In 2019, Google announced that its 53-qubit machine had achieved quantum supremacy-performing a task that could not be handled by a conventional computer কিন্তু but IBM challenged that claim. The same year, IBM has launched its 53-bit Quantum computer. In 2020, IonQ Has unveiled a 32-qubit system that the company says is “the most powerful quantum computer in the world.” And this week IBM launched its new 127-qubit quantum processor, which the press release described as “a small miracle of design.” “The big news, from my point of view, is how it works,” said Jay Gambeta, vice president of IBM’s Quantum Computing.
Now QuEra claims to have created a device with far more qubits than those competitors.
The ultimate goal of quantum computing, of course, is not to play Tetris, but to transcend classical computers to solve problems of practical interest. Enthusiasts believe that when these computers become powerful enough, perhaps within a decade or two, they could have transformative effects in fields like medicine and money, neuroscience and AI. Quantum machines will probably need thousands of cubits to handle such complex problems.
The number of cubits, however, is not the only important issue.
QuEra also talks about the enhanced programmability of its device, where each qubit is a single, ultra-cold atom. These atoms are precisely arranged with a series of lasers (physicists call them optical tweezers). The position of the qubits allows the machine to be programmed, tuned to the problem under investigation, and reconfigured in real time during the calculation process.
“Different problems require atoms to be placed in different configurations,” said Alex Kiesling, CEO of QuEra and co-inventor of the technology. “One of the unique things about our machine is that every time we run it, a few times a second, we can completely redefine the connection of geometry and qubits.”
The advantage of atoms
The QuEra machine was built from a blueprint and technologies refined over several years, led by Mikhail Lukin and Marcus Greiner at Harvard and Vladan Vuletic and Dark England at MIT (all on QuEra’s founding team). In 2017, an earlier model of the device from Harvard Group was used only 51 qubit; In 2020, they performed 256-qubit machine. The QuEra team expects to reach 1,000 qubits in two years, and then, without changing the platform too much, they expect to continue scaling the system beyond a few thousand qubits.
It is QuEra’s unique platform, the way the system is integrated, and the way data is encoded and processed, which allows it to jump to such a scale.
Although Google and IBM’s quantum computing systems use superconducting qubits, and IonQ uses stuck ions, QuEra’s platform uses arrays of neutral atoms that create qubits with impressive combinations (i.e., high levels of “quantumness”). The machine uses laser pulses to interact with atoms, stimulating them into a state of energy – a “Rydberg state”, as described by the Swedish physicist Johannes Rydberg in 1888 – where they can perform quantum logic in a powerful way with high fidelity. This Rydberg approach Per Quantum computing It has worked for decades, but with technological advances উদাহরণ for example, lasers and photonics it needed to work reliably.
When computer scientist Umesh Vajirani, director of the Quantum Computation Center in Berkeley, first learned of Lukin’s research, he felt “unreasonably overwhelmed” – it seemed like a strange approach, although Vajirani questioned whether his insights were in touch with reality. “We have a variety of well-developed paths, such as superconductors and ion traps, that have been in operation for a long time,” he says. “Shouldn’t we think of different plans?” He checked in with John Preskill, a physicist at the California Institute of Technology and director of the Institute for Quantum Information and Matter, who assured Vajirani that his excitement was justified.
Preskill Rydberg finds platforms (not just QuEra) interesting because they create strongly interacting qubits that are highly frozen: “And that’s where quantum magic comes in,” he says. “I’m excited about the possibility of discovering unexpected things on a relatively short scale.”
As well as imitation and understanding Quantum materials and dynamicsResearchers are also working on quantum algorithms to solve computational optimization problems, which Lukin called “the first examples of useful quantum benefits involved in scientific applications.” NP-complete (I.e., very difficult).