Free Quantum Computing For Everyone

I almost shocked to know that, now if you want to play with a small quantum computer, you can do. If it is not a big happy shock, then hear it again. You can do it for completely free!

Yes, when I came to know about it, I was in disbelief. University of Bristol, developed a two-(qu)bit quantum computer. It has decided to give the access to this quantum computer, anybody who is interested in it. Basically University doesn’t sell the quantum computer. Instead it will let you use it over the cloud. How awesome it is?? However to get that access, one should come up with some interesting problem/algorithm.

Hey, don’t worry of you don’t have a killing unique idea. At least you can practice with their simulator for now. In the simulator, you can input some photons and adjust their phases and you will get some statistics at the output. If you are confident about your experiment in the simulator, you can then run the experiment on real quantum computer.  May be one day, who knows, you will be solving a big problem using the basics learned from these quantum computer experiments.

I know nothing much about quantum computers. But having studied the quantum mechanics and recently exploring  about the computers make me really excited about this! If you want to know more details of quantum computer, look at the links below.

 

http://www.bristol.ac.uk/physics/research/quantum/qcloud

http://www.wired.com/2014/05/quantum-computing/

 

Raspberry Pi Spotter

 

What is it?

Rapberry Pi Spotter basically recognizes the features in a given image. It analyses the images taken from a webcam connected to the Raspberry Pi and tells us what is in the image. Please see the video of jetpac in my previous post.

Why it is important?

The importance of this software is that, it does all this image analysis in offline. One might wonder how come the Raspberry Pi with little CPU power can do this. As I have explained in the previous post, Pi does it through its powerful GPU.

I thought it is really cool, if I can implement it on a simple Robotic platform. The robot goes around and keep  informing us what it sees in its surroundings, just like in the video shown by Jetpac.

What did I do ?

So I have constructed a basic robot with an RC car, and connected a webcam to Pi (The construction details and better pics will be detailed in the next post).  Then  I have Installed the webiopi to get the video stream. Then I have implemented the browser interface as shown  in the above pic for the Raspberry Pi.  Now the Robot can be controlled from any device with a browser. I will upload the  demonstration video soon.

How does it work?

The arrow buttons on the interface control the Robot’s movement. In the above, the video is streamed just like the cambot developed by Eric / trouch. The yellow button above the video stream is to implement the deepbelief software.

When we click on this spotter button, webiopi will search for the latest image captured by the webcam and deepbelief will find the features in that image. Then it displays the results in the text box above the spotter button.

How to install it?

In my previous post, I have explained how to install the deepbelief network on Raspberry Pi. You can find the instructions to install the webiopi here.

I have uploaded my code into github. Note, that the code is really very rough at the moment. This code will work, even if one does not have a Robot. In that case the arrow buttons do nothing. However, video feed will be analyzed as in the original video of jetpac.

First, one has to download the files of cambot.py and index.html into a folder, where the deep belief software was installed previously. We also need to configure the settings of motion, so that it will stream the images directly into that folder. I will write detailed instructions with videos when I get more time.

Comments:

Results are not that great.  One reason might be I am not looking at real life objects. Anyway, I am not concerned about the quality of results yet.  Also, my coding skills are very limited. I hope people with better knowledge of python, in particular webiopi and deepbelief can make it work even better. One concern is that my Pi has only 256 MB of RAM. So it usually keep freezing under load. May be 512 MB RAM Pi will give a smoother experience.

Credits:

I would like to thank Pete Warden (CTO of Jetpac) and Toshi Bass (in Webiopi google group) for helping me throughout this project. I am also thankful to guys at stackoverflow for their help with the python doubts.