Blog
The latest news from Google AI
A step closer to quantum computation with Quantum Error Correction
Wednesday, March 4, 2015
Posted by Julian Kelly, Rami Barends, and Austin Fowler, Quantum Electronics Engineers
Computer scientists have dreamt of large-scale
quantum computation
since at least
1994
-- the hope is that quantum computers will be able to process certain calculations much more quickly than any classical computer, helping to solve problems ranging from complicated physics or chemistry simulations to solving optimization problems to accelerating machine learning tasks.
One of the primary challenges is that quantum memory elements (“
qubits
”) have always been too prone to errors. They’re fragile and easily disturbed -- any fluctuation or noise from their environment can introduce memory errors, rendering the computations useless. As it turns out, getting even just a small number of qubits together to repeatedly perform the required quantum logic operations and still be nearly error-free is just plain
hard
. But our team has been developing the quantum logic operations and qubit architectures to do just that.
In our paper “
State preservation by repetitive error detection in a superconducting quantum circuit
”, published in the journal
Nature
, we describe a superconducting quantum circuit with nine qubits where, for the first time, the qubits are able to detect and effectively protect each other from bit errors. This
quantum error correction
(QEC) can overcome memory errors by applying a carefully choreographed series of logic operations on the qubits to detect where errors have occurred.
Photograph of the device containing nine quantum bits (qubits). Each qubit interacts with its neighbors to protect them from error.
So how does QEC work? In a classical computer, we can monitor bits directly to detect errors. However, qubits are much more fickle -- measuring a qubit directly will collapse
entanglement
and
superposition
states, removing the quantum elements that make it useful for computation.
To get around this, we introduce additional ‘measurement’ qubits, and perform a series of quantum logic operations that look at the 'measurement' and 'data' qubits in combination. By looking at the state of these pairwise combinations (using quantum
XOR gates
), and performing some careful cross-checking, we can pull out just enough information to detect errors without altering the information in any individual qubit.
The basics of error correction. ‘Measurement’ qubits can detect errors on ‘data’ qubits through the use of quantum XOR gates.
We’ve also shown that storing information in five qubits works better than just storing it in one, and that with nine qubits the error correction works even better. That’s a key result -- it shows that the quantum logic operations are trustworthy enough that by adding more qubits, we can detect more complex errors that otherwise may cause algorithmic failure.
While the basic physical processes behind quantum error correction are feasible, many challenges remain, such as improving the logic operations behind error correction and testing protection from phase-flip errors. We’re excited to tackle these challenges on the way towards making real computations possible.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2020
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
