Commercially viable quantum computing could be here sooner than you think, thanks to a new innovation that shrinks quantum tech down onto a chip: a cryochip.

Seeker explains:

It seems like quantum computers will likely be a big part of our computing future—but getting them to do anything super useful has been famously difficult. Lots of new technologies are aiming to get commercially viable quantum computing here just a little bit faster, including one innovation that shrinks quantum technology down onto a chip.

Krysta Svore, principal researcher at Microsoft, demonstrates the new Microsoft Quantum Development Kit.

The Quantum Development Kit makes it easy for you to start experimenting with quantum computing now and includes: · A native, quantum-focused programming language called Q# · Local and Azure-hosted simulators for you to test your Q# solution · And sample Q# code and libraries to help you get started

In this demo, she walks through a few code examples and explains where quantum principles like superposition and entanglement apply. She explains how quantum communication works using teleportation as your first “Hello World” inspired program. And keep watching to see more complex computations with molecular hydrogen.  

Seeker examines a leaked paper from Google claimed that a quantum computer demonstrated “quantum supremacy.”

But what does that mean exactly?

Quantum computers’ potential and the advantages they promise over classical computers all remain largely theoretical, and hypothetically speaking, it is predicted that quantum computers will be able to solve problems that are beyond the reach of the classical computers we use today. Passing such a threshold will be considered proof of what we call “quantum supremacy.”

Siraj Raval wrote a research paper titled “The Neural Qubit” where he describe a quantum machine learning architecture inspired by neurons in the human brain.


From the video description:

I’m pretty excited about quantum computing, it gives me a deep sense of wonder & confusion that i really enjoy. I’m so glad to be so confused (again)! I have lots more quantum machine learning papers to read in the coming weeks. In this episode, I describe the nonlinear motivations behind my paper, how i thought through the research process, and how i eventually came to some interesting results + conclusions. With the help of math, code, & manim(!) animations I’ll give it my best shot explaining some of the complex topics at the very edge of Computer Science I tackled. I hope you find it useful, enjoy!