To be honest, i am currently a Ph.D. in Electrical Engineering. I was inspired many such “flawed” articles in popular press and decided to pursue quantum computation when i was an undergrad. Being young and naive i thought i could come up with another exponenitally faster quantum alogrithm! In the end i came up with a much simpler result which was published at WorldComp 2009 (where you were one of the keynote speakers) and infact realized the potential limitations of QC to solve a simple image processing problem and decided to move to another field.

It is then I once again came across the “fancy” news articles about Nanoelectronics and I came to USA and spent(loaned) 35,000$ to do my MS at a Top 10 school and realized that i reached a dead-end. No one knows where the research in Nanotube,nanowire or Graphene transistors is going! Looks like the industry will be still using CMOS in the near future.After my MS in so called Nanoelectronics, i did not get even a single job offer!!

Having burnt my fingers, i moved to more traditional ECE fields like Computer Architecure, Parallel Programming and Computational Intelligence during my Ph.D. studies and doing reasonably well.

A lot of people underestimate the harm caused by such frivolous reporting of scientific research.It can seriously mislead the young and naive aspiring researchers who will be the Scientists of tomorrow.

It is very unfortunate that it has become very fashionable to use the buzzwords ‘quantum’ and ‘nano’ very indiscriminately even by some of the scientific community as a means to attract funding and attention!

I always thought Science was an objective pursuit but unfortunately human beings are creatures of emotions rather than logic!

rrtucci’s last message seems to be talking about the effects of noise on the system, saying that continuous/quantum problems are the worst in terms of noise.

Although these are both true, I think they are completely different, and it sounds like you both think you were talking about the same issue.

Besides quantum Bayesian networks and independently of them, I use my blog to advocate for the use of quantum computers to do MCMC (Markov Chain Monte Carlo)

Let me call discrete problems (continuous problems, respectively) those whose set of possible answers is the natural numbers (real numbers, resp.). I believe both quantum and classical computers can produce statistical errors in their answers for both discrete and continuous problems. (Shor’s algorithm, though it solves a discrete problem, has a finite probability of failing, but there is a high probability of success after only a small number of repetitions of the experiment.) In the case of classical computers, we don’t normally worry about statistical errors (unless it’s a Monte Carlo algorithm) because current classical computers make such errors only extremely rarely. (If you operate then in a friendly environment, that is. You must, of course, worry about statistical errors if the environment is unfriendly; for instance, if your classical computer is operating at a very high temperature, or if it’s in outer space and exposed to very high levels of cosmic radiation.) Compared with classical computers, quantum computers have to contend with an additional and hard to handle source of statistical errors, namely quantum mechanical noise. One worries less about statistical errors for discrete problems (because the discreteness tends to filter out some of the noise) than for continuous problems, and less for classical computers than for quantum ones. Hence, you are right in your intuition of singling out as the worse possible scenario for statistical errors, that of solving a continuous problem on a quantum computer.

I’d also be interested in a cross-check regarding whether the statements in my earlier comment are correct regarding the nature of what I termed “continuous-variable quantum algorithms”. I sometimes write comments with the intent of writing at least a rough-draft of tutorial content for readers, and corrections or clarifications in concepts and terminology are more than welcome.

Re. fibs — Yes, erosion of scientific credibility is an enormous cost, and has the characteristics of a tragedy of the commons problem, paired with a public-goods problem: Exaggeration can pay, for the exaggerator, while the cumulative costs are borne by science and society. Thus, trust is a kind of commons that is consumed by exaggerators. For the same reason, the service of combating these assaults on the fabric of trust is a public good, with general benefit but concentrated costs. It parallels the classic examples of military and police protection, structurally, though the appropriate remedies would be quite different.

BTW, it is important to keep in mind that misunderstandings — even those that benefit the ultimate information source — can result simply from mistakes in understanding, or from over-simplification on the part of the people who stand between knowledge sources and knowledge receivers.

For example, assume that the researchers in this instance consistently stated a clear and correct message (as they did, for example, in the abstract of their paper). I would still expect reports to be distorted in the way that we see. When the facts are hard to explain, and are easily misunderstood, and the misunderstanding is easily understood and it makes a better story (all true here), I tend to think less in terms of guilty parties, and more in terms of evolving ideas.