Quantum Bayesian Networks

February 21, 2019

Qubiter can now do Hybrid Quantum-Classical Computation, kind of

Filed under: Uncategorized — rrtucci @ 11:33 am

Habemus papam…kind of. So here is the scoop. Qubiter can now do Hybrid Quantum-Classical Computation…kind of. It is not yet of the most general kind, but we are getting there. “The journey of a thousand miles begins with one step.” (a saying attributed to Chinese philosopher Laozi, 600 BC)

The most general, what the Brits would call The Full Monty, would be if Qubiter could
(1) use distributed computing and back-propagation supplied by TensorFlow, PyTorch, and

(2) run a hybrid quantum-classical simulation on a physical hardware backend such as those already available to the public via the cloud, thanks to the companies IBM and Rigetti.

At this point, Qubiter cannot do either (1) or (2). Instead of (1), it currently does undistributed computing executed by the Python function scipy.optimize.minimize. Instead of (2), it uses Qubiter’s own built-in simulator as a backend.

Amazingly, the wonderful open-source software Pennylane by Xanadu already does (1) and (2). So far, they are the only ones that have accomplished this amazing feat. None of the big 3: Google Cirq, IBM Qiskit, and Rigetti Pyquil can do (1) yet either so we are in good company. I am sure that eventually, the big 3 will succeed in coaxing their own software stacks to do (1) and (2) too. But probably not for a while because large companies often suffer from infighting between too many generals, so they tend to move more slowly than small ones. They also almost always shamelessly copy the good ideas of the smaller companies.

I too want to eventually add features (1) and (2) to Qubiter, but, for today, I am happy with what I already have. Here is a jupyter notebook explaining in more detail what Qubiter can do currently in the area of hybrid-quantum classical computation

https://github.com/artiste-qb-net/qubiter/blob/master/qubiter/jupyter_notebooks/MeanHamilMinimizer_native_scipy.ipynb

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