Author: paschmann

Quantum Algorithms – Complexity Classes Notes

paschmann

Traveling sales person problem

Solve problems which are NP hard – and they can’t be solved in polynomial time.

P versus NP problem: full polynomial versus nondeterministic polynomial problem

A P problem is one that can be solved in “polynomial time,” which means that an algorithm exists for its solution such that the number of steps in the algorithm is bounded by a polynomial function of n, where n corresponds to the length of the input for the problem. Thus, P problems are said to be easy, or tractable. A problem is called NP if its solution can be guessed and verified in polynomial time, and nondeterministic means that no particular rule is followed to make the guess. read more

Do Things That Don’t Scale

paschmann

http://www.paulgraham.com/ds.html#f1n

One of the most common types of advice we give at Y Combinator is to do things that don’t scale. A lot of would-be founders believe that startups either take off or don’t. You build something, make it available, and if you’ve made a better mousetrap, people beat a path to your door as promised. Or they don’t, in which case the market must not exist.

Bookmark: Quantum Links

paschmann

Good practical Python Quantum workbook

https://livebook.manning.com/book/learn-quantum-computing-with-python-and-q-sharp/chapter-1/v-8/

QCodes Github is what a lot of experimenters are using to control machines.

https://github.com/QCoDeS/Qcodes

Quantum Jobs

https://medium.com/qiskit/the-hitchhiking-cats-guide-to-getting-a-job-in-quantum-computing-da7e3bb9ff64

UC Berkley Course – Vazirani

MIT Open Course on Quantum

https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-845-quantum-complexity-theory-fall-2010/lecture-notes/

Qiskit Hello

https://mybinder.org/v2/gh/QISKit/qiskit-tutorial/master?filepath=community/games/Hello_Qiskit.ipynb read more

Bookmarks: Emerging Architectures for Modern Data Infrastructure

paschmann

As an industry, we’ve gotten exceptionally good at building large, complex software systems. We’re now starting to see the rise of massive, complex systems built around data – where the primary business value of the system comes from the analysis of data, rather than the software directly. We’re seeing quick-moving impacts of this trend across the industry, including the emergence of new roles, shifts in customer spending, and the emergence of new startups providing infrastructure and tooling around data.

In fact, many of today’s fastest growing infrastructure startups build products to manage data. These systems enable data-driven decision making (analytic systems) and drive data-powered products, including with machine learning (operational systems). They range from the pipes that carry data, to storage solutions that house data, to SQL engines that analyze data, to dashboards that make data easy to understand – from data science and machine learning libraries, to automated data pipelines, to data catalogs, and beyond. read more