Supposedly one of the really big, important things could do with a quantum computer (QC) is quickly solve to optimality instances of NP-complete optimization problems, e.g., problems in scheduling, resource allocation, logistics, etc. which can be formulated as linear programming problems (that need just knowledge of linear equations) where want all the variables to have whole number values, that is integer linear programming (ILP).

Okay, integer linear programming problems .... To get all excited about quantum computing (QC), need to get excited by the big money to be saved by solving all those important, practical ILP problems.

Okay, I had a good background in pure/applied math and in computing and got into ILP for scheduling the fleet at FedEx. Since the promised stock was 1+ years late, I ran off and got a Ph.D., in one of the best programs, in more in hopefully useful pure/applied math, and much of that work was in ILP.

Here is some blunt truth about the NP-complete problems and the cartoon at the beginning of the famous book by Garey and Johnson: The math guys were talking to their manager explaining that they couldn't solve the manager's problem but neither could some long line of other math guys.

Here the blunt part is the meaning of "solve" -- with a computer program running in time only a polynomial in the size of the problem get an optimal solution to any instance of the problem including the worst cases. And here optimal means down to the last penny to be saved. So, for some network deployment by AT&T that was to cost $1 billion, save down to the last penny, in polynomial time, including for the worst case instance of the problem.

Yup, maybe the savings would be $51,937,228.21. And do want to save that last penny. But if the manager would settle for saving just the first $51,900,000.00 in reasonable computer time for all or nearly all the actual instances of the manager's real problem, then there would be little or no difficulty. And should be able to tell the manager that savings of more than $55 million, or some such, were impossible -- that is, have an upper bound.

So, much of the difficulty was saving the last $37,228.21, guaranteeing to do so, for all instances of the problem, including the worst cases.

Well, I can assure readers that should I have insisted on a career saving, e.g., $51,900,000.00 where savings of $55 million were impossible, then I would have spent the last several decades homeless on the streets or dead from homeless on the streets -- no joke.

Bluntly, there just is no significant demand for solving ILP problems in practice. The "managers" don't want to get involved.

Selling pizzas from the back of a truck? Sure -- might sell 100 pizzas a day. Selling solutions to ILP and other NP-complete problems -- f'get about it.

Uh, since there is no significant demand for saving $51,900,000.00 with a bound of $55 million, there stands to be not significantly more demand for saving $51,937,228.21.

Thus, there stands to be no significant value for QC for solving NP-complete ILP problems. Sorry 'bout that. If some people want to get the $51,900,000.00 savings, they've been able to do that for decades and have voted loud and clear "We don't care.".

E.g., in one of my attempts, a guy sent me an ILP problem, we talked, and two weeks later I had running code that in 900 seconds on a slow computer got a feasible solution guaranteed to be within 0.025% of optimality. The problem had 600,000 variables and 40,000 constraints. I had done the work for free. Still, then, suddenly he was not interested.

So be it.

There was another one: I was writing the code using the idea of a strongly feasible basis, and suddenly the customer was not interested and returned to some not very good heuristic code he had.

Better, a lot better, to sell something a lot of people actually want, e.g., a lot better to sell pizza.

And I am doing a startup that to me continues to look good, software running, but it has nothing to do with NP-complete or ILP and wouldn't be helped by QC.

So, to me, e.g., even if Google gets a good QC that can solve ILP problems, then I don't believe that they will have many customers or much of a business and there will be no big reason for IBM or Microsoft to worry.

Since there is no significant demand for using ILP to save money now, I don't see a significant demand for using QC on ILP to save money in the future.

Their employees might be better off selling pizzas. Let's see: From some of my arithmetic about costs of pizza, can do well for $2-3 a pizza. From a pizza truck in a good location might be able to sell the pizzas for an average of $10 each, e.g., an extra $1 for anchovies! Might sell 100 pizzas a day for $1000 a day, maybe 20 days a month. Looks like a better career than QC research!

If there is no demand for pizzas, then there won't be much demand for pizzas with anchovies.

Uh, the Google QC researchers are well paid? Terrific -- park the pizza truck near the Google QC research building!!!!

For some parts of US national security, the situation for a good QC might be significantly different -- I doubt it, but maybe.