Decoding Recommendation Algorithms

Quantum computing promises unprecedented speed, but in practice, it’s proven remarkably difficult to find important questions that quantum machines can solve faster than classical ones. One of the most notable demonstrations of this came from Ewin Tang who rose to prominence in the field as a teenager. When quantum algorithms had in principle cracked the so-called recommendation problem, Tang designed classical algorithms that could match them.

So began the approach of “dequantizing,” in which computer scientists look at quantum algorithms and try to achieve the same speeds with classical counterparts. To understand the ongoing contest between classical and quantum computing, co-host Janna Levin spoke to Tang on The Joy of Why podcast. The wide-ranging conversation covered what it was like for Tang to challenge the prevailing wisdom at such a young age, the role of failure in scientific progress, and whether quantum computing will ultimately fulfill its grand ambitions.