Experiments prove Quantum Computers are more efficient than the sum of their parts

Chip with an ion trap, which researchers utilize to control and capture quantum qubits made of ion (quantum bits). Credit Kai Hudek/JQI

Quantum computer research at UMD proves that combining quantum computer parts doesn’t involve combining their error rate.

Pobody’s nerfect isn’t just the indifferent bits that calculate the core of computers. However, JQI Christopher Monroe’s team and his colleagues at Duke University have made advancements towards ensuring that you can trust the outputs of quantum computers, even if they are constructed from parts that can fail. In an experiment in the first instance, they’ve demonstrated that an assembly of quantum computing parts is superior to the parts used to construct it. In a paper published within the scientific journal Nature today (October 4, 2021), The team explained the process they used to take this significant move towards real-time, reliable quantum computers. Their research combined a variety of qubits–the quantum equivalent of bits–so that they worked together as a unit, referred to as the”logical qubit. They created the logical qubit predicated on a quantum error correction code to easily detect and correct errors, unlike for the person’s physical qubits. They made it fault-tolerant–capable of containing errors to minimize their negative effects.

“Qubits made up of identical atomic ions are natively very clean by themselves,” states Monroe. He is a Fellow at the Joint Center for Quantum Information and Computer Science and a College Park Professor in the Department of Physics at the University of Maryland. “However there comes a time where a large number of qubits or operations are needed, errors need to be further reduced, and it’s easier to add qubits to encode information in different ways. The advantage of error correction codes for atomic ions is that they can be extremely effective and can be turned on by computer controls.”

The box which houses the quantum computer with ion trap is located in Christopher Monroe’s laboratory. Credit:Marko Cetina/JQI

The first time a logical quantum has been proven to be more secure than the least susceptible to errors required to create it. They were able to place the logical qubit into its state of creation successfully and then measure its reliability to be 99.4 percent all the time even though it uses six quantum operations that are expected to function just 98.9 percent often.

It may not seem like a significant distinction, but it’s a vital step in the quest to create much more powerful quantum computers. Suppose the quantum operations comprised workers who focused on one task. In that case, the assembly line could not produce the correct initial state 93.6 percent all the time (98.9 percent multiplied six times)–roughly ten times more than the error found in the test. The reason for this is that in the experiment, the defective components work in tandem to limit the probability of quantum errors accumulating and destroying the outcome, like attentive workers who can spot each their own mistakes. The results were obtained using Monroe’s ion trap system at UMD that uses up to 32 different charged atoms, ions that are cooled by lasers before being suspended by electrodes on the chip. They use each ion as a qubit, manipulating it using lasers.

“We have 32 laser beams,” Monroe says. Monroe. “And the atoms behave similarly to ducks in a row, each one with its adjustable laser beam. I liked to think of it as the atoms that make up the form of a linear string. We’re plucked like an instrument string. It’s plucking with lasers that we can turn off and on in a way that can be programmed. That’s the computer. It’s the central processor unit.”

In creating fault-tolerant logical qubits using this technique, researchers have demonstrated that the most thoughtful, innovative designs could remove quantum computing of the limitations of the inherent errors that are part that is present in the technology. The fault-tolerant logical qubits offer an opportunity to overcome the flaws in current qubits and become the basis of quantum computer systems that are robust and large enough to be used in practical applications.

Correcting Errors and Tolerating Failures fault-tolerant

Bits that can perform error correction is crucial since Murphy’s law is inflexible; no matter how thoroughly you design a machine, it will eventually fail. In a computer, every part or qubit has a chance of occasionally failing at the job it was designed to do. The many qubits in a quantum computer mean numerous opportunities for introducing mistakes.

The good news is that engineers can create a computer whose components work in tandem to detect errors, such as making sure important information is backed up on an additional hard drive or having a third person look over your email to check for errors before you send it. The person reading the email and the drives must mess up to make it through. In comparison, it is more work to complete the job and ensure the quality of the finished product.

A few of the most popular technologies, including high-speed modems and cell phones, are currently using error correction to ensure the accuracy of data transmissions and avoid other issues. Error correction using redundancy can reduce the likelihood of a missed error so long as the procedure you use isn’t more frequently wrong than it’s correct. For example, the process of sending and storing information in three copies and relying on the majority of the voting can lower the risk of an error by one hundred percent or less to one percent one thousand. So although perfection might never be at hand, errors can improve computer performance as well as you want, as long as you can afford the cost of using additional resources. Researchers are planning to employ quantum error correction to aid in the development of more efficient qubits and enable quantum computers to be built without having to eliminate every error quantum computers suffer from.

“What’s amazing about fault tolerance is it’s a formula for just how exactly to take small unreliable parts and turn them into a very reliable device,” says Kenneth Brown, a professor of computer and electrical engineering at Duke and co-author of the paper. “And fault-tolerant quantum error correction will enable us {to make|to create|to produce} very reliable quantum computers from faulty quantum parts.”


Quantum error correction comes with special challenges: qubits are more intricate than conventional bits and could be faulty in various ways. It isn’t possible to duplicate a qubit or even verify its value in the middle of a computation. Qubits are so useful because they can exist as a quantum superposition of many states and become quantum mechanically linked to one with each other. To copy qubits, one must know precisely what information is currently stored in physical terms; you need to measure. The measurement converts it into a well-defined quantum state, eliminating the superpositions or any entanglement the quantum calculation is based upon.

To correct quantum errors, you have to fix mistakes in the text that you’re not allowed to copy or examine too closely. It’s like proofreading blindfolded. In the early 1990s, researchers began to propose ways to achieve this through quantum mechanics’ subtleties; however, quantum computers are only at the point at which they can test the theories to the test.

The main idea is to construct a logical quantum qubit using redundant physical qubits to determine if qubits are in agreement with certain quantum mechanical principles without knowing the status of any one of them.

Can’t Improve on the Atom

There are numerous quantum error correction algorithms to pick from, and some of them are more appropriate to a specific method of making the quantum computer. Each method of building quantum computers has distinct kinds of errors and advantages. Thus, creating a useful quantum computer involves understanding how to deal with unique weaknesses and strengths that your particular method offers.

The quantum computer based on the ion trap that Monroe and colleagues are working with is advantageous because the qubits that make up each are the same and extremely stable. Because the qubits are charged with electricity, each can communicate with the other qubits in the line by electrical nudges. This gives the system a lot to move freely compared to systems that require a strong connection to their immediate neighbors.

“They’re atoms of a certain element and isotope, so they’re perfectly replicable,” Monroe says. Monroe. “And when you save coherence into the qubits and then leave them to themselves, it’s basically for the rest of time. The qubit that is left to itself is perfect. We must contact lasers, modify them, and keep the Atom in place using electrodes in the vacuum chamber to use this qubit. All of these technical devices can be noisy, and they could influence it.”

In Monroe’s model, the main cause of the error is entangling operations, the creation of quantum links between two qubits by using laser pulses. Entangling operations are essential components of running a quantum computer and combining qubits to create logical qubits. Therefore, while the team cannot ensure that their logical qubits keep information more securely than individual ion qubits, the errors that can occur while entangled are a significant improvement.

The researchers picked the Bacon-Shor algorithm to be a perfect choice for both their method’s strengths and drawbacks. In this case, they only required fifteen of the 32 Ions their system can support, and two ions were not employed as qubits but were required to ensure equal spacing between other ions. The code employed nine qubits to encode the logical qubit redundantly. They also used four qubits for picking areas where errors could have occurred. Using this information, identified defective qubits could theoretically be rectified with no risk of having to worry about the “quantum-ness” of the qubits being compromised due to the measurement of the condition of each qubit.

“The important thing element of quantum error correction is redundancy, which is why we needed nine qubits to get one logical qubit,” declares JQI Ph.D. student Laird Egan, who is the primary creator of the study. “But that redundancy helps us look for errors and correct them because a mistake on a single qubit can be protected by one other eight.”

The team successfully used the Bacon-Shor program using the ion-trap system. The resulting logical quantum required six entangling processes, each of which had an error rate of between 0.7 percent and 1.5 percent. But due to the careful layout of the code, the errors aren’t a result of an even greater percentage of errors when entanglement processes were employed to prepare the logic qubit in its original state.

The team detected an oversight in preparing the qubit, and the measurement was 0.6 percent of the time. This is lower than the lowest error expected for each operation to entangle. The team could move the qubit’s logical state into a second state, with an error of only 0.3 percent. The team also deliberately introduced errors and showed they could spot these.

“This is a demonstration of quantum error correction improving performance of the underlying components for the first time,” Egan says. Egan.  “And there’s zero reasons to think that other platforms shouldn’t be able to accomplish the same thing when they expand. This is a clear proof of idea of quantum error correction that is effective.”

While the team continues with this project, they hope to replicate their success in developing even more complex quantum logical gates from their qubits. They will perform the full error correction cycle, where the errors detected are rectified and entangle several qubits of logical logic.

“Until this paper, everyone’s been dedicated to making one logical qubit,” says Egan. “And after we’ve created one, we’re thinking, “Single logic qubits aren’t working, and what do be done with two?'”Reference: “Fault-tolerant control of an error-corrected qubit” by Laird Egan, Dripto M. Debroy, Crystal Noel, Andrew Risinger, Dawei Zhu, Debopriyo Biswas, Michael Newman, Muyuan Li, Kenneth R. Brown, Marko Cetina, and Christopher Monroe, October 4, 2021, Nature.

You May Also Like

The explanation of panel Mount, The Panel Mount USB Connectors

Large Scorching-Hot Plasma Blast A Sun-Like Star Could be announcing dire warnings to the public about this great coronal

How to Train a Robot (Using Artificial Intelligence and Supercomputers)

MIT Student’s Innovative Approach to the Design of Medical Devices

Leave a Reply

Your email address will not be published.