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Home » See how smartphones shockingly outperform quantum computers.
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See how smartphones shockingly outperform quantum computers.

adminBy adminOctober 29, 2024No Comments4 Mins Read
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Surprised and confused adult man looking at smartphoneSurprised and confused adult man looking at smartphone

Surprised and confused adult man looking at his smartphone (© Prostock-studio – Stock.adobe.com)

new york – In the high-stakes race to build quantum computers, scientists have discovered something completely unexpected. These futuristic machines can still be outdone by the device in your pocket. Researchers at the Flatiron Institute in New York have witnessed a monumental event in which the ordinary computers powering smartphones outperform the next generation of quantum computers.

This David-versus-Goliath reversal is telling us interesting new things about how quantum systems work. Earlier this year, researchers at the Flatiron Institute’s Center for Computational Quantum Physics (CCQ) used ordinary computers to solve complex quantum problems that IBM had claimed could only be tackled with its advanced quantum computers. It was resolved. Even more surprising, the solution was efficient enough to run on a smartphone.

“We didn’t actually introduce cutting-edge technology,” lead researcher Joseph Tyndall said in a media release. “We brought together many ideas in a concise and elegant way that allowed us to solve problems. I couldn’t.”

Diagram of a quantum system simulated on both classical and quantum computers. The highlighted section shows how the influence of a component of the system is limited to neighboring components.Diagram of a quantum system simulated on both classical and quantum computers. The highlighted section shows how the influence of a component of the system is limited to neighboring components.
Diagram of a quantum system simulated on both classical and quantum computers. The highlighted section shows how the influence of a component of the system is limited to neighboring components. (Credit: Lucy Reading-Ikanda/Simons Foundation)

Now, a new study has been published physical review letterTyndall and colleague Dries Sells revealed why this quantum puzzle was so surprisingly easy to solve. The answer involves an interesting phenomenon called “confinement,” which traps quantum particles in cages like invisible sheep.

The initial challenge involved simulating how an array of tiny magnets changes over time when exposed to a magnetic field. These quantum magnets, unlike the regular magnets you find in your refrigerator, can exist in multiple states at the same time and can point both up and down at the same time. Typically, such quantum systems quickly become so complex that they become nearly impossible to simulate on a classical computer as the magnets “entangle” with each other.

“There are some boundaries between what quantum computing can do and what classical computers can do,” Tyndall explains. “Right now, that line is incredibly blurry. I think our work will help make that line a little clearer.”

The researchers discovered that the honeycomb arrangement of magnets naturally created an energy barrier that prevented large-scale entanglements from forming. Confinement essentially keeps quantum systems well-behaved and predictable, much like a playground fence keeps children from wandering too far.

“In this system, the magnet doesn’t just suddenly go wild; it actually just oscillates around its initial state, even over very long timescales,” Tyndall says. “This is very interesting from a physics perspective, because it means that the system is not only completely disordered, but remains in a state with a very specific structure.”

This discovery has important implications for quantum computing. If quantum particles remain confined, they can be easier to control and simulate, potentially leading to more reliable quantum computers and new ways to test quantum systems using classical computers.

Infographic explaining quantum systems that classical computers solved faster than quantum computers. Infographic explaining quantum systems that classical computers solved faster than quantum computers.
Infographic explaining quantum systems that classical computers solved faster than quantum computers. (Credit: Lucy Reading-Ikanda/Simons Foundation)

Paper summary

methodology

The researchers used sophisticated mathematical modeling to simulate an infinite grid of quantum magnets. Their approach, called “Infinite Tensor Network States Optimized with Belief Propagation” (BP-iTNS), allowed them to track how magnets behave when disturbed by magnetic fields. Identifying different types of trapped particle patterns can potentially predict and explain stable oscillations in systems.

Main results

The study reveals that quantum confinement occurs naturally in this two-dimensional system, keeping quantum particles organized in predictable patterns. This explains why classical computers were able to simulate systems so effectively. Confinement prevented the explosion of complexity that normally makes quantum systems difficult to simulate. The researchers developed a mathematical model that accurately predicted the system’s behavior, consistent with computer simulations.

Research limitations

Although this study focuses on a specific type of quantum system arranged in a honeycomb pattern, the researchers believe their findings may also apply to other two-dimensional quantum systems. . However, further studies are needed to confirm this broader application. Although simulation techniques are very effective for this system, they may not work well for quantum systems that do not exhibit confinement.

Discussion and key points

This research helps clarify the boundaries between what classical and quantum computers can achieve. The discovery of confinement in two-dimensional quantum systems provides new tools for testing and benchmarking quantum simulations. This discovery is particularly important for the field, as until now this type of confinement has only been observed in one-dimensional quantum systems.

Funding and disclosure

This research was supported by the Flatiron Institute, a division of the Simons Foundation, and the Air Force Office of Scientific Research (grant number FA9550-21-1-0236). The researchers used the publicly available ITENSORNETWORKS.JL software package. No conflicts of interest are declared.



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