
Google Quantum AI
@GoogleQuantumAI
Followers
42K
Following
70
Media
311
Statuses
567
Our mission is to build quantum computing for otherwise unsolvable problems.
Joined April 2021
Meet Willow: Our state-of-the-art quantum chip. It's the first quantum chip to show exponential error reduction as qubits scale, paving the way for large-scale, fault-tolerant quantum computers. Dive in →
312
635
2K
Congrats to Hartmut Neven, founder and lead of our team, recognized on the #TIME100AI list. Quantum computing and AI, while distinct, are complementary. AI can help build better quantum computers, and in the future, quantum computing will empower AI with new capabilities.
Behind every AI breakthrough and launch, there are hundreds of Googlers working to build AI that’s helpful for everyone. This year, our very own @JoshWoodward VP of Gemini and Google Labs, @JeffDean Chief Scientist at DeepMind and Google Research, and Hartmut Neven founder
14
35
199
RT @PennyLaneAI: PennyLane and Qualtran integrated? Sweet! 🍬. This new integration merges the best features of both, enabling you to constr….
0
3
0
Tensor networks are becoming a vital tool in quantum computing. Originally used for simulating quantum systems, their applications now include: . - Quantum circuit synthesis .- Quantum error correction .- Quantum machine learning . Read more from Nature:
nature.com
Nature Reviews Physics - Tensor networks provide a powerful tool for understanding and improving quantum computing. This Technical Review discusses applications in simulation, circuit synthesis,...
8
70
341
Excited to see this report from the @BritishCouncil, featuring Erik Lucero and our Google Quantum AI Artists in Residence Program, highlights the growing intersection of quantum computing and the arts. Read the report:
📢 Excited to share our new publication: "Why technology needs artists"! Explore how artists drive tech advancement. 📖 This new report features 56 leaders from across 24 countries and five continents, read it here: @BritishArts
7
21
102
New research presents a complete algorithm for ground-state energy estimation, addressing the challenge of imperfect initial state preparation. We detail more efficient matrix-product-state (MPS) preparation and improved energy sampling methods →
journals.aps.org
Faster preparation of ground-state approximations plus more efficient sampling of those prepared states yields a method to reliably estimate the ground-state energy of complex chemical systems.
5
84
400
RT @Googleorg: Applications are open for the Google Academic Research Awards, a program that supports academic research in computing and te….
research.google
0
11
0
RT @GoogleResearch: Error correction is a key component of tomorrow’s large-scale quantum computers. Today we’re excited to report the expe….
0
59
0