Want to Learn Quantum Computing? Here’s Your Reading List

If you want to future-proof your technology career in finance (among other segments), you probably need to learn about quantum computing. As we’ve noted often before, banks are lining up quantum computing research teams, and JPMorgan already boasts achievements in areas such as quantum cryptography.

In line with that, quantum researchers at both Goldman Sachs and JPMorgan have released new reading materials to help aspiring quantum researchers self-inform about their area. 

Rajiv Krishnakumar joined Goldman Sachs in 2018 after leaving Stanford University with a PhD in applied physics. Krishnakumar helped set up Goldman’s quantum computing research team in 2019, and he’s just posted a blog on Medium with advice on how to get started in quantum computing. 

Krishnakumar’s quantum reading list includes the following: Quantum computing Progress and Prospects (cost: $55); Quantum computing in the Noisy Intermediate-Scale Quantum (NISQ) era and beyond (cost: zero); Quantum Computing and Quantum Information (cost: zero); Introduction to Quantum Algorithms (cost: zero); the Qiskit textbook (cost: zero); Intro to Quantum Machine Learning (a free YouTube video).

Krishnakumar’s intention is to help teach aspiring quantum computing experts the basics. Once you’ve worked through that, you can move on to the latest paper on quantum search released by  Constantin Gonciulea, Austin Gilliam and Marco Pistoia, who are in engineering research at JPMorgan. 

Gonciulea is the JPMorgan technologist who previously achieved a ‘quantum breakthrough’ while working weekends at home during the lockdown. The new paper builds on this by further shortening quantum search times using ‘amplitude amplification,’ partitioning target states into different classes and achieving 100 percent probability when searching for a particular quantum state.

If you’ve read (and understood) all of that, you can revert to something a little less exhausting: Gonciulea also advises reading the Book of Why by Judea Pearl and Dana Mackenzie for insights into causation that will take artificial intelligence beyond mere ‘curve fitting.’

A modified version of this article originally appeared in eFinancialCareers.