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Kyle Monson
Works at Knock Twice
Attended Brigham Young University
Lives in New York City
2,343 followers|99,807 views


No better validation for qCraft than a thank-you note from a dad for teaching his 9-year-old about quantum physics.
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I want a FitBit-like tracker that tallies up the positive vs. negative things I say every day. Sentiment analysis + quantified self. 
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Kyle Monson

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Very excited, just signed the lease for our new Knock Twice office... top floor of the Museum of Sex building. Exit through the gift shop!
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10 years ago tonight, Corinne and I drove into NYC for the first time. We had nothing but a beat-up car, a month’s worth of cash, an awesome (but unpaid) internship, and a cheap motel room in Parsippany, NJ from which we could conduct a hasty apartment hunt. 

Each of us has now lived in NYC longer than we’ve lived anywhere else, and every day we still find new reasons to love and hate it. It’s the only place we’ve lived as adults, and together, we’ve worked at seven jobs, lived in six apartments, started five blogs, played in four bands, belonged to three church congregations, birthed two kids, and been helped out of a taxi by Alec Baldwin once. 

Happy 10 years, NYC.  
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Kyle Monson

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I see... I guess that makes sense. I've never really gotten the hang of sarcasm and rhetorical questions, which is weird because I'm at the top of my English class.
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Kyle Monson

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The compilation album from the Academy of Electronic Music ( #AEM ) is now available in iTunes, Spotify, and Google Play! 

These 7 artists and tracks were selected over thousands of others by +Armada Music, given a month of intense training, and this album is the result of that work. 

The tracks sound great! 

Join the Academy of Electronic Music Community on Google+ to get your track ready for next year's program. 
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Kyle Monson

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Hey #makercamp, get in here and pick a few of your favorite places for us to visit this summer! 
Hey Campers! We're gearing up for this summer's Maker Camp and we want to know what Friday Field Trips you'd like to see. Visit: to fill out the survey and let us know your picks!
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Hey +Colin Crook Great idea!  Sorry for the response delay :)  I see +Bryan Kimbell is all over it :)
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Kyle Monson

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Don't know that I've ever seen footnotes and figures in a social media post before. Very nice! 
Where do we stand on benchmarking the D-Wave 2?

Since there’s a fresh round of discussion on the performance of the D-Wave 2 processor, we thought it might be helpful to give an interim report on where we stand with benchmarking.

Let’s quickly review how the chip is programmed. See Figure 1 in the slideshow. You program the connection strengths between variables represented by the qubits to define what mathematicians call a quadratic optimization problem. Then you ask the machine to return the optimal solution.

Since there’s an astronomical number of different problem instances you could program the chip to solve, it’s impossible to check the performance on all of them -- you need to look at subsets. So what are good sets of instances to study? If you don’t really know where to start looking, you might as well just pick instances at random, measure relative performance, and see what happens.

But a more pragmatic approach is to study problems that arise in practical applications. At this stage we’re mostly interested in answering the question: Can we find a set of problems where the hardware outperforms the best known algorithms running on classical hardware? Since quantum optimization processors are still in rapid evolution, we’re less interested in the absolute runtimes; rather, we want to see how the scaling of the runtime increases as the number of variables increases. 

The hardware outperforms off-the-shelf solvers by a large margin

In an early test we dialed up random instances and pitted the machine against popular of-the-shelf solvers -- Tabu Search, Akmaxsat and CPLEX. At 509 qubits, the machine is about 35,500 times (!) faster than the best of these solvers. (You may have heard about a 3,600-fold speedup earlier, but that was on an older chip with only 439 qubits.[1] We got both numbers using the same protocol.[2])

While this is an interesting baseline, these competitors are general-purpose solvers. You can create much tougher classical competition by writing highly optimized code that accounts for the sparse connectivity structure of the current D-Wave chip. 

Two world-class teams have done that. One is a team at ETH Zurich led by Matthias Troyer, considered to be one of the world’s strongest computational physicists. With help from Nvidia, his team managed to write classical simulated annealing code running on GPUs that achieves an incredible 200 spin updates per nanosecond. The other tailor-made classical competitor was written by Alex Selby. You may recall he won £1 million for cracking the Eternity puzzle. 

Alex devised a smart large-neighborhood search that improves subsets of the 509-variable string while keeping the complement constant. The trick is to use only subsets that lie on tree structured graphs. These tree structured neighborhoods can be searched over in linear time using dynamic programming techniques. Because of the sparse connectivity, these neighborhoods can be very large -- up to 80% of all variables. This makes this solver very powerful.[3]

Both authors were kind enough to share the code with our team. In fact, Matthias’s postdoc Sergei Isakov wrote the fast annealing codes and is now a member of our group.

A portfolio of custom solvers designed to beat the hardware on its own turf is competitive

So what do we get if we pit the hardware against these solvers designed to compete with the D-Wave hardware on its own turf? The following pattern emerges: For each solver, there are problems for which the classical solver wins or at least achieves similar performance. But the inverse is also true. For each classical solver, there are problems for which the hardware does much better.

For example, if you use random problems as a benchmark, the fast simulated annealers take about the same time as the hardware. See Figure 2 in the slideshow.

But importantly, if you move to problems with structure, then the hardware does much better. See Figure 3. This example is intriguing from a physics perspective, since it suggests co-tunneling is helping the hardware figure out that the spins in each unit cell have to to be flipped as a block to see a lower energy state.  

But if we form a portfolio of the classical solvers and keep the best solution across all of them, then this portfolio is still competitive with the current version of the hardware. Again, a good example is the structured problem in Figure 3 in the slideshow. It slows down the annealers, but Alex Selby’s code has no problem with it and obtains the solution about as fast as the hardware does.[4] 

Sparse connectivity is a major limitation

A principal reason the portfolio solver is still competitive right now is actually rather mundane -- the qubits in the current chip are still only sparsely connected. As the connectivity in future versions of quantum annealing processors gets denser, approaches such as Alex Selby’s will be much less effective.

One indication that sparse connectivity is a culprit also comes from well-understood examples such as the “Hamming weight function with a barrier” problem -- quantum annealing tackles it easily but classical annealing fails.[5] But we haven’t been able to implement such examples as benchmark problems yet because of the sparse connectivity.

There’s a list of other hardware aspects still limiting performance that future iterations will need to improve -- reduced control errors, longer coherence times, error correction, richer non-stoquastic couplings between qubits, etc.

A big data approach may lead to new conclusions

So will we have to wait for the next generation chip with higher connectivity before we can hope to see the hardware outperform the portfolio solver? Until very recently we thought so. But remember that these latest benchmarking results were obtained from relatively small datasets -- just 1000 instances in the ones that got recent attention. 

It’s easy to make premature conclusions on such small sets, as there are not enough data points from possible subsets of problem instances that might indicate a speedup. Moreover, as several groups independently discovered, such random problems tend to be too easy and don’t challenge the quantum hardware or classical solvers.[6] 

Ever since the D-Wave 2 machine became operational at NASA Ames,  the head of our benchmarking efforts, Sergio Boixo, made sure we used every second of machine time to take data from running optimization problems. Simultaneously we gave the same problems to a portfolio of the best classical solvers we’re aware of. We now have data for 400,000 problem instances. This is the largest set collected to date, and it keeps growing. 

Eyeballing this treasure trove of data, we’re now trying to identify a class of problems for which the current quantum hardware might outperform all known classical solvers. But it will take us a bit of time to publish firm conclusions, because as Rønnow et al’s recent work shows, you have to carefully exclude a number of factors that can mask or fake a speedup.

So stay tuned!

1. Engineers from IBM, the maker of CPLEX reported that they tuned the CPLEX parameters to perform better on this task but the performance was still several hundred times slower than the hardware. See the paper here:

2.  McGeoch and Wang 2013:

3. For a detailed description of Alex Seby’s code see here:

4. You can also enhance the simulated classical annealers with so-called cluster updates. But if you customize the annealers to be faster for structured problems, they’ll be slower on the random instances.

5. The problem we refer to is discussed in section III B of a paper by Ben Reichardt ( It is a variant of a problem originally proposed by Edward Farhi.

6. Figure 5 in a reference by Katzgraber and Hamze ( illustrates why this is the case. Random problems tend to have a global minimum that can be reached without having to traverse high energy barriers.

#quantumcomputing   #quantum   #quantumphysics   #quantumcomputer  
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How many women can you name who have both a supercomputer and a U.S Navy destroyer named after them? Grace Hopper—who we’re celebrating with a doodle today in the U.S.—is one. “Amazing Grace”’s contributions to computer science made her a pioneer in the field. She created the first compiler for a programming language and led the development of COBOL, the first modern programming language. Happy 107th birthday to Grace Hopper!
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Kyle Monson

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This is going to be a fun one... and follow +Imagine Science Films if you don't already, they're posting some of the coolest science stuff I've seen anywhere. 
Want to create cheese from the bacteria on your feet? Interested in making games with slime mold? Do-It-Yourself biology labs support many unique projects such as these around the world. The Do-It-Yourself Biology movement emerged out of public interest to pursue biotechnology. Without a PhD or access to an institution, it is very difficult to practice biotechnology. Genspace, a DIYbio lab in Brooklyn, New York, will introduce you to DIYbio and teach you how to isolate your own DNA with household ingredients. Time will be allocated for the instructor to answer student questions. Prepared questions are encouraged. Activity can be adjusted for classrooms of all ages.

1. Learn about Do-It-Youself Biology – What is Genspace?
2. Connect biotechnology to everyday curiosity
3. Isolate your own DNA using household ingredients 

1. SDS Detergent 
2. Table Salt
3. isopropanol alcohol
4. 15 ML test tubes

1. Science is fun and accessible!
2. DNA is the biological barcode for life. 
3. Human cells can be broken down with detergent due to their lipid bilayers.

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Content Strategist and Chief Creative
  • Knock Twice
    Partner/Owner, 2012 - present
  • JWT
    Editor and Content Strategy Director, 2009 - 2012
  • PC Magazine
    Senior Editor, 2004 - 2009
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New York City
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Equal parts journalist, adman, and startup founder. I run the NYC office of Knock Twice. I also play in some bands.
  • Brigham Young University
    Comms - Print Journalism
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Kyle Monson's +1's are the things they like, agree with, or want to recommend.

FEATURES:- Enjoy magazine style layout in the new tablet version - Automatically share photos to an Event with Party Mode - Video chat with

The Verge

The Verge is the ultimate source for gadget and technology news and reviews. The site launches Fall 2011. Don't miss it!

Apple - iPad mini - Every inch an iPad.

A beautiful screen, powerful A5 chip, FaceTime and iSight cameras, amazing apps, and 10-hour battery life, make iPad mini every inch an iPad

VetNet HQ

A career service for those who've served

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There are many things about my youth that have been ground into fine dust by a relentless online culture determined to use every emotion I'v

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