My goal here was to build a quantum deep neural network for classification tasks, but all the effort involved in calculating errors, updating weights, training a model, and so forth turned out to be completely unnecessary. The above circuit is much simpler than it must already look, and I am going to fully break it down for you.
Disclaimer
This circuit is intentionally not optimized. Rather, it is intended to be comprehensible. I intend to address optimization as I add complexity to future circuits, which will have their own associated articles.
Background
This origin of this classification task is a very simple neural network that had been written in Python. Long ago, I rewrote this neural network in C to force me to better understand how it worked. Without the use of NumPy, in particular, I had to write all the functions from scratch (I avoided potentially-helpful C libraries, as well). Armed with this relatively-deep understanding, I selected this same neural network to translate further from C into OpenQASM.
Registers
This circuit uses four registers. The “a” register consists of two ancilla qubits, each paired up with one qubit from the two-qubit “data” register. The “train” register consists of the training data from the original neural network in Python; the data is…
