Quantum Bayesian Networks

March 30, 2019

Second New Idea For Doing Back-Propagation On a Quantum Computer

Filed under: Uncategorized — rrtucci @ 4:59 pm

Last weekend, in a blog post entitled “New Idea For Doing Back-Propagation On a Quantum Computer”, I announced that I had uploaded to the Qubiter repo an essay entitled

“Calculating the Gradient of a Cost Function for a Parametric Quantum Circuit in FIVE EASY PIECES”

This weekend, I decided to add to that essay a new, more technical, 5 page appendix that fills some of the gaps left behind in the main part of the essay. So even if you perused the essay before today, you might be interested in re-opening it to take a peek at the new addition.

What I do in the new appendix is to show how to express the gradient of the cost function as a sum of mean values that are readily evaluated empirically on a real qc. So far, the PennyLane software only considers evaluating the gradient of cost functions wherein the parameters being differentiated only occur in one qubit gates with *no* controls. I show in this new appendix how to deal with the case when the parameters being differentiated also occur in gates with any (0, 1, 2, …) number of controls.

The Yellow Brick Road follows the gradient of a quantum cost function. God’s truth. To do back-propagation, “close your eyes and tap your heels together three times. And think to yourself, there’s no place like home.”

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