The problem has to do with sampling, random numbers and probability distributions, so it is of interest to our community. As Scott Aaronson describes it in his blog, here is the problem:

You can read more here, including answers to the following questions:

- But if the quantum computer is just executing some random garbage circuit, whose only purpose is to be hard to simulate classically, then who cares? Isn’t this a big overhyped nothingburger?
- If Google has indeed achieved quantum supremacy, does that mean that now “no code is uncrackable”, as Democratic presidential candidate Andrew Yang recently tweeted?
- If quantum supremacy calculations just involve sampling from probability distributions, how do you check that they were done correctly?
- Is there a mathematical proof that no fast classical algorithm could possibly spoof the results of a sampling-based quantum supremacy experiment?
- Does sampling-based quantum supremacy have any applications in itself?
- If the quantum supremacy experiments are just generating random bits, isn’t that uninteresting? Isn’t it trivial to convert qubits into random bits, just by measuring them?

This topic was also covered on Quora, here. The leaked NASA document with all the details, can be found here.

© 2020 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central