Nanotechnology Now

Our NanoNews Digest Sponsors
Heifer International



Home > Press > Advance in quantum error correction: Protocol corrects virtually all errors in quantum memory, but requires little measure of quantum states

A new quantum error correcting code requires measurements of only a few quantum bits at a time, to ensure consistency between one stage of a computation and the next.

Jose-Luis Olivares/MIT
A new quantum error correcting code requires measurements of only a few quantum bits at a time, to ensure consistency between one stage of a computation and the next.

Jose-Luis Olivares/MIT

Abstract:
Quantum computers are largely theoretical devices that could perform some computations exponentially faster than conventional computers can. Crucial to most designs for quantum computers is quantum error correction, which helps preserve the fragile quantum states on which quantum computation depends.

Advance in quantum error correction: Protocol corrects virtually all errors in quantum memory, but requires little measure of quantum states

Cambridge, MA | Posted on May 27th, 2015

The ideal quantum error correction code would correct any errors in quantum data, and it would require measurement of only a few quantum bits, or qubits, at a time. But until now, codes that could make do with limited measurements could correct only a limited number of errors -- one roughly equal to the square root of the total number of qubits. So they could correct eight errors in a 64-qubit quantum computer, for instance, but not 10.

In a paper they're presenting at the Association for Computing Machinery's Symposium on Theory of Computing in June, researchers from MIT, Google, the University of Sydney, and Cornell University present a new code that can correct errors afflicting a specified fraction of a computer's qubits, not just the square root of their number. And that fraction can be arbitrarily large, although the larger it is, the more qubits the computer requires.

"There were many, many different proposals, all of which seemed to get stuck at this square-root point," says Aram Harrow, an assistant professor of physics at MIT, who led the research. "So going above that is one of the reasons we're excited about this work."

Like a bit in a conventional computer, a qubit can represent 1 or 0, but it can also inhabit a state known as "quantum superposition," where it represents 1 and 0 simultaneously. This is the reason for quantum computers' potential advantages: A string of qubits in superposition could, in some sense, perform a huge number of computations in parallel.

Once you perform a measurement on the qubits, however, the superposition collapses, and the qubits take on definite values. The key to quantum algorithm design is manipulating the quantum state of the qubits so that when the superposition collapses, the result is (with high probability) the solution to a problem.

Baby, bathwater

But the need to preserve superposition makes error correction difficult. "People thought that error correction was impossible in the '90s," Harrow explains. "It seemed that to figure out what the error was you had to measure, and measurement destroys your quantum information."

The first quantum error correction code was invented in 1994 by Peter Shor, now the Morss Professor of Applied Mathematics at MIT, with an office just down the hall from Harrow's. Shor is also responsible for the theoretical result that put quantum computing on the map, an algorithm that would enable a quantum computer to factor large numbers exponentially faster than a conventional computer can. In fact, his error-correction code was a response to skepticism about the feasibility of implementing his factoring algorithm.

Shor's insight was that it's possible to measure relationships between qubits without measuring the values stored by the qubits themselves. A simple error-correcting code could, for instance, instantiate a single qubit of data as three physical qubits. It's possible to determine whether the first and second qubit have the same value, and whether the second and third qubit have the same value, without determining what that value is. If one of the qubits turns out to disagree with the other two, it can be reset to their value.

In quantum error correction, Harrow explains, "These measurement always have the form 'Does A disagree with B?' Except it might be, instead of A and B, A B C D E F G, a whole block of things. Those types of measurements, in a real system, can be very hard to do. That's why it's really desirable to reduce the number of qubits you have to measure at once."

Time embodied

A quantum computation is a succession of states of quantum bits. The bits are in some state; then they're modified, so that they assume another state; then they're modified again; and so on. The final state represents the result of the computation.

In their paper, Harrow and his colleagues assign each state of the computation its own bank of qubits; it's like turning the time dimension of the computation into a spatial dimension. Suppose that the state of qubit 8 at time 5 has implications for the states of both qubit 8 and qubit 11 at time 6. The researchers' protocol performs one of those agreement measurements on all three qubits, modifying the state of any qubit that's out of alignment with the other two.

Since the measurement doesn't reveal the state of any of the qubits, modification of a misaligned qubit could actually introduce an error where none existed previously. But that's by design: The purpose of the protocol is to ensure that errors spread through the qubits in a lawful way. That way, measurements made on the final state of the qubits are guaranteed to reveal relationships between qubits without revealing their values. If an error is detected, the protocol can trace it back to its origin and correct it.

It may be possible to implement the researchers' scheme without actually duplicating banks of qubits. But, Harrow says, some redundancy in the hardware will probably be necessary to make the scheme efficient. How much redundancy remains to be seen: Certainly, if each state of a computation required its own bank of qubits, the computer might become so complex as to offset the advantages of good error correction.

But, Harrow says, "Almost all of the sparse schemes started out with not very many logical qubits, and then people figured out how to get a lot more. Usually, it's been easier to increase the number of logical qubits than to increase the distance -- the number of errors you can correct. So we're hoping that will be the case for ours, too."

####

For more information, please click here

Contacts:
Abby Abazorius

617-253-2709

Copyright © Massachusetts Institute of Technology

If you have a comment, please Contact us.

Issuers of news releases, not 7th Wave, Inc. or Nanotechnology Now, are solely responsible for the accuracy of the content.

Bookmark:
Delicious Digg Newsvine Google Yahoo Reddit Magnoliacom Furl Facebook

Related Links

Paper: “Sparse quantum codes from quantum circuits”:

Related News Press

News and information

Quantum computer improves AI predictions April 17th, 2026

Flexible sensor gains sensitivity under pressure April 17th, 2026

A reusable chip for particulate matter sensing April 17th, 2026

Detecting vibrational quantum beating in the predissociation dynamics of SF6 using time-resolved photoelectron spectroscopy April 17th, 2026

New UBC wash removes pesticides and extends produce shelf life: Natural, biodegradable rinse removes up to 96 per cent of pesticide residue and slowed spoilage in apples and grapes April 17th, 2026

Physics

UC Irvine physicists discover method to reverse ‘quantum scrambling’ : The work addresses the problem of information loss in quantum computing system April 17th, 2026

INRS and ELI deepen strategic partnership to train the next generation in laser science:PhD students will benefit from international mobility and privileged access to cutting-edge infrastructure June 6th, 2025

Quantum computers simulate fundamental physics: shedding light on the building blocks of nature June 6th, 2025

Chip Technology

A reusable chip for particulate matter sensing April 17th, 2026

When light gets trapped at nanoscale: New ways to power the future of optoelectronics From bound states in the continuum to machine-learning design, photonic metasurfaces are opening scalable routes to efficient light control April 17th, 2026

Rice study resolves decades-old mystery in organic light-emitting crystals: Findings reveal how molecular defects can enhance light conversion efficiency: April 17th, 2026

Metasurfaces smooth light to boost magnetic sensing precision January 30th, 2026

Memory Technology

Researchers tackle the memory bottleneck stalling quantum computing October 3rd, 2025

First real-time observation of two-dimensional melting process: Researchers at Mainz University unveil new insights into magnetic vortex structures August 8th, 2025

An earth-abundant mineral for sustainable spintronics: Iron-rich hematite, commonly found in rocks and soil, turns out to have magnetic properties that make it a promising material for ultrafast next-generation computing April 25th, 2025

Utilizing palladium for addressing contact issues of buried oxide thin film transistors April 5th, 2024

Quantum Computing

Quantum computer improves AI predictions April 17th, 2026

Qjump: Shallow-circuit quantum sampling guides combinatorial optimization On up to 104 superconducting qubits, Qjump assists in searching the ground states of hard Ising problems and might outperform simulated annealing on near-term quantum hardware April 17th, 2026

UC Irvine physicists discover method to reverse ‘quantum scrambling’ : The work addresses the problem of information loss in quantum computing system April 17th, 2026

Researchers develop molecular qubits that communicate at telecom frequencies October 3rd, 2025

Discoveries

Quantum computer improves AI predictions April 17th, 2026

Flexible sensor gains sensitivity under pressure April 17th, 2026

A reusable chip for particulate matter sensing April 17th, 2026

Detecting vibrational quantum beating in the predissociation dynamics of SF6 using time-resolved photoelectron spectroscopy April 17th, 2026

Announcements

A fundamentally new therapeutic approach to cystic fibrosis: Nanobody repairs cellular defect April 17th, 2026

Qjump: Shallow-circuit quantum sampling guides combinatorial optimization On up to 104 superconducting qubits, Qjump assists in searching the ground states of hard Ising problems and might outperform simulated annealing on near-term quantum hardware April 17th, 2026

Rice study resolves decades-old mystery in organic light-emitting crystals: Findings reveal how molecular defects can enhance light conversion efficiency: April 17th, 2026

UC Irvine physicists discover method to reverse ‘quantum scrambling’ : The work addresses the problem of information loss in quantum computing system April 17th, 2026

Interviews/Book Reviews/Essays/Reports/Podcasts/Journals/White papers/Posters

A fundamentally new therapeutic approach to cystic fibrosis: Nanobody repairs cellular defect April 17th, 2026

Qjump: Shallow-circuit quantum sampling guides combinatorial optimization On up to 104 superconducting qubits, Qjump assists in searching the ground states of hard Ising problems and might outperform simulated annealing on near-term quantum hardware April 17th, 2026

Rice study resolves decades-old mystery in organic light-emitting crystals: Findings reveal how molecular defects can enhance light conversion efficiency: April 17th, 2026

UC Irvine physicists discover method to reverse ‘quantum scrambling’ : The work addresses the problem of information loss in quantum computing system April 17th, 2026

NanoNews-Digest
The latest news from around the world, FREE




  Premium Products
NanoNews-Custom
Only the news you want to read!
 Learn More
NanoStrategies
Full-service, expert consulting
 Learn More











ASP
Nanotechnology Now Featured Books




NNN

The Hunger Project