(paper for fun) An Introduction to quantum computing

Title: An Introduction to Quantum Computing.
Author: Norson S. Yanofsky

It introduces a taste of quantum computing targeting for computer science undergraduate and even advanced high school student.

Hilbert space: regular vector space except each axis is a complex number.

One of key points in Quantum Computing.
: Quantum can be existed in SEVERAL states AT THE SAME TIME.
: when quantum is measured, it is aggregated either 0 or 1. (in case of 2 bit quantum computer)

(Paper)Information-Theoretic definition of Similarity

Title: Information-Theoretic Definition of Similarity.
Conference: ICML 1998

Paper provides a general similarity measure applicable across many domains.
Previous similarity measure is specific for each domain.

Report for 3/4

What I did:
Collected over 60 UCI data set.
Currently, 105 UCI data set is available. However, about 70 data sets are applicable to classcification.

Very fortunately, I think, that I've found some interesting patterns for 3 or 4 meta features.

The problem I got:
For some data set, MLP ate too much time. Even I used three computers, I haven't got MLP results yet for 6-7 data sets.

What I will do next time.
There are many things to be done.
1. Cluster data set based on the vector of accuracies.
2. Examine meta features per cluster

Report for 2/19

The things I did:
I implemented 16 meta-attributes and some more from STARLOG and METAL project.
I found some bugs in my code and took times to figure out.
I will report the preliminary result ASAP this week.

The problem I got:
I was busy with some odd works for last weekend. It ate lots of my time. so I couldn't follow my original research schedule. I learned that I have to save my reserach time with any costs.

My pre-coded meta-attributes functions are spread out through diverse projects.
and it took time to integrate all of them into one project.

The thing I plan to do:
Get some preliminary positive result ASAP.(no later than next report)

Report for 2/13

** The thing I did this week:

1. Additional result on 5 attributes data comparison
data set: 100 5-attribute random data.
Size of data instances: about 4950(=all possible pairs out of 100)
1.1
Training accruacy from ID3
Similarity accuracy: 91.0303%(C4.5),90.9293%(RandomCommittee),89.63%(SVM),89.5758%(MLP)

1.2
Training accuracy from MLP
Similarity accuracy: 89.4545(C4.5), 89.798%(RandomCommittee), 90.1212%(SVM),88.5859%(MLP), 89.9394%(Bagging)

2. I implemented deterministic Q-learning algorithm as a starting point for future research.

3. Reading
Kate A. Smith-miles(Cross-Disciplinary Perspectives On Meta-Learning For Algorithm Selection): Good paper to review diverse disciplines regarding to best algorithm selection for various problem domains.
Question: how our research goal is different from landmarking. According to her paper, landmarking is preidicting the peformance of one algorithm based upon the performance of cheaper and effective algorithm.

** Problem I confronted.
Generating random data with broad accuracies is hard.
When I generated 100 5-attribute data set, the accuracy range is only between 2.24% and 24.20 %

** Plan for next week.

1. experiment with more data set having different # of attribute, experiment with different algorithm.

2. Read 5 papers (Q-Decompsiiont paper in ICML 2003, Task decomposition in IEEE 1997, Recognizing Enviromental Change, IEEE 1999, Environmental adoption IEEE 1999) and do summary.

3. extend deterministic Q-learning into 'non-deterministic' Q-learning

Report for 1/30

1. What you have done
Steve implemented random-arff-file-generator for me.

With that code, I generated twenty 4-attribute arff files.
I genearted one file consisting of 180 data instances where each instance contains entropy,infomation gain,kai-square, and difference(DT accuracy) in a pair arff files.

I obtained MLP 64.7368%, SVM 42.1053 %, DT 57.3684%

2. What problems you have encountered
For data similairty, I don't have problems. Just lack of accuracy.

3. Next week plan.
During this week, I plan to implement standard Q-learning as the start and refine the idea.

Conference Information

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