First ever DBM trained using a quantum computer

In Terminator 2, Arnold reveals that his CPU is a neural net processor, a learning computer. Of course it is! What else would it be? Interestingly, there are real neural net processors in the world. D-Wave makes the only superconducting version, but there are other types out there also. Today we’ll use one of our superconducting neural nets to re-run the three experiments we did last time.

I believe this is the first time quantum hardware has been used to train a DBM, although there have been some theoretical investigations.

Embedding into hardware

Recall that the network we were training in the previous post had one visible layer with up to four units, and two hidden layers each with four units. For what follows we’re going to associate each of these units with a specific qubit in a Vesuvius processor. The way we’re going to do this is to use a total of 16 qubits in two unit cells to represent the 12 units in the DBM.

All D-Wave processors can be thought of as hardware neural nets, where the qubits are the neurons and the physical couplers between pairs of qubits are edges between qubits.Specifically you should think of them as a type of Deep Boltzmann Machine (DBM), where specifying the biases and weights in a DBM is exactly like specifying the biases and coupling strengths in a D-Wave processor. As in a DBM, what you get out are samples from a probability distribution, which are the (binary) states of the DBM’s units (both visible and hidden).

In the Vesuvius design, there is an 8×8 tile of eight-qubit unit cells, for a total of 512 ‘neurons’. Each neuron is connected to at most 6 other neurons in Vesuvius. To do the experiments we want to do, we only need two of the 64 unit cells. For the experts out there, we could use the rest to do some interesting tricks to use more of the chip, such as gauge transformations and simple classical parallelism, but for now we’ll just stick to the most basic implementation.