The latest results announced by Google come from a new chip called Willow, which contains 105 “quantum bits.”
Recently, Google claimed it has solved a key challenge in quantum computing by using its next-generation chip, solving a computational problem in five minutes that would take a conventional computer a time longer than the age of the universe to complete.
A quantum computer is a physical device that performs high-speed mathematical and logical operations, stores, and processes quantum information based on the principles of quantum mechanics. Broadly speaking, any device that processes and computes quantum information using quantum algorithms can be called a quantum computer.
The main characteristics of quantum computers include faster operation speeds, stronger information storage capabilities, and broader application scopes. Compared to conventional computers, the more data processed, the more advantageous it is for quantum computers to perform calculations, thus ensuring precision in computations. The computational foundation of quantum computers is the quantum bit.
Like other tech giants such as Microsoft and IBM, Alphabet’s Google is also pursuing quantum computing because of the promise of computational speeds far surpassing today’s fastest systems. Although the mathematical problems solved by Google’s quantum laboratory in Santa Barbara, California, have no commercial applications, Google hopes that one day quantum computers will address issues in medicine, battery chemistry, and artificial intelligence that current computers cannot solve.
The latest results from Google come from a new chip named Willow, which contains 105 “quantum bits,” the cornerstone of quantum computers. Quantum bits are fast but prone to errors because they can be affected by tiny objects, like subatomic particles from outer space events.
According to Google, even today’s fastest supercomputers would require “10 to the 25th power” years to complete this calculation— a number far exceeding the age of the universe.
As the number of quantum bits on a chip increases, these errors can accumulate, and the chip’s performance may not be superior to traditional computer chips. Therefore, since the 1990s, scientists have been researching quantum error correction.
In a paper published on December 9 in Nature, Google announced that it had found a method to link the quantum bits of the Willow chip, reducing the error rate as the number of quantum bits increases. The company also stated that it can correct errors in real-time, which is a crucial step toward making quantum machines practical.
“We’ve passed the break-even point,” said Hartmut Neven, head of Google’s Quantum AI division, in an interview.
Looking back at the history of quantum computers, in 2019, Google developed a 53-qubit quantum computer named “Sycamore,” achieving quantum supremacy for the first time in the world. In 2020, Pan Jianwei’s team in China built a quantum computing prototype with 76 photons, called “Jiuzhang,” making China the second country to achieve quantum supremacy. In 2021, Pan Jianwei’s team successfully developed the “Jiuzhang II” with 113 photons and the “Zuchongzhi II” with 66 qubits, making China the first country to achieve quantum supremacy in both optical and superconducting technologies. On October 11, 2023, the quantum computing prototype “Jiuzhang III” was successfully built, and its most complex sample could be computed in 1 microsecond, a task that the world’s fastest supercomputers would need about 20 billion years to complete.
Back in 2019, IBM questioned Google’s claim, which stated that its quantum chip solved a problem that would take traditional computers 10,000 years, arguing that with different technical assumptions about traditional systems, the problem could be solved in two and a half days.
Over the past few decades, quantum computing technology has developed rapidly. As a new computing paradigm, quantum computing not only breaks through the bottlenecks of traditional computers but also promises enormous breakthroughs and revolutions in various fields.
For example, in some machine learning tasks, such as training and optimizing neural networks, quantum computers can accelerate the computational process. Some quantum machine learning algorithms (such as quantum support vector machines and quantum neural networks) can leverage the superposition and entanglement properties of quantum states to process data more efficiently. For instance, when handling large-scale image recognition tasks or natural language processing tasks, quantum computers are expected to reduce training time and improve the learning efficiency of algorithms.
In quantum physics research, quantum computers can simulate complex quantum systems. For example, they can be used to study quantum many-body problems, such as high-temperature superconductivity mechanisms. The microscopic physical mechanisms of high-temperature superconductivity have been a challenge in condensed matter physics, but quantum computers can help scientists better understand the causes of superconducting phenomena by accurately simulating the interactions between electrons, providing theoretical support for the development of superconducting materials that work at higher temperatures.
As we can see, from basic scientific research to commercial applications, quantum computing has undeniable potential.
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