Algorithm circuit that will determine the best combination of people who like/dislike each other.
The architecture is as follows:
Breaking down each component.
- IPad App (Swift): Why? Because Javascript annoys me. 🙂 Actually creating apps is very easy and swift is a lovely language. If you haven’t coded in it and want to, I recommend the App Brewery training.
- Orchestration Layer (Python/Flask): My focus was on speed and python has all the modules to easily interact with everything else. Perfect for backend demo.
- Watson Assistant: This is to handle the human interaction. Also to pull out the logical components and actors mentioned in the conversation.
- Equation Generator: When the user asks to solve the problem, it translates the Watson Assistant results to an equation that Qiskit can run on.
- Quantum Engine: This is just a helper class I created to build and run the quantum circuit, and then hand the results off to the reporting NLP. Of course what comes back is all 1’s and 0’s.
- Reporting NLP: This takes the result of the quantum computer and converts it into meaningful report to the human. This is then handed back to the iPad App to render.
All this was built and running in a day. It’s not because I’m awesome 😉 but that the technology has moved forward so much that much of the heavy lifting is handled for you.
I’m not going to release the code (if you want some code, why not try pong? I wrote over the weekend). I will go over some of the annoyances that might help others. But first a demo.
Watson Assistant
This was the easiest and most trivial to set up. Just three intents and one entity. The intents detect if two people should be considered friendly or unfriendly. The names of the two people is picked up by the entities. The last intent just triggers the solve process.
Equation Generator
This is a lot less exciting than it sounds. When sending a formula to qiskit it needs to take it in a format like so:
((A ^ B) & (C & D) & ~(D & A))Which is something like “Bob hates Jane, Mike likes Anna, Mike and Bob don’t get on” in normal human speech.
Each single letter has to equate to each person mentioned. So those have to be kept track of as well as the relationships to build this.
Quantum Computing
So Qiskit literally holds your hand for most of this. It’s a fun API. If you want to start off learning Quantum Computing I strongly recommend “Learn Quantum Computing with Python and IBM Quantum Experience“. It focuses from a developer perspective, making it easier to start working through the math later.
To show how simple it is, Qiskit has a helper class called an Oracle. This is literally all the code to build and run the circuit.
# example expression
expression = '((A ^ B) & (C & D) & ~(D & C))'
oracle = LogicalExpressionOracle(expression)
quantum_circuit = oracle.construct_circuit()
quantum_instance = QuantumInstance(BasicAer.get_backend(quantum_computer), shots=2048)
grover = Grover(oracle)
result = grover.run(quantum_instance)What you get back is mostly 1’s and 0’s. You can also generate graphs from the helper class, but they tend to be more for the Quantum Engineer.
Reporting
I used the report generated by Qiskit. But as the results are all 0/1 and backwards. I translate them out to the ABCD… and then added a legend to the report. That was all straight forward.
The tricky bit came in sending the image back to the iPad app. To do this I converted the image to base64 like so (Using OpenCV):
def imageToBase64(img):
b = base64.b64encode(cv2.imencode('.png', img)[1]).decode()
return f'{b}'On the Swift side of things you can convert the base64 string back to an image like so.
func base64ToImage(_ base64Text: String) -> UIImage {
let imageData = Data(base64Encoded: base64Text)
let image = UIImage(data: imageData!)
return image!
}Getting it to render the image in a UIViewTable was messy. I ended up creating a custom UIViewTableCell. This also allowed to make it feel more chat-botty.
When I get around to cleaning up the code I’ll release and link here.
In closing…
While this was a fun distraction, it’s unlikely to be anything beyond a simple demo. There already exists complex decision optimization that can handle human interaction quite well.
But the field of quantum computing is changing rapidly. So it’s good to get on it early. 🙂