Here’s an interesting (if not pessimistic) look at the near term future of Quantum AI’s wider deployment.
Quantum AI executes ML (machine learning), DL (deep learning), and other data-driven AI algorithms reasonably well.
As an approach, quantum AI has moved well beyond the proof-of-concept stage. However, that is not the same as being able to claim that quantum approaches are superior to classical approaches for executing the matrix operations upon which AI’s inferencing and training workloads depend.
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.
Because our most powerful classical computers are limited in the chemical modeling they can perform, so are the solutions they can unlock.
Quantum computing could change that.
On this episode of Quantum Impact, Dr. Krysta Svore, general manager of quantum systems and software at Microsoft, heads to Richland, Washington to meet with Dr. Nathan Baker and Dr. Bojana Ginovska at Pacific Northwest National Laboratory (PNNL).
Microsoft is partnering with PNNL to bring the power of quantum to our understanding of chemistry. One of PNNL’s areas of interest is catalysis, or the process of converting chemicals from one form to another, and Nathan shares the complexity involved in truly understanding that process.
Bojana, a computational chemist, then speaks with Krysta about her work studying nitrogenase, an enzyme present in healthy soil. She’s exploring how we can turn nitrogen into ammonia for agriculture in a way that doesn’t deplete our energy resources.
Together with PNNL, Microsoft is working to develop quantum algorithms to help solve challenging problems in chemistry, which will have hugely positive impacts on our world and our planet’s future.
Quantum computing is still in at the early stage. However, the technology is maturing rapidly.
To take advantage of tomorrow’s ultra-fast quantum machines, researchers will have to write specialized algorithms that can run on qubits instead of bits, and they’ll need equally specialized development tools to help with the task.
That’s where TensorFlow Quantum comes into the picture. It provides a set of operators, low-level programming building blocks, for creating AI models that work with qubits, quantum logic gates and quantum circuits. These operators abstract away some of the underlying complexity to reduce the amount of code researchers need to write.
This tutorial on TensorFlow.org implements a simplified Quantum Convolutional Neural Network (QCNN), a proposed quantum analogue to a classical convolutional neural network that is also translationally invariant.
This tutorial implements a simplified Quantum Convolutional Neural Network (QCNN), a proposed quantum analogue to a classical convolutional neural network that is also translationally invariant . This example demonstrates how to detect certain properties of a quantum data source, such as a quantum sensor or a complex simulation from […]
Lex Fridman interviews Alex Garland, writer and director of many imaginative and philosophical films from the dreamlike exploration of human self-destruction in the movie Annihilation to the deep questions of consciousness and intelligence raised in the movie Ex Machina.
0:00 – Introduction
3:42 – Are we living in a dream?
7:15 – Aliens
12:34 – Science fiction: imagination becoming reality
17:29 – Artificial intelligence
22:40 – The new “Devs” series and the veneer of virtue in Silicon Valley
31:50 – Ex Machina and 2001: A Space Odyssey
44:58 – Lone genius
49:34 – Drawing inpiration from Elon Musk
51:24 – Space travel
54:03 – Free will
57:35 – Devs and the poetry of science
1:06:38 – What will you be remembered for?