Quantum computing faces its make-or-break scale test

quantum computers – From refrigeration-heavy chip fabrication to the physics of Schrödinger’s cat, quantum computing is nearing a turning point: researchers must scale today’s fragile systems enough to beat classical machines on tasks that matter. Hype about fast breakthroughs an

In a low-slung building in an office park near the southeastern edge of the San Francisco Bay. the lights never feel fully white. Blue illumination pools over a cluster of tanks that hold liquid helium and liquid nitrogen—coolant reserves so massive they define the place as much as the people inside it. Inside those refrigeration tanks are superconducting circuits etched into chips, each housed by golden chandelierlike structures.

The superconducting chips are fabricated in a clean room next door, where white-suited workers operate room-size machinery, fume hoods, and acid baths. It’s a workflow built for one goal: making quantum computers real.

The facility is the main fabrication plant for quantum computing company Rigetti Computing in California. Each refrigeration tank contains one of Rigetti’s top-of-the-line quantum processing units. Rigetti’s CEO, Subodh Kulkarni, argues the promise is extraordinary. “We’re talking a million [or a] billion times faster at a very, very small fractional energy consumption,” he says. “That’s the beauty of quantum computing. We can potentially solve problems that are unsolvable today.”.

Rigetti is only one of dozens of outfits pursuing those promises. Over the past 20 years. start-ups such as Rigetti and giants such as IBM and Google have invested big money in quantum computing—$1.2 billion from venture capitalists in 2023 alone. Universities and government laboratories around the world have also thrown serious resources behind the idea.

But the field’s central tension is not how loudly it can dream. It’s whether today’s quantum machines can grow into something that performs useful work—without falling apart.

Quantum computing is reaching its make-or-break moment. scientists hope: the next few decades may bring the scaling needed for real breakthroughs and for quantum systems to finally beat classical machines at useful tasks. If researchers can do that, quantum computers could change the world in all kinds of ways. If they can’t. the hype will keep colliding with the same obstacle—what quantum computers can and cannot do. once the lab turns into a machine meant to run.

What makes a quantum computer quantum is not just that it’s governed by quantum physics. That would describe any computer built from matter. Nor is it enough to say the device takes advantage of quantum phenomena. Conventional computers also rely on quantum physics to understand how their silicon transistors work.

To pinpoint the difference, you end up back in a box.

In Erwin Schrödinger’s 1930s thought experiment. a cat is sealed in a box with a lump of radioactive metal. a vial of poison. and a contraption that smashes the vial if it detects radiation. Quantum physics dictates that after a certain amount of time there’s a 50–50 chance the metal has emitted radioactivity. But until someone measures what happened. the lump exists in a superposition—a state where it is both emitted and not emitted. That same superposition feeds into the vial and the cat. leaving the cat in an equal superposition of dead and alive until the box is opened.

If the box is opened at different times, the probabilities shift. Immediately before opening, the cat’s superposition can tilt—more “aliveness” than “deadness,” depending on the waiting period. That ability to place a system into tunable superpositions is at the heart of what makes a quantum computer more than a conventional one with fancy parts.

Conventional computers use bits that can be in one of two states: zero or one. Quantum computers use quantum bits, or qubits, which have more options because they can be in superpositions of zero and one. And the other required ingredient is entanglement.

In Schrödinger’s story. the cat becomes entangled with the rest of the box—tied up with the metal lump. the detector contraption. and the poison. A quantum computer has to entangle its qubits too, to perform calculations. The entanglement must be controlled: which qubits are entangled, how much, and in what way.

That controlled combination—qubits in carefully managed superposition and entanglement—is what enables quantum computers’ most famous trick.

One widely touted possibility is factoring very large numbers faster than a standard computer. using Shor’s algorithm. named after Massachusetts Institute of Technology theoretical computer scientist Peter Shor. who invented it in 1994. The word “faster” is an understatement. Theoretically, Shor’s algorithm could factor in several days a number that would take a nonquantum supercomputer millions of years.

This is not just a math flex. The difficulty of factoring large numbers underpins modern encryption, especially the encryption used on the web. A quantum computer capable of running Shor’s algorithm wouldn’t only be a code breaker—it could potentially break the cryptography that underpins the entire Internet. That’s one reason governments have treated quantum computing as a priority for their security apparatuses.

There’s another pitch, one tied to fundamental science. Quantum computers might be able to mimic nature by modeling the interactions of atoms and molecules with detail that regular computers—lacking the same “quantumness” as the systems they model—can’t match. If that works. the field points to breakthroughs in basic physics and chemistry. and applied research in materials science. pharmaceutical drugs. and other areas. Some advocates, including Kulkarni, extend that ambition to familiar problems too, including simulating financial markets and Earth’s climate.

Yet every promise loops back to one harsh reality: quantum states don’t stay obedient.

In Schrödinger’s experiment, entanglement happens without effort—objects become entangled with each other when they interact. In a quantum computer, entanglement is supposed to be deliberate and controlled. The rest of the world doesn’t cooperate.

The villain is decoherence, the natural entanglement of a quantum system with its environment. A quantum processor has to stay isolated while it does its work. It must maintain control over the physical interactions among the atomic components of its own qubits. That is difficult even for fractions of a second. Preventing unwanted interactions among a quantum computer’s components is one of the prime hurdles between today’s relatively modest quantum computers and the larger systems engineers want.

One main strategy is to make the system very, very cold. Heat—the random motion of atoms—creates unintended entanglements.

The field’s machines offer a glimpse of how this challenge plays out in practice. The Chuang-tzu 2.0, a two-dimensional superconducting quantum computer, uses 78 qubits in its calculations. It was built at the Chinese Academy of Sciences Institute of Physics.

Scaling up is also tied to a bigger open question: what’s the best way to make a qubit?

Bits in standard computers are made through voltage changes on small electronic gates in a solid-state chip or through magnetic domains on the disk of a hard drive. But qubits have to be even more finely controlled. A quantum computer must place its qubits into a specific initial quantum state. control their entanglement through sequences of quantum logic gates. maintain isolation from the environment. and prevent qubits from interacting with each other—or with other parts of the computer—in unwanted ways.

That challenge once looked so severe that for years after quantum computers were first proposed in the 1980s, some experts were skeptical they could be built. A minority of researchers still believe that usefully large quantum computers cannot be built.

In the past 20 years. though. scientists have developed working quantum computers—relatively small ones. with no more than several hundred qubits. For now. those machines are too simple to perform interesting calculations like Shor’s algorithm or advanced quantum simulations except on small example problems. Scaling them up means making a bet.

As Alaina Green, a physicist at the University of Maryland’s Joint Quantum Institute, puts it: “There are a lot of ways to do it, and everyone thinks that they have the best way.”

Two leading qubit approaches dominate discussions: superconducting circuits and trapped atoms or ions. Superconducting qubits are microscopic electronic circuits made from superconducting materials such as aluminum or tantalum that have no electrical resistance at supercold temperatures. Their advantage is that they can be built using variations on existing technology for computer-chip fabrication. and they can work fast.

Their disadvantage is measured in reality rather than theory: each chip is made of billions of trillions of atoms. Even at a hundredth of a degree above absolute zero, having that many atoms around means the chips decohere in tens of microseconds.

The other strategy traps individual atoms or ions. It shines in the exact place superconducting qubits struggle. With only one atom involved, it’s easier to keep it from decohering. Trapped atom or ion qubits can be kept coherent for milliseconds at a time—but individual atoms are slower to work with. and engineers can’t piggyback on conventional computer-chip-fabrication technology.

For now, both approaches can perform roughly the same number of calculations before they decohere, at least for the systems built today. Although the most powerful quantum computers today use superconducting qubits, atom and ion approaches are not far behind.

Even with better qubits and colder hardware, errors inevitably creep in. Decoherence and other unwanted quantum effects will still occur. The field’s answer is quantum error correction.

Quantum error correction is supposed to let computers detect and correct an unwanted error without totally destroying the qubit’s superposition or its entanglement with other qubits—like changing the mix of deadness and aliveness in Schrödinger’s cat without opening the box.

That idea makes quantum computing more feasible in principle. But it has a cost that goes straight to the scaling test. Quantum error correction requires building groups of qubits into “logical qubits. ” with each logical qubit made of many actual physical qubits so that a single physical-qubit error matters less. For error correction to work well, each logical qubit needs around 100 to 1,000 physical qubits. And to run Shor’s algorithm on an interesting problem—or nearly any other useful application—a quantum computer must have thousands of logical qubits.

So the scaling requirement is brutal: today’s systems with a few hundred physical qubits must grow into systems with millions of physical qubits, with entanglement controlled well enough to run long computations.

Recent breakthroughs have offered some researchers hope that quantum error correction might be possible with fewer physical qubits. In one recent study. researchers at the California Institute of Technology and quantum computing start-up Oratomic proposed a method for quantum error correction requiring only about five physical qubits for each logical qubit. lowering the threshold for implementation of Shor’s algorithm to around 10. 000 qubits.

The study has not yet gone through peer review, but if the results hold, it could make it possible to run Shor’s algorithm sooner than expected.

Even if that timeline shifts, the central question remains unchanged: how long will it take to scale quantum computers to the point where they are useful?

Conventional computers have grown in power over the last 60 years in line with Moore’s law—Gordon Moore’s prediction that the number of transistors on a chip would double roughly every two years. Quantum computing may or may not follow a similar exponential trajectory. Moore’s law isn’t a law of nature. Decoherence, however, is an inevitability.

The field’s focus on Shor’s algorithm also depends on something else: the availability of algorithms that offer genuine speedups. Shor’s algorithm is the only one scientists are sure will do something far faster than a conventional computer. Green says she has “been looking for other algorithms that are like [Shor’s] for a long time. and they haven’t found any.” “Like. none.”.

Designing quantum algorithms is hard. Proving they’re significantly better than existing nonquantum algorithms is harder. Proving they beat any conceivable nonquantum algorithm is “generally extremely difficult, if not impossible,” the story says.

So where does the optimism live?

Many researchers are most optimistic about using quantum computers to simulate quantum aspects of nature. Ewin Tang. a quantum computer scientist at the University of California. Berkeley. argues that was the original reason quantum computers were proposed. But Tang also cautions that even with quantum simulation, quantum computers might not beat classical ones. “There aren’t that many superconcrete plans for what one would do with a quantum computer that gives a provable quantum advantage. ” he says.

Green agrees about the difficulty of proof, while still believing the outcome should hold. “Unfortunately, it’s harder to make provable statements about quantum simulation definitely being able to improve over classical computing,” she says. “But we think it should be true.”

There’s also a limit to what “advantage” means in everyday life. Even if quantum computers outperform classical machines for simulating certain quantum systems, and even if scientists find more algorithms like Shor’s, quantum computers still won’t beat conventional computers at most tasks.

William Oliver, a professor of electrical engineering, computer science and physics at M.I.T. and co-founder of a quantum computing start-up recently acquired by Google. is blunt: “The idea that quantum computers can do anything faster than classical computers—that is just simply not true.” “There are only certain problems. which have a certain internal structure to them. as we understand it today. that allow a quantum computer to take advantage of its quantumness.”.

In the best case. quantum computers will be specialized devices used in a data center or supercomputing cluster alongside conventional computers—not gadgets miniaturized for daily use. Green says. “The things that quantum computing is good at are just not things that people need to do every day.” She adds a personal-looking timeline: “In 30 years you might have a prescription drug in your medicine cabinet that was developed by models run on quantum computers. ” but “you almost certainly won’t have a quantum computer of your own.”.

Yet money and forecasts keep moving ahead of certainty. The Boston Consulting Group projected in 2024 that quantum computers would generate $450 billion to $850 billion in value by 2040. The report’s writers said, “Impediments to quantum computing in the near term … do not threaten the long-term development of the technology or the market. ” while also adding that its 2021 forecast about value creation based on quantum hardware and software improvements was overly “optimistic.”.

The confidence is hard to take seriously, in part because the field remains young and filled with unknowns. Oliver says. “Quantum computing is real. it’s happening. and it’s going to take time.” “It’s going to take engineering. and there’s still science to do as well. It’s not all buttoned up.” He estimates that larger-scale quantum computers may arrive in about 20 years. “Whatever that time frame is. we will be using them to better understand. from a scientific standpoint. the world around us.”.

Green refused to make predictions when asked when good quantum computers would arrive. “I don’t know. and I’m unwilling to make a prediction. and I would be very surprised if you found any [physicist] who would.” Even so. she’s drawn to the stakes: “There is a class of problems … [that] we have no chance of ever solving with classical computers.” “For me. the most promising application of quantum computing is the prospect of potentially solving those problems.”.

Right now, the field’s future can’t be secured by enthusiasm alone. Quantum computing may eventually match its promise—but today it’s still a question of scaling fragile quantum behavior into machines powerful enough to deliver on specific, proven gains.

What’s clear is that the field is exciting, the scientific challenges are real, and the timing is uncertain. Anyone who claims they know exactly what will happen with quantum computers is, in this moment, likely selling something.

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