recently I learned that some quantum compilers use ZX-calculus as an intermediate representation. The diagrammatic rewrite rules allow circuit optimization through graph transformations, which can be more scalable than working directly with matrices. I will share some notes i took on this soon.
#zx
#zx
β€12
I'm still reading this book so I don't have a final thought on it yet. It's really good from what I've seen so far.
If you're looking for a resource for qc, do check it out.
Oh and the author makes music and I think he has a stage in his office too.
#zx
If you're looking for a resource for qc, do check it out.
Oh and the author makes music and I think he has a stage in his office too.
#zx
β€7
Debugging Epohul
he then proceeded to teach for 4 hours straight
he was right. Clifford groups are everywhere and I'm starting to get it.
There was an event today organized by the company Riverlane. I was a bit late, and I was only able to listen to the second panel discussion, where representatives from Nvidia and HPE Quantum were the guests.
Few things I learned were:
Nvidia
Nvidia's approach is to advance accelerated quantum supercomputing, and they are working on bringing AI models to advance quantum computing while also using AI as part of the process. One example I saw was their recent work in decoders using a convolutional neural network coupled with a global decoder, which performed better (I think). Another example mentioned was using AI to find lower-depth circuits, better syndrome extraction, and for reasoning over different architectures... more.
HPE Quantum:
HPE is advancing quantum computing by integrating it with high-performance computing (HPC) and AI, aiming for a hybrid quantum-classical approach that makes the technology practical for enterprise use cases like simulation and optimization. They are working on full-stack integration with a top-down approach, starting from the application side and then working with different quantum vendors that are suitable for the specific application.
QLDPC code?
Few things I learned were:
Nvidia
Nvidia's approach is to advance accelerated quantum supercomputing, and they are working on bringing AI models to advance quantum computing while also using AI as part of the process. One example I saw was their recent work in decoders using a convolutional neural network coupled with a global decoder, which performed better (I think). Another example mentioned was using AI to find lower-depth circuits, better syndrome extraction, and for reasoning over different architectures... more.
HPE Quantum:
HPE is advancing quantum computing by integrating it with high-performance computing (HPC) and AI, aiming for a hybrid quantum-classical approach that makes the technology practical for enterprise use cases like simulation and optimization. They are working on full-stack integration with a top-down approach, starting from the application side and then working with different quantum vendors that are suitable for the specific application.
QLDPC code?
β€10π1