Auto Quantum Circuits

🔘 Laboratory page: github.com/pinballsurgeon/deluxo_adjacency/blob/main/auto_circuits_humongous.ipynb

Summary

«AutoQML, self-assembling circuits, hyper-parameterized Quantum ML platform, using cirq, tensorflow and tfq. Trillions of possible qubit registries, gate combinations and moment sequences, ready to be adapted into your ML flow. Here I demonstrate climatechange, jameswebbspacetelescope and microbiology vision applications… [Thus far, a circuit with 16-Qubits and a gate sequence of [ YY ] – [ XX ] – [CNOT] has performed the best, per my blend of metrics…]».

Dan Ehlers. _{^{[linkedin.com/posts/dan-ehlers-32953444_cirq-tensorflow-tfq-activity-6960956732453924864-OM8m?utm_source=linkedin_share&utm_medium=member_desktop_web]}}

Process –

1. Choose vision dataset (James Webb, Bacteria Gram Stains, Wild Fires, or MNIST).
2. Define qubit grid range (ig. 1-5 for free tier colab, 36 total qubits).
3. Define number of experiements you want auto designed and ran.
4. Define range of gate combinations (ig. a range of [3-5] would produce random combination of 3, 4 or 5 gates defined in the next step ).
5. Define types of possible gate (ig. XX, YY, CNOT, ISWAP ect.).
6. Define Tensorflow epoch, batch size, learnign rate, optimzier. loss and metrics ect.
7. Enjoy and test you quantum ciruit, one which may yet to have ever existed.

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Author

Dan Ehlers | github.com/pinballsurgeon |

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