Andrew Milne's Profile

1065
Points

Questions
5

Answers
16

  • Asked on December 21, 2018 in General Finance.

    Quantum Computing is still at a very early stage.  I’m not going to pretend to offer investment advice.  However, here’s something to consider:

    If you search on LinkedIn for “quantum computing”, most of the people you find are in academic or R&D roles.  If you search for a different area of technology that you believe is currently of interest to venture capital, what do you find?

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  • Asked on December 21, 2018 in Quantum Computing.

    There are many architectures being worked on.  One place to get started is with the work of Andrea Morello.  This page at the SFU Physics website has links to some of his papers and those of others, published in major journals.  https://www.sfu.ca/physics/newsevents/phys-events/2017/feb/event10.html

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  • Asked on October 5, 2018 in Quantum Computing.

    One easy way to get started is the IBM Q Experience: https://quantumexperience.ng.bluemix.net/qx/experience

    There are a number of people already doing experiments of various kinds.

    Also, a “live” map showing where the experiments are being done: https://qe-executions-map.mybluemix.net

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  • Asked on June 26, 2018 in Sampling/Monte Carlo.

    An additional paper worth looking at is Physics-inspired optimization for constraint-satisfaction problems using a digital annealer, recently posted on arXiv  by some of my colleagues here at 1QBit and Fujitsu.  Its 65 references are mostly from the physics literature, and may therefore provide a fresh perspective.

    Link to arXiv: https://arxiv.org/pdf/1806.08815.pdf

    The Fujitsu Digital Annealer can treat Ising-type optimization problems of a size up to 1024 variables, with 26 and 16 bits of (fixed) precision for the biases and variable couplers, respectively.  According to the authors, physics-inspired optimization techniques, such as simulated annealing and parallel tempering Monte Carlo, have been shown to  outperform specialized quantum hardware, such as the currently available D-Wave devices.

     

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  • Asked on June 1, 2018 in Sampling/Monte Carlo.

    I am not an expert in Monte Carlo methods.  However, I have re-posted your question on 1QBit’s internal message board, and we’ll see what emerges in the way of helpful information.

    As background, there is a paper from 2014 that compares population annealing (a Monte Carlo algorithm) with simulated annealing and parallel tempering Monte Carlo.   https://arxiv.org/abs/1412.2104

    I am mentioning it because one of the authors is Helmut Katzgraber, who heads the Optimization Team at 1QBit. Helmut has published extensively, and with a large number of co-authors.  One way to begin your search for relevant articles would be to start with the above paper on arXiv and look through the citations for its authors.

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  • Asked on February 5, 2018 in Quantum Computing.

    In addition to the material on this site, there are several 1QBit White Papers that describe concrete applications.  Some of these are similar to the versions published here.

    This answer accepted by Chris6632. on March 19, 2018 Earned 15 points.

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  • Asked on January 10, 2018 in Quantum Computing.

    Synthesis, if I understand your question correctly, involves creating the physical machine from an abstract description.

    There are many ways of implementing quantum gates.  However, Google us good place to look for more information (seriously).  One of their goals of their work in quantum computing is to  have the physical machines built by outside vendors.  Part of this means having a way to specify what they want. 

    If something like VHDL for quantum computers is somewhere in our future, I’d expect Google to be involved.  I’d also expect the various fabricators to have explanations of how they translate the spec into physical hardware.  At the moment, though, there’s not a lot of information available.

    https://futurism.com/google-just-revealed-how-theyll-build-quantum-computers/

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  • Asked on May 25, 2017 in General Finance.

    Interesting chart from the Eris Futures website.  I’m posting it here as an example of how Brexit showed up in other types of market data.  The bid/ask spread on the order book can be read as an expression of intent.  The reported trades used in our clique analysis are an expression of how intent was acted on.  Note how the bid/ask spread narrowed before volume started to recover in the order book.

    RE: What do the edges in the FX pair graph really mean?

    http://www.erisfutures.com/EE/Eris_Liquidity.pdf

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  • Asked on May 5, 2017 in Optimization.

    One easy thing to do is follow 1QBit and other companies on Twitter.  There are some links on this site that you can follow to get connected.

    However, I’ll try to collect some recommendations from my colleagues at 1QBit and  organize them into something you can use.  This isn’t a systematic list, but it will put you on the same pages (literally) as people working in the field.

    Here’s one on Grover’s Algorithm:

    A review paper “Grover’s Algorithm: Quantum Database Search” written by C. Lavor for non-expert.

    There is  also a lecture on youtube on Grover’s search algorithm https://www.youtube.com/watch?v=JCM7M7XfSFg

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  • Asked on May 3, 2017 in Optimization.

    At the present time, the use of quantum computing in large-scale search is relatively limited.

    There have been a lot of very smart people working on search and retrieval problems for a very long time.  Herman Hollerith received his first patent on punched cards back in 1889, and although the meaning of “large” has evolved over time, the need to find records according to criteria has always pushed up against the limits of   technology.  Classical search and retrieval techniques are highly developed, and so are the architectures that people use to organize them.

    There are applications of quantum computing in large scale search.  For example, graph coarsening (e.g. for relationship graphs) can reveal large scale patterns that a user might want to explore .  Feature selection (to associate small scale data points with large scale semantics) can increase the efficiency of storage.  Machine learning could be used to control the action of indexers to make their results more relevant.  At some point, the role of quantum computers in large-scale search architectures may change the way that we think about search and retrieval itself, but this is likely some way off.

    It’s an area worth watching.  It’s possible that someone might try to fit one of these specialty applications into a widely available program.  But at this point I think it would be a labor of love as opposed to a commercial offering.

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