Quantum Bayesian Networks

April 30, 2019

Leaving artiste-qb.net

Filed under: Uncategorized — rrtucci @ 3:06 am

After working for 5 years at artiste-qb.net, I am leaving that company for personal reasons. I am looking for a new job. I would prefer to have a job doing what I love, quantum computing software and algorithm development, but I am open to other kinds of job offers as well. I am proud to be the almost sole developer of Qubiter, Quantum Fog, and Entanglish, 3 insanely great open source software libraries available at github. I write software so that it will last forever 💍💍💎💎

Write software that will save the world, not eat it 🙃 You can’t save the world and eat it too🍕🌮🍧

Updated on Nov. 30, 2019

April 22, 2019

Multi-Threading and Gradients of Cost Functions in Quantum Computing

Filed under: Uncategorized — rrtucci @ 5:15 pm

I am VERY pleased to announce that last night, Easter Sunday, I uploaded a major addition to the Qubiter repo at GitHub. The relevant code files all start with the word “Stairs” and are located in the adv_applications folder of Qubiter.

What does the new code do? I explain this in detail in a paper that I wrote for the occasion. The paper is entitled

Calculation of the Gradient of a Quantum Cost Function using ‘Threading’. Application of these ‘threaded gradients’ to a Quantum Neural Net inspired by Quantum Bayesian Networks

Click to access threaded_grad.pdf

As usual, I included lots of docstrings explaining the code, and a main() method at the end of each class, illustrating its usage and testing it. I will also write some jupyter notebooks with examples of usage in the next week or so.

So what do I mean by threading?(I and most people use the words threading and multi-threading synonymously) I believe I am one of the first persons to use the word threading in connection with quantum computing. What I mean by it is the strategy of partitioning the qubits in a (gate model) quantum computer into small, disjoint sets (“islands”) that are uncorrelated from each other and run concurrently. The qubits within one of these islands are strongly correlated but qubits from different islands are probabilistically independent. This is an ideal scenario for NISQ (Noisy Intermediate Scale Quantum) devices and HQC (Hybrid Quantum Classical) computing being pursued by Rigetti Inc. and others. It is also a good fit for calculating the gradient of quantum cost functions: Each island, after many shots and final measurements, yields a mean value, and a linear combination of the mean values from all the islands equals the gradient. In an artistic, poetical sense, qc threading reminds me of what is commonly called “digital rain”, especially if one draws quantum circuits with time pointing downwards, like Qubiter does.

April 9, 2019

I’m on my way to Canaan’s Land, I did it my way

Filed under: Uncategorized — rrtucci @ 3:08 am

The grand challenge: Minimizing Cost functions obtained from parameterized quantum circuits. It’s not obvious what is the best way to do this, but doing this efficiently is an absolute necessity for the Hybrid Quantum Classical computing Programme of Rigetti and others to succeed, I think.

After weeks of deliberation, this weekend I finally decided on my own plan to meet this challenge. I am coding it as we speak. I’m lucky that I am the main author of the quantum simulator Qubiter, because it provides me a lot of polished tools that I know inside out, and that are necessary or extremely useful to meet this challenge. It’s a big head start on others who might embark on the same quest and would try to write their own Qubiter-like tools first. I am also fortunate that my friend Dr. Tao Yin is going to help me.

My idea looks very promising to me, but as Richard Feynman once warned in one of my favorite quotes of his, human beings are very good at fooling themselves, the easiest person to fool is yourself. So even though it looks great to me now, my algo might turn out to be a dud. Still, it will be a lot of fun to test its worth.

On previous blog posts, I have commented on the software PennyLane, which is attempting to meet this challenge. Tonight, during my daily visit to arXiv, I noticed two papers, one that came out today (https://arxiv.org/abs/1904.03206), and another that came out on Mar 28 (https://arxiv.org/abs/1903.12166) that attempt to meet this grand challenge too, by using a tomographic approach. PennyLane and this tomographic approach are formidable competitors to my approach, which is quite different to theirs. It will be fun to race them against each other, even if mine loses.

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