README.md

k_agents

Knowledge agents for lab automation.

Why k-agents

Motivation

Laboratory automation is important for the efficiency of scientific discovery. However, it is hard to transfer laboratory knowledge to AI.

Our solution

• We provide user-friendly interfaces to inject laboratory knowledge into AI.
• The injected knowledge is wrapped into LLM-based knowledge agents.
• Execution agents use the knowledge agents to automate laboratory procedures.

Supported knowledge types

Here we show how the users can inject knowledge into the AI.

Actions that can be done by code

from k_agents.experiment import Experiment
class SomeActionInLab(Experiment):
    def run(self):
        """
        documenation of the experiment
        """
        # do something in the lab
        ...

Complicated experimental procedures

# Experiment 1
## Steps
1. Do experiment A. If failed, go to step 3.
2. Do experiment B. If failed, try again.
3. Do experiment C. If failed, the procedure is failed.

How to analyze experiment proces

class SomeActionInLab(Experiment):
    @visual_inspection("""
    If there is a clear peak in the figure, the experiment is successful.
    Else, the experiment is failed.
    """)
    def function_that_make_plot(self):
        # produce a figure
        return fig

    @text_insepction
    def function_that_produces_a_report(self):
        # produce a report
        report = "The experiment is successful."
        return report

Application to superconducting qubit calibration

The k-agents framework has been applied to calibrate superconducting quantum gates

Indexing experiments

Experiments:

https://github.com/ShuxiangCao/LeeQ/tree/k_agents/leeq/experiments/builtin/basic/calibrations

Procedures:

https://github.com/ShuxiangCao/LeeQ/tree/main/leeq/experiments/procedures

Notebook for calibration (tune-up)

https://github.com/ShuxiangCao/LeeQ/blob/main/notebooks/Agent/SingleQubitTuneUp.ipynb

https://github.com/ShuxiangCao/LeeQ/blob/main/notebooks/Agent/TwoQubitTuneUp.ipynb