ViewTube

Important changes to Qiskit documentation and learning resources

 https://medium.com/qiskit/important-changes-to-qiskit-documentation-and-learning-resources-7f4e346b19ab 

Today, we’re announcing important changes to Qiskit.org that you should be aware of. Starting on November 29, 2023, Qiskit Documentation and Qiskit educational resources will live on IBM Quantum Platform and will be removed from Qiskit.org. We will be slowly moving content over until then.

Read more...

 https://medium.com/qiskit/important-changes-to-qiskit-documentation-and-learning-resources-7f4e346b19ab 

A very special panel, join us on October 27th Noon Eastern

This session will be a little different from the usual Qiskit Quantum Seminar. Following an introduction by Jay Gambetta, this panel discussion will highlight some of the recent work using circuits with more than 100 qubits in order to push science forward, and discuss what it takes to perform research at this scale.

Quantum computing has long been in an exploratory phase, where researchers largely ran small-scale experiments. These experiments were valuable, both to help educate the community about how to run circuits on quantum hardware and to validate the tenets of quantum computation. But these experiments don’t push the field of quantum computation forward, since we can simulate them exactly using a classical computer. Therefore, real advancement means we need to run large circuits—and we need to run those circuits on real quantum hardware. Today, papers from researchers based at the University of Washington, Stony Brook University, IBM, and elsewhere are exploring a space beyond exact classical simulability, running circuits with over a hundred qubits and hundreds‚ even thousands of gates. 

Quantum computers are now competing to surpass classical supercomputers in test-bed calculations, but errors limit their performance. Quantum error mitigation could overcome these errors, although in practice it can necessitate prohibitive computational overhead. Machine learning has been suggested as a solution to this problem, but its practical effectiveness remains uncertain. Here, we demonstrate that machine learning methods can be a key ingredient of quantum error mitigation in practice. We benchmark a variety of machine learning models---linear regression, random forests, multi-layer perceptrons, and graph neural networks---on diverse classes of quantum circuits and device noise profiles. For small-scale, simulable quantum circuits, machine learning models outperform a popular approach for quantum error mitigation (digital zero noise extrapolation) in both accuracy and runtime efficiency, even when applied to complex circuits and observables absent in training. To scale to large, classically intractable circuits, we demonstrate that machine learning methods can mimic other error mitigation methods---thereby reducing their overhead---through experiments on quantum hardware using 100 qubits. These results highlight the potential of classical machine learning for practical quantum computation. 

Applications are now open to intern with IBM Quantum the summer of 2024. 
 https://research.ibm.com/blog/2024-quantum-internships 

We have directly trained more than 400 interns at all levels of higher education 
since 2020, many of whom have gone on to work at IBM Quantum or elsewhere 
in the field of quantum after graduation. Interns in the US will work at either the 
Thomas J. Watson Research Center in Yorktown Heights, New York, or at IBM 
Research — Almaden in San Jose, California from either May 20, 2024 to August 
9, 2024, or from June 17, 2024 to September 6, 2024. 

International internship opportunities will open later. See the announcement for more information! 

👀 New series coming soon! 

Quantum neural networks (QNNs) have potential to bring benefits to ML tasks like medical diagnosis and logistics, but they can be hard to train. 

 https://medium.com/qiskit/layerwise-learning-for-quantum-neural-networks-with-qiskit-e17ff4b1c419 

In this week's blog, we look at how layerwise learning can help tackle a common trainability problem in QNNs. 

You’ve probably heard about quantum safe cryptography, but how do quantum safe encryption algorithms work? And why are they immune to quantum cyberattacks?

In 2022, after six years of rigorous testing and review of almost 70 candidates, the US National Institute for Science and Technology (NIST) selected four quantum-safe cryptographic algorithms for standardization.

All four of NIST’s newly selected algorithms are based on problems that exploit properties of mathematical lattices, a kind of abstract structure that is useful in many different disciplines.

In this week’s blog, we take a look at how lattice-based crypto works, and why you should begin to explore quantum safe cryptography:

 https://medium.com/qiskit/why-quantum-learners-should-study-quantum-safe-cryptography-7877ec695790 

NEW BLOG! On the Qiskit Medium, we take a look at the career of Qiskit’s own Helena Zhang and her journey from Physics PhD to leading Qiskit Experiments, part of our Qiskit Ecosystem.

 https://medium.com/qiskit/how-helena-zhang-came-to-lead-qiskit-experiments-45e32a84f8d1 

This week on the Qiskit Medium: Quantum computers could one day help researchers simulate life-saving drugs and sustainable new materials, but can quantum computers simulate… different quantum computers?  https://qisk.it/3K1ExDG 

Happy World Quantum Day! Olivia was featured in this video from our friends at  @q12education