Liwaiwai Liwaiwai
  • /
  • Artificial Intelligence
  • Machine Learning
  • Robotics
  • Engineering
    • Architecture
    • Design
    • Software
    • Hybrid Cloud
    • Data
  • Learning
  • About
  • /
  • Artificial Intelligence
  • Machine Learning
  • Robotics
  • Engineering
    • Architecture
    • Design
    • Software
    • Hybrid Cloud
    • Data
  • Learning
  • About
Liwaiwai Liwaiwai
  • /
  • Artificial Intelligence
  • Machine Learning
  • Robotics
  • Engineering
    • Architecture
    • Design
    • Software
    • Hybrid Cloud
    • Data
  • Learning
  • About
  • Machine Learning

Machine Learning Blazes Path To Reliable Near-term Quantum Computers

  • February 22, 2021
  • liwaiwai.com
Noise-aware circuit learning uses machine learning to formulate a circuit, or algorithm, with the best strategy to run a specific task in the most reliable way on a given quantum computer.

Using machine learning to develop algorithms that compensate for the crippling noise endemic on today’s quantum computers offers a way to maximize their power for reliably performing actual tasks, according to a new paper.

“The method, called noise-aware circuit learning, or NACL, will play an important role in the quest for quantum advantage, when a quantum computer solves a problem that’s impossible on a classical computer,” said Patrick Coles, a quantum physicist in at Los Alamos National Laboratory and lead author on the paper, “Machine learning of noise-resilient quantum circuits,” published today in Physical Review X Quantum.

“Our work automates designing quantum computing algorithms and comes up with the fastest algorithm tailored to the imperfections of a specific hardware platform and a specific task,” said Lukasz Cincio, a quantum physicist at Los Alamos. “This will be a crucial tool for using real quantum computers in the near term for work such as simulating a biological molecule or physics simulations relevant to the national security mission at Los Alamos.”

Coles likened the machine-learning approach to a vaccine that strengthens a person’s resistance to a virus by training their immune system in the presence of a piece of that pathogen. Similarly, the machine learning trains quantum circuits in the presence of a specific quantum computer’s noise processes. The resulting circuit, or algorithm, is resistant to that noise, which is the biggest problem facing today’s noisy intermediate-scale quantum computers.


Partner with liwaiwai.com
for your next big idea.
Let us know here.



From our partners:

CITI.IO :: Business. Institutions. Society. Global Political Economy.
CYBERPOGO.COM :: For the Arts, Sciences, and Technology.
DADAHACKS.COM :: Parenting For The Rest Of Us.
ZEDISTA.COM :: Entertainment. Sports. Culture. Escape.
TAKUMAKU.COM :: For The Hearth And Home.
ASTER.CLOUD :: From The Cloud And Beyond.
LIWAIWAI.COM :: Intelligence, Inside and Outside.
GLOBALCLOUDPLATFORMS.COM :: For The World's Computing Needs.
FIREGULAMAN.COM :: For The Fire In The Belly Of The Coder.
ASTERCASTER.COM :: Supra Astra. Beyond The Stars.
BARTDAY.COM :: Prosperity For Everyone.

NACL starts with two things: a description of a computational task and a model of the noise on the quantum computer that will perform the task. Then the machine learning program formulates a circuit with the best strategy to run the task in the most reliable way on that particular computer, based on its unique noise profile.

Read More  New Biosensor Designed To Detect Toxins And More

The framework is practical, too. It works for all of the common tasks in quantum computing — extracting observables, preparing quantum states, and compiling circuits. The Los Alamos–led team tested sample problems in each of these areas and demonstrated that NACL reduces error rates in algorithms run on quantum computers by factors of 2 to 3 compared to textbook circuits for the same tasks.

 

Noise leads to errors

Errors are caused by disruptive noise in the form of various kinds of interactions between the quantum bits, or qubits, and the surrounding environment. Those interactions cause the qubits to lose their “quantumness” in a process called decoherence, which occurs within a millionth of a second.

Quantum bits are the fundamental processing unit of a quantum computer. Bits on a classical computer can only have a value of 0 or 1—that’s the basis of all computing on your phone or laptop. Qubits, on the other hand, can have a value of 0, 1, or various “superpositions” that result in probabilities between 0 and 1. That quality gives quantum computers their potential for supreme processing power.

Previous machine-learning attempts sought to reduce the errors by shortening the circuits and reducing the number of logic gates, but did not profile the errors in particular hardware platforms. Gates are the part of a circuit that act on the qubits as part of an algorithm. Previous machine-learning codes did not train to recognize and compensate for noise.

 

Letting the computer do the work

“In this new research, we let the computer discover what’s best,” Coles explained. “In essence, we say, ‘Computer, please find the best strategy for making a resilient circuit.’ We found the computer discovers strategies that make sense to us.”

Read More  AI Reveals First Direct Observation Of Rupture Propagation During Slow Quakes

It turns out the shortest circuit isn’t always the best. Every gate is imperfect, so sometimes it’s better to add gates that correct errors on the fly.

For instance, if a particular computer erroneously over-rotates one individual qubit, the machine learning might surround it with other gates to correct errors from original gate. That’s a well-known strategy called dynamically corrected gates, but it emerges spontaneously out of the NACL optimization procedure.

Another common error-correction strategy in quantum computing is called drift, or the do-nothing gate—a qubit is left undisturbed by the algorithm, and its quantum state drifts, like a boat on a lake. If its state is a certain electron spin, for example, the earth’s magnetic field might cause a tiny alteration in that spin. But NACL rarely chooses to let a qubit sit and do nothing—the machine learning wants a gate to do something.

 

Classical training, quantum results

Coles said the team’s theoretical work involved developing a noise model of the quantum computer of interest, putting that model on a classical desktop computer, then training the machine learning on that model. After training, the machine learning output circuit, or algorithm, adapted to that particular quantum computer’s noise model.

The team then transferred the resulting algorithm to the quantum computer and evaluated its outcomes on target problems. The evaluation is based on how closely the observed output matched standard ways of measuring that output for a known problem

NACL brings a few advantages compared to other methods of compiling circuits for qubits. For instance, NACL can automatically derive known noise suppression concepts and apply them where they are useful. It also incorporates common-sense strategies such as minimizing the number of noisy idle gates and maximizing the use of ideal gates.

Read More  Azure Machine Learning Helps Customers Stay Ahead Of Challenges

“For the future, it will be important to figure out how to scale NACL to develop noise-resilient circuits for larger devices,” Coles said.

Co-authors of the paper are Lukasz Cincio, also of Los Alamos, and Kenneth Rudinger and Mohan Sarovar, of Sandia National Laboratories.

The paper: “Machine learning of noise-resilient quantum circuits,” Lukasz Cincio, Kenneth Rudinger, Mohan Sarovar, and Patrick J. Coles, Physical Review X Quantum, Feb. 16.

The funding: Funding was provided by Los Alamos National Laboratory’s Laboratory Directed Research and Development (LDRD) program.

About Los Alamos National Laboratory

Los Alamos National Laboratory, a multidisciplinary research institution engaged in strategic science on behalf of national security, is managed by Triad, a public service oriented, national security science organization equally owned by its three founding members: Battelle Memorial Institute (Battelle), the Texas A&M University System (TAMUS), and the Regents of the University of California (UC) for the Department of Energy’s National Nuclear Security Administration.

Los Alamos enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.


For enquiries, product placements, sponsorships, and collaborations, connect with us at [email protected]. We'd love to hear from you!

Our humans need coffee too! Your support is highly appreciated, thank you!

liwaiwai.com

Related Topics
  • LANL
  • Los Alamos National Laboratory
  • NACL
  • Quantum
You May Also Like
Microsoft and Adobe
View Post
  • Artificial Intelligence
  • Machine Learning
  • Platforms

Microsoft And Adobe Partner To Deliver Cost Savings And Business Benefits

  • September 21, 2023
Data
View Post
  • Artificial Intelligence
  • Machine Learning
  • Technology

UK Space Sector Has Sights Set On Artificial Intelligence And Machine Learning Professionals

  • September 15, 2023
Data
View Post
  • Artificial Intelligence
  • Engineering
  • Machine Learning
  • Platforms

How Verve Group Transforms Customer Experiences With Google Cloud Vertex AI

  • September 11, 2023
View Post
  • Artificial Intelligence
  • Machine Learning
  • Technology

ListenField Enables Farmers To Harvest The Benefits Of AI And Machine Learning

  • September 7, 2023
View Post
  • Artificial Intelligence
  • Hybrid Cloud
  • Machine Learning
  • Platforms

Red Hat OpenShift Now Available In AWS Marketplace For The U.S. Intelligence Community

  • September 6, 2023
View Post
  • Artificial Intelligence
  • Machine Learning
  • Software
  • Technology

Series Of Events Will Highlight Generative AI Use Cases Powered By Open Source Software

  • September 6, 2023
View Post
  • Artificial Intelligence
  • Machine Learning
  • Platforms

Introducing Duet AI In Apigee API Management And Application Integration

  • September 1, 2023
View Post
  • Artificial Intelligence
  • Data
  • Machine Learning
  • Platforms

IBM Introduces ‘Watsonx Your Business’

  • August 28, 2023
A Field Guide To A.I.
Navigate the complexities of Artificial Intelligence and unlock new perspectives in this must-have guide.
Now available in print and ebook.

charity-water



Stay Connected!
LATEST
  • 1
    AI-Driven Tool Makes It Easy To Personalize 3D-Printable Models
    • September 22, 2023
  • 2
    Applying Generative AI To Product Design With BigQuery DataFrames
    • September 21, 2023
  • 3
    Combining AI With A Trusted Data Approach On IBM Power To Fuel Business Outcomes
    • September 21, 2023
  • Microsoft and Adobe 4
    Microsoft And Adobe Partner To Deliver Cost Savings And Business Benefits
    • September 21, 2023
  • 5
    Huawei Connect 2023: Accelerating Intelligence For Shared Success
    • September 20, 2023
  • 6
    Document AI Workbench Is Now Powered By Generative AI To Structure Document Data Faster
    • September 15, 2023
  • Data 7
    UK Space Sector Has Sights Set On Artificial Intelligence And Machine Learning Professionals
    • September 15, 2023
  • Intel Innovation 8
    Intel Innovation 2023
    • September 15, 2023
  • 9
    Introducing OpenAI Dublin
    • September 14, 2023
  • 10
    Microsoft And Oracle Expand Partnership To Deliver Oracle Database Services On Oracle Cloud Infrastructure In Microsoft Azure
    • September 14, 2023

about
About
Hello World!

We are liwaiwai.com. Created by programmers for programmers.

Our site aims to provide materials, guides, programming how-tos, and resources relating to artificial intelligence, machine learning and the likes.

We would like to hear from you.

If you have any questions, enquiries or would like to sponsor content, kindly reach out to us at:

[email protected]

Live long & prosper!
Most Popular
  • 1
    Real-Time Ubuntu Is Now Available In AWS Marketplace
    • September 12, 2023
  • 2
    IBM Brings Watsonx To ESPN Fantasy Football With New Waiver Grades And Trade Grades
    • September 13, 2023
  • 3
    IBM Announced As A Sponsor Of 2023 U.N. Climate Change Conference (COP28)
    • September 13, 2023
  • 4
    NASA Shares Unidentified Anomalous Phenomena Independent Study Report
    • September 14, 2023
  • 5
    Bristol Set To Host UK’s Most Powerful Supercomputer To Turbocharge AI Innovation
    • September 13, 2023
  • /
  • Artificial Intelligence
  • Explore
  • About
  • Contact Us

Input your search keywords and press Enter.