A research effort from Google AI that aims to build quantum processors and develop novel quantum algorithms to dramatically accelerate computational tasks for machine learning.
Google AI Quantum is advancing quantum computing by developing quantum processors and novel quantum algorithms to help researchers and developers solve near-term problems both theoretical and practical.
We think quantum computing will help us develop the innovations of tomorrow, including AI. That’s why we’re committed to building dedicated quantum hardware and software today.
Quantum computing is a new paradigm that will play a big role in accelerating tasks for AI. We want to offer researchers and developers access to open source frameworks and computing power that can operate beyond classical capabilities.
In this video, Marissa Giustina addresses some basic questions about quantum computing. You’ll learn about what makes a quantum computer "quantum”, and what differentiates it from a regular computer. In addition, you’ll get to see what Google’s current quantum processors look like, and see the stack of hardware infrastructure needed to run the full system.
In this episode of QuantumCasts, Daniel Sank discusses the difference between classical and quantum information at the physical level, and how quantum information is harnessed in superconducting devices. You’ll learn what makes a superconducting qubit a quantum mechanical device, as well as some of the challenges researchers face in preserving quantum information.
Want to learn how to program a quantum computer using Cirq? In this episode of QuantumCasts, Dave Bacon (Twitter: @dabacon) teaches you what a quantum program looks like via a simple "hello qubit” program. You’ll also learn about some of the exciting challenges facing quantum programmers today, such as whether Noisy Intermediate-Scale Quantum (NISQ) processors have the ability to solve important practical problems. We’ll also delve a little into how the open source Python framework Cirq was designed to help answer that question.
Sergio Boixo from the Google AI Quantum applications and algorithms team explains an important milestone for quantum computing known as quantum supremacy. The Google AI Quantum team is currently attempting to achieve this milestone with its own hardware.
Nature Physics, vol. 14 (2018), 595–600
Proceedings of the 2019 International Solid State Circuits Conference, IEEE, pp. 456-458
Physical Review Letters, vol. 121 (2018), pp. 090502
Our open-source frameworks are specifically designed for developing novel quantum algorithms to help solve near-term applications for practical problems.
Cirq is an open source Python framework for writing, compiling, and running quantum algorithms optimized for today’s quantum computers.
Quantum circuits have the ability to concisely express relationships between input variables that can be intractably difficult for traditional neural networks. Exploring this capability in the context of quantum neural networks for classification problems and how basic properties of quantum systems influence training of these networks is crucial to understanding the role of quantum computers in machine learning.
A low-power cryogenic-CMOS single-qubit controller deployed inside the cryostat at 3 Kelvin. This proof of principle prototype motivates future qubit control techniques with lower density of cables in the cryostat.
Understanding performance fluctuations in quantum processors lays foundation for improving processor design, fabrication, and calibration.
Theoretical foundation for our research to demonstrate a computational task that is prohibitively hard for today’s classical computers but which can be carried out experimentally with our quantum processors.
We show how molecules can be represented on quantum computers to simplify the quantum circuits required to solve the problem, and design algorithms for near-term quantum processors with qubits laid out in a linear array.