IBM’s newest quantum-computing chip, revealed on 15 November, established a
milestone of sorts: it packs in 127 quantum bits (qubits), making it the
first such device to reach 3 digits. But the achievement is only one step in
an aggressive agenda boosted by billions of dollars in investments across
the industry.
The ‘Eagle’ chip is a step towards IBM’s goal of creating a 433-qubit
quantum processor next year, followed by one with 1,121 qubits named Condor
by 2023. Such targets echo those that for decades the electronics industry
has set itself for miniaturizing silicon chips, says Jerry Chow, head of
IBM’s experimental quantum-computing group at the Thomas J. Watson Research
Center in Yorktown Heights, New York.
Other companies — including technology behemoths Google and Honeywell, and a
slew of well-funded start-up companies — have similarly ambitious plans.
Ultimately, they aim to make quantum computers capable of performing certain
tasks that are out of reach of even the largest supercomputers that use
classical technology.
“It’s good to have ambitious goals, but what matters is whether they can
execute their plans,” says quantum information theorist John Preskill at the
California Institute of Technology in Pasadena.
Quantum advantage
By exploiting the laws of quantum physics to process binary information,
quantum-computing circuits such as the Eagle chip can already do
calculations that can’t easily be simulated on classical supercomputers.
Google famously reported achieving such a ‘quantum advantage’ in 2019, using
qubits made, like IBM’s, with superconducting loops . A team at the
University of Science and Technology of China (USTC) in Hefei last year
reported achieving quantum advantage using optical qubits; this year, it did
the same with superconducting qubits.
But the tasks these machines were given were artificial, researchers warn.
“The current state of the art is that no experiment has demonstrated quantum
advantage for practical tasks yet,” says physicist Chao-Yang Lu, who co-led
the USTC effort. Solving real-world problems such as simulating drug
molecules or materials using quantum chemistry will require quantum
computers to get drastically bigger and more powerful.
Quantum engineer Andrew Dzurak at the University of New South Wales in
Sydney, Australia, thinks that with 1,000-qubit chips such as IBM’s planned
Condor, the technology might start to prove its worth. “It’s hoped that some
useful and even commercially valuable problems can be solved using quantum
computers in this thousand-to-million-qubit range,” he says. “But to do
really paradigm-shifting stuff, you are going to need millions of physical
qubits.”
Chip challenges
The Eagle chip has almost twice as many qubits as IBM’s previous flagship
quantum circuit, the 65-qubit Hummingbird. The increase required the team to
solve several engineering problems, says Chow. To enable each qubit to
interact with several others, the researchers opted for an arrangement, in
which each is linked to two or three neighbours on a hexagonal grid. And to
allow individual control of each qubit without an unmanageable tangle of
wires, the team placed wires and other components on several stacked tiers.
Chow says that to solve this ‘packaging’ problem, the researchers drew on
experience with 3D architectures in conventional chips. He adds that it was
also crucial to find materials that would perform well at the ultralow
temperatures needed for superconducting qubits to function.
But the processing power of a quantum circuit isn’t just about how many
qubits it has. It also depends on how fast they operate and on how resistant
they are to errors that could scramble a calculation, due for example to
random fluctuations. Chow says that there’s still scope for improvement in
all these respects for superconducting qubits.
Dealing with errors is particularly difficult, because the laws of physics
prevent quantum computers from using the error-correcting methods of
classical machines, which typically require keeping multiple copies of each
bit.
Instead, researchers aim to build ‘logical qubits’ — in which almost all
errors can be identified and corrected — from complicated arrangements of
many physical qubits. The procedures so far proposed typically demand that
each logical qubit contain around 1,000 physical qubits, although that ratio
depends on the intrinsic fidelity — the error-resistance — of the physical
qubits, says Dzurak.
Error correction
Some other approaches to building quantum computers hope to benefit from
qubits with lower intrinsic error rates. That’s one potential advantage of
using trapped ions as the qubits, as is done by the company IonQ, spun out
of research at the University of Maryland in College Park, which last month
raised more than US$600 million when it became the first purely
quantum-computing company to trade publicly on the New York Stock Exchange —
a deal that valued the company at almost $2 billion. Rigetti Computing, a
start-up in Berkeley, California, also went public this year, with a
$1.5-billion valuation.
IonQ co-founder Christopher Monroe, a physicist at the University of
Maryland, and his co-workers last month reported a fault-tolerant logical
qubit made from just 13 trapped-ion qubits, although Dzurak says that its
degree of error-correction was “still quite some way from what is needed for
a useful quantum computer, which needs logical error rates well below one in
a million”.
The Google team, meanwhile, has achieved similar logical error rates using
21 superconducting qubits: again, "an important result", says Dzurak, but
still far from what is needed to crack the error-correction problem.
But Chow cautions against placing too much emphasis on attaining logical
qubits. “We won’t have a situation where we flip a switch and say
‘error-correction is on’”, he says. “Improving qubit performance is a more
important story than making logical qubits and dividing everything by a
thousand.”
Signal boost
IBM and others are trying to gain a detailed understanding of the
error-related noise in a circuit, and then to extract it — rather like noise
cancellation to improve the signal-to-noise ratio in acoustics.
Beyond Condor-level devices, Chow says, circuit designs are likely to become
modular, with several chips linked through “quantum interconnects”. It’s not
yet clear how best to do that — perhaps with the microwave-frequency signals
currently used for data input and output to superconducting qubits, or maybe
by converting the quantum information to light-based signals. “It’s an
entirely new area of research,” says Chow.
Many researchers think the first real-world applications of quantum
computers are likely to be in relatively specialized fields, such as
simulation of molecules and materials, machine learning and optimization
problems in industries including finance. To get to that stage, “I expect
we’ll see gradual improvement in performance rather than a sudden leap
forward”, says Preskill. “It is likely to be a long slog before we can run
useful applications.”
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