DIY: Ion Trap Quantum Computer

An ion is a atom with one of the outer electrons removed (normally removed by pointing a laser beam at it) – forming positively charged ion.
Ion trapping is done in a vacuum chamber to isolate the ions from the external environment as much as possible. (And avoid other atoms in the air from bumping and

  • Atoms
    • Consist of 3 components, a proton and neutron (called the nucleus) in the center and electrons which orbit this nucleus
    • 117 different types
    • Are elements the same as atoms
    • Every element is unique and has an atomic number
      • That number tells you the number of protons in every atom of the element. The atomic number is also called the proton number.
      • read more

  • UC Berkley – Lecture Notes from Vazirani on Quantum Computing

    Notes: 

    Quantum Systems are exponentially powerful

    Based on particles: 2^500 – More particles in the universe

    Challenges:

    • Not all problems are well suited
    • Need to understand Q Mechanics

    Qubit – simplest quantum system

    Entanglement-

    • Bell Inequalities
    • Teleportation

    Lecture 1: Double Slit Experiment

    • Is light a particle or a wave?
    • If light was a particle, it would be like a bullet, and we would see the two beams overlap each other:
    • Strange that if both holes are open why we get this strange interference pattern.
    • Why did the count drop when both holes were open? From a decent amount, to nearly zero.
    • If we use bullets, we would see a whole number of bullets that get through
    • If we use water & waves, we get the same interference pattern.

    If we add a measuring device just after the slits to track which slit the electron goes through, it “disrupts” the measurement and we get the 2nd pattern. If we use a very slight/dim light enough light, we get the 3rd “expected” pattern, but we also miss a lot of the electrons and may not capture the pattern.

    = Hesienburgs uncertainty principle = Impossible to design apparatus which can detect which slit it went through without disturbing the interference pattern. read more

    Quantum Algorithms – Complexity Classes Notes

    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

    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

    Bookmarks: Emerging Architectures for Modern Data Infrastructure

    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