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Google’s claim on “quantum supremacy”

Last month, Google’s researchers submitted a paper on a Nasa website which claimed that their processor was able to perform a calculation in three minutes and 20 seconds that would take today’s most advanced classical computer, known as Summit, approximately 10,000 years. The researchers mentioned this meant “quantum supremacy”. This paper had been saved by some readers before it was removed shortly afterwards.

About Quantum Supremacy

“Quantum supremacy” is a term popularised by Caltech professor John Preskill with regard to quantum computational capability. It is interpreted as a milestone when a quantum computer is able to solve formerly impossible mathematical calculations for classical computers. According to professor Preskill, the term is aimed to show the concept of a quantum computational advantage, which dates back to 1980s.

Will quantum supremacy threat cryptography?

Regardless of the truth of Google’s paper, such a dramatic speed-up over classical algorithm does have the potential to break cryptography. However, quantum supremacy alone will not threat cryptography for the two main factors: 1. The unlikely recent application of quantum computers 2. The development of quantum-resist cryptography tools.

“Google’s quantum breakthrough is for a primitive type of quantum computing that is nowhere near breaking cryptography,” said bitcoin core developer Peter Todd. “We still don’t even know if it’s possible to scale quantum computers.” The term “quantum supremacy” has indeed raised an unreasonable expectation from people who are not familiar with quantum computing. The practical application of quantum computing still needs time and efforts.

As an example, Ethereum has included quantum-resist cryptography toolsin its 2.0 roadmap. The resistance from quantum-safe cryptography tools seems to be able to come earlier than the presence of quantum computing.

To sum up, For the time being, we can briefly say that it is not likely that quantum computing truly threats cryptography in recent future.

 

Notes:

This blog is aimed to explain Quantum Supremacy and its impact in an easily understandable manner. The author does not have a background in quantum computing or cryptography studies.

 

Reference:

Feynman, Richard P. (1982-06-01). “Simulating Physics with Computers”. International Journal of Theoretical Physics. 21 (6–7): 467–488. Bibcode:1982IJTP…21..467F. CiteSeerX 10.1.1.45.9310. doi:10.1007/BF02650179. ISSN 0020-7748.

Kim, C., 2019. What Google’s ‘Quantum Supremacy’ Means for the Future of Cryptocurrency. CoinDesk. Available at: <https://www.coindesk.com/what-googles-quantum-supremacy-means-for-the-future-of-cryptocurrency> [Accessed 14 Oct. 2019].

Murgia, M. and Waters, R., 2019. Google claims to have reached quantum supremacy. [online] Financial Times. Available at: <https://www.ft.com/content/b9bb4e54-dbc1-11e9-8f9b-77216ebe1f17> [Accessed 14 Oct. 2019].

 

 

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Intro

Recommendation systems are widely applied in a variety of domains in our life, such as entertainment, e-commerce and social media. By screening massive amounts of data, the system delivers value and convenience to users. Yet, have you ever had such an experience when your recommender keeps pushing ads of very similar products, or the songs in your playlist are in a similar musical style which you might have been fed up with?

More than user experience

This is not just concerned with user experience, but also with economic effect and development. As one of the side effects, popularity bias is a common type of recommendation bias. It has been observed that under some conditions popular items are more likely to be recommended leading to a rich get richer effect, exacerbating the unequal distribution of social resources. Likewise, “information cocoons” is another type of bias that may happen due to the closed-loop attribute of recommendation algorithm.

Solutions?

Combination of multiple recommendation strategies seems to be a possible solution. Take Spotify as an example. As a music streaming service provider, its recommendation system Discover Weekly and algorithm are well designed, giving Spotify an advantage over other competitors. The system is able to recommend songs that suit your taste but sound different. It is also able to avoid popularity bias and bring attention to new songs or niche musical works. Instead of simply relying on one recommendation strategy (or model), the combination of several recommendation strategies (or models) plays an important role in the success. Here are the three types of models that Spotify employs:

1.Collaborative Filtering models, which analyse both your behaviour and others’ behaviours

2.Natural Language Processing (NLP) models, which analyse text

3.Audio models, which analyse the raw audio tracks themselves

Another possible solution is to add a post-processing algorithm to adjust and re-rank the set of the recommendations. For post-processing, the key point is to eliminate bias but meantime keep the utility of recommendations as high as possible.

 

References:

Abdollahpouri, H., Burke, R. and Mobasher, B., 2019. Managing Popularity Bias in Recommender Systems with Personalized Re-ranking. arXiv:1901.07555 [cs]. [online] Available at: <http://arxiv.org/abs/1901.07555> [Accessed 23 Sep. 2019].

Ciocca, S., 2018. How Does Spotify Know You So Well?[online] Medium. Available at: <https://medium.com/s/story/spotifys-discover-weekly-how-machine-learning-finds-your-new-music-19a41ab76efe> [Accessed 20 Sep. 2019].

Farnadi, G., Kouki, P., Thompson, S.K., Srinivasan, S. and Getoor, L., 2018. A Fairness-aware Hybrid Recommender System. arXiv:1809.09030 [cs, stat]. [online] Available at: <http://arxiv.org/abs/1809.09030> [Accessed 23 Sep. 2019].

Tsintzou, V., Pitoura, E. and Tsaparas, P., 2018. Bias Disparity in Recommendation Systems. arXiv:1811.01461 [cs]. [online] Available at: <http://arxiv.org/abs/1811.01461> [Accessed 20 Sep. 2019].