Back from choking some folks out. Gots ta represent.
OK in this second installment of Geordie’s Easter Weekend Vent-a-thon, I’d like to offer some food for thought related to Scott Aaronson’s latest blog post. Specifically I’d like to take him to task on his rationalization of why he is so crusty, the most recent examples of which are:
1. I asked Geordie Rose (the founder of D-Wave and sometime commenter on this blog) twice for actual information about the speedup D-Wave claimed to be seeing over classical algorithms, and he never provided any.
I don’t remember actually ever receiving this request from Scott, so I apologize for not being timely in my response. For the record, here is what I have said, in some of the posts here, several interviews, and the demo itself: (A) Orion is roughly 100x slower than state of the art algorithms running on a $1000 PC for solution of small Maximum Independent Set problems, and (B) the only way scaling can be extracted is empirically, and we can’t build big enough systems (yet) to answer scaling questions.
2. Instead of answering my and others’ technical questions, D-Wave decided to attack academic science itself. Here’s what Ed Martin, the CEO of D-Wave, told an interviewer from ITworld: “Businesses aren’t too fascinated about the details of quantum mechanics, but academics have their own axes to grind. I can assure you that our VCs look at us a lot closer than the government looks at the academics who win research grants.” The track record of companies that engage in this sort of behavior is not good.
I can’t see how you can argue that we haven’t tried to answer technical questions. I go out of my way to answer any questions that are asked. If you have any let me know, I’ll answer them if I can.
Casting Herb’s (not Ed’s-there’s something ironic there) statements as attacks on academic science is ridiculous. The above statement has been taken WAY out of context. The original question was how our investors do due diligence on the company without any peer reviewed publications, which is a fair question. Herb’s answer–which is a true statement, whether or not you like it–is that financing a company like this puts us under levels of scrutiny far beyond the norm, either for a start-up company or a big research grant. Herb’s statement isn’t an attack on academic science. It’s that the bar we have to get over to raise cash is higher than it is for other start-ups or academic groups.
I don’t know what you mean by that ominous last statement. You mean like Intel, Microsoft, IBM, GE?
3. I read article after article claiming that quantum computers can solve NP-complete problems in polynomial time. I reflected on the fact that a single one of these articles will probably reach more people than every quantum complexity paper ever written.
This is probably true, although only 50 people in the world understand what you just said. And note that I have never said that, and in fact go out of my way to state a point of view that is very similar to Scott’s.
4. It became clear, both from published interviews and from talking to Jason, that D-Wave was doing essentially nothing to correct journalists’ misconceptions about what sorts of problems are believed to be efficiently solvable by quantum computers.
While I personally find questions about efficiently solvable problems fascinating, these issues are remote from what we are actually doing here.
This is worth emphasizing, because I thought it was obvious, but it turns out alot of people don’t get this. Most of the poorly thought out comments related to what we’re trying to do have come from theoretical computer scientists, who assume that the things they hold dear are likewise treasured by everyone else. Because they worship efficiency, they have assumed that’s the objective of our projects, when I have repeatedly said it’s not.
Here is what I care about: (A) getting to the same level of accuracy as state of the art heuristics in less time and/or (B) given an allotment of time, produce a more accurate solution than state of the art heuristics, for high-value discrete optimization problems that people actually care about.
When people ask what the systems we’re building are for, I tell them that they are for solving discrete optimization problems (which they are). For someone for whom complexity theory is the fulcrum of the universe, this might be interpreted as a statement that we plan to exactly solve all discrete optimization problems, efficiently. For most people who have to deal with these types of problems in real life, the questions arising have a slightly different flavor: “Can you beat our simulated annealing approach?”, which seems like a lot more intelligent question.