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How (Not) To Use Today’s Noisy Intermediate-Scale Quantum Computers
How to approach IBM’s Quantum Open Science Prize
Do you want to get started with Quantum Machine Learning? Have a look at Hands-On Quantum Machine Learning With Python.
Once you get into quantum computing, you inevitably hear about the noisy intermediate-scale quantum devices (NISQ) we have today. But those who point out these limitations rarely give helpful advice.
I am guilty as charged, too!
In a previous post, I explained what NISQ means. My conclusion that these devices require different algorithms, tools, and strategies wasn’t wrong. Yet, I didn’t provide much practical advice either.
Of course, how to cope with quantum errors has been on my timetable for quite a while already. As a result, I collected a lot of unsorted “I gotta read this” resources on that topic. Yet, quantum algorithms can be pretty unfathomable in their own right. It’s hard enough to get through them in any way. So, I never felt the urge to renounce the amenities of a perfect error-free simulator — until now.
IBM announced its second Quantum Open Science Prize. They ask for a solution to a quantum simulation problem. They want us to simulate a Heisenberg model Hamiltonian for a three-particle…
