🔘 Laboratory page: github.com/pinballsurgeon/deluxo_adjacency/blob/main/auto_circuits_humongous.ipynb
«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]
- Choose vision dataset (James Webb, Bacteria Gram Stains, Wild Fires, or MNIST).
- Define qubit grid range (ig. 1-5 for free tier colab, 36 total qubits).
- Define number of experiements you want auto designed and ran.
- 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 ).
- Define types of possible gate (ig. XX, YY, CNOT, ISWAP ect.).
- Define Tensorflow epoch, batch size, learnign rate, optimzier. loss and metrics ect.
- Enjoy and test you quantum ciruit, one which may yet to have ever existed.
Dan Ehlers | github.com/pinballsurgeon |
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