Hacker News Reader

Comment by brownbat | Hacker News Reader

People often focus on the potential of quantum computing, but generally focus far too little on the potential of quantum sensing.

We revolutionized the precision of timekeeping with atomic clocks. Quantum inertial sensors could precisely track your location with very little drift and no satellite constellation maintenance.

https://www.newscientist.com/article/mg22229694-000-quantum-...

View on HN

Even quantum sensors will need something (i.e., another sensor) to stabilize the vertical channel. The effect of imperfectly compensating the gravity component out of the specific force measurements from the accelerometers leads to a positive feedback loop. Finite precision arithmetic and fundamental algorithm error (solving the Bortz equation) leads to errors that will grow in the horizontal direction as well.

sych a device would still require synchronization, it promises lower drift, but to eliminate drift you will always need to recallibrate to synchronize with reference points (stars, beacons, GPS, ...)

I wonder what kind of improvement you can get by using several of the devices at once? Could some sort of consensus monitoring at least tell you what kind of margin of error you are working with ? I guess for some applications being 100m out is fine (for example an Astute in open ocean), but getting it that wrong in, for example, the Med or the Sound of Skye is probably a problem.

Standard error decreases by the square root of the number of samples. So an order of magnitude improvement would need 100 sensors!

That’s true when averaging together iid samples, but you could probably do something smarter if you know about the likely errors and the sensors’ covariance. For example, if three of the four sensors agree, you should probably completely disregard the fourth, rather than averaging it in.

> you could probably do something smarter

Assuming measurements are independent and errors are Gaussian, the Kalman filter is this smarter thing. Variants exist for nonlinear systems.

https://en.wikipedia.org/wiki/Kalman_filter

Of course, when you decrease the size of the sensor by half in each dimension while maintaining its error rate you've quadrupled the number of sensors. If you can increase the computing power similarly, or decrease the computing requirements for combining those sensor measurements - it quickly becomes feasible to use massive numbers of sensors to improve the standard error significantly - but it is a lot of ifs.

Practical example, you'd only have to shrink the sensor by half 6 or 7 times to fit 100 in the same area as 1.

Bear in mind that any ship operating near shore will be able to get visual or radar fixes from features on the coastline.

Really interesting : I was aware of the 2018 announcement reported in the FT but I have never seen this 2014 article before.

Reply on news.ycombinator.com