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Meet Willow, our state-of-the-art quantum chip
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MEET WILLOW, OUR STATE-OF-THE-ART QUANTUM CHIP
Dec 09, 2024
·
7 min read
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Our new chip demonstrates error correction and performance that paves the way to
a useful, large-scale quantum computer
H
Hartmut Neven
Founder and Lead, Google Quantum AI
Read AI-generated summary
GENERAL SUMMARY
Google has developed a new quantum chip called Willow, which significantly
reduces errors as it scales up, a major breakthrough in quantum error
correction. Willow also performed a computation in under five minutes that would
take a supercomputer 10 septillion years, demonstrating its potential for
solving complex problems beyond the reach of classical computers. This
achievement marks a significant step towards building commercially relevant
quantum computers that can revolutionize fields like medicine, energy, and AI.
Summaries were generated by Google AI. Generative AI is experimental.
BULLET POINTS
* Google's new quantum chip, Willow, is a major step towards building a useful,
large-scale quantum computer.
* Willow reduces errors exponentially as it scales up, achieving a breakthrough
in quantum error correction.
* Willow performed a benchmark computation in under five minutes that would
take a supercomputer 10 septillion years.
* Willow's performance is a sign that useful, very large quantum computers can
be built.
* Google is working on developing quantum algorithms that can solve real-world
problems.
Summaries were generated by Google AI. Generative AI is experimental.
EXPLORE OTHER STYLES:
* General summary
* Bullet points
Share
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Sorry, your browser doesn't support embedded videos, but don't worry, you can
download it and watch it with your favorite video player!
Today I’m delighted to announce Willow, our latest quantum chip. Willow has
state-of-the-art performance across a number of metrics, enabling two major
achievements.
* The first is that Willow can reduce errors exponentially as we scale up using
more qubits. This cracks a key challenge in quantum error correction that the
field has pursued for almost 30 years.
* Second, Willow performed a standard benchmark computation in under five
minutes that would take one of today’s fastest supercomputers 10 septillion
(that is, 1025) years — a number that vastly exceeds the age of the Universe.
The Willow chip is a major step on a journey that began over 10 years ago. When
I founded Google Quantum AI in 2012, the vision was to build a useful,
large-scale quantum computer that could harness quantum mechanics — the
“operating system” of nature to the extent we know it today — to benefit society
by advancing scientific discovery, developing helpful applications, and tackling
some of society's greatest challenges. As part of Google Research, our team has
charted a long-term roadmap, and Willow moves us significantly along that path
towards commercially relevant applications.
6:39
A video with Director of Quantum Hardware Julian Kelly introducing Willow and
its breakthrough achievements
EXPONENTIAL QUANTUM ERROR CORRECTION — BELOW THRESHOLD!
Errors are one of the greatest challenges in quantum computing, since qubits,
the units of computation in quantum computers, have a tendency to rapidly
exchange information with their environment, making it difficult to protect the
information needed to complete a computation. Typically the more qubits you use,
the more errors will occur, and the system becomes classical.
Today in Nature, we published results showing that the more qubits we use in
Willow, the more we reduce errors, and the more quantum the system becomes. We
tested ever-larger arrays of physical qubits, scaling up from a grid of 3x3
encoded qubits, to a grid of 5x5, to a grid of 7x7 — and each time, using our
latest advances in quantum error correction, we were able to cut the error rate
in half. In other words, we achieved an exponential reduction in the error rate.
This historic accomplishment is known in the field as “below threshold” — being
able to drive errors down while scaling up the number of qubits. You must
demonstrate being below threshold to show real progress on error correction, and
this has been an outstanding challenge since quantum error correction was
introduced by Peter Shor in 1995.
There are other scientific “firsts” involved in this result as well. For
example, it’s also one of the first compelling examples of real-time error
correction on a superconducting quantum system — crucial for any useful
computation, because if you can’t correct errors fast enough, they ruin your
computation before it’s done. And it’s a "beyond breakeven" demonstration, where
our arrays of qubits have longer lifetimes than the individual physical qubits
do, an unfakable sign that error correction is improving the system overall.
As the first system below threshold, this is the most convincing prototype for a
scalable logical qubit built to date. It’s a strong sign that useful, very large
quantum computers can indeed be built. Willow brings us closer to running
practical, commercially-relevant algorithms that can’t be replicated on
conventional computers.
10 SEPTILLION YEARS ON ONE OF TODAY’S FASTEST SUPERCOMPUTERS
As a measure of Willow’s performance, we used the random circuit sampling (RCS)
benchmark. Pioneered by our team and now widely used as a standard in the field,
RCS is the classically hardest benchmark that can be done on a quantum computer
today. You can think of this as an entry point for quantum computing — it checks
whether a quantum computer is doing something that couldn’t be done on a
classical computer. Any team building a quantum computer should check first if
it can beat classical computers on RCS; otherwise there is strong reason for
skepticism that it can tackle more complex quantum tasks. We’ve consistently
used this benchmark to assess progress from one generation of chip to the next —
we reported Sycamore results in October 2019 and again recently in October 2024.
Willow’s performance on this benchmark is astonishing: It performed a
computation in under five minutes that would take one of today’s fastest
supercomputers 1025 or 10 septillion years. If you want to write it out, it’s
10,000,000,000,000,000,000,000,000 years. This mind-boggling number exceeds
known timescales in physics and vastly exceeds the age of the universe. It lends
credence to the notion that quantum computation occurs in many parallel
universes, in line with the idea that we live in a multiverse, a prediction
first made by David Deutsch.
These latest results for Willow, as shown in the plot below, are our best so
far, but we’ll continue to make progress.
Computational costs are heavily influenced by available memory. Our estimates
therefore consider a range of scenarios, from an ideal situation with unlimited
memory (▲) to a more practical, embarrassingly parallelizable implementation on
GPUs (⬤).
Our assessment of how Willow outpaces one of the world’s most powerful classical
supercomputers, Frontier, was based on conservative assumptions. For example, we
assumed full access to secondary storage, i.e., hard drives, without any
bandwidth overhead — a generous and unrealistic allowance for Frontier. Of
course, as happened after we announced the first beyond-classical computation in
2019, we expect classical computers to keep improving on this benchmark, but the
rapidly growing gap shows that quantum processors are peeling away at a double
exponential rate and will continue to vastly outperform classical computers as
we scale up.
5:59
A video with Principal Scientist Sergio Boixo, Founder and Lead Hartmut Neven,
and renowned physicist John Preskill discussing random circuit sampling, a
benchmark that demonstrates beyond-classical performance in quantum computers.
STATE-OF-THE-ART PERFORMANCE
Willow was fabricated in our new, state-of-the-art fabrication facility in Santa
Barbara — one of only a few facilities in the world built from the ground up for
this purpose. System engineering is key when designing and fabricating quantum
chips: All components of a chip, such as single and two-qubit gates, qubit
reset, and readout, have to be simultaneously well engineered and integrated. If
any component lags or if two components don't function well together, it drags
down system performance. Therefore, maximizing system performance informs all
aspects of our process, from chip architecture and fabrication to gate
development and calibration. The achievements we report assess quantum computing
systems holistically, not just one factor at a time.
We’re focusing on quality, not just quantity — because just producing larger
numbers of qubits doesn’t help if they’re not high enough quality. With 105
qubits, Willow now has best-in-class performance across the two system
benchmarks discussed above: quantum error correction and random circuit
sampling. Such algorithmic benchmarks are the best way to measure overall chip
performance. Other more specific performance metrics are also important; for
example, our T1 times, which measure how long qubits can retain an excitation —
the key quantum computational resource — are now approaching 100 µs
(microseconds). This is an impressive ~5x improvement over our previous
generation of chips. If you want to evaluate quantum hardware and compare across
platforms, here is a table of key specifications:
Willow’s performance across a number of metrics.
WHAT’S NEXT WITH WILLOW AND BEYOND
The next challenge for the field is to demonstrate a first "useful,
beyond-classical" computation on today's quantum chips that is relevant to a
real-world application. We’re optimistic that the Willow generation of chips can
help us achieve this goal. So far, there have been two separate types of
experiments. On the one hand, we’ve run the RCS benchmark, which measures
performance against classical computers but has no known real-world
applications. On the other hand, we’ve done scientifically interesting
simulations of quantum systems, which have led to new scientific discoveries but
are still within the reach of classical computers. Our goal is to do both at the
same time — to step into the realm of algorithms that are beyond the reach of
classical computers and that are useful for real-world, commercially relevant
problems.
Random circuit sampling (RCS), while extremely challenging for classical
computers, has yet to demonstrate practical commercial applications.
We invite researchers, engineers, and developers to join us on this journey by
checking out our open source software and educational resources, including our
new course on Coursera, where developers can learn the essentials of quantum
error correction and help us create algorithms that can solve the problems of
the future.
My colleagues sometimes ask me why I left the burgeoning field of AI to focus on
quantum computing. My answer is that both will prove to be the most
transformational technologies of our time, but advanced AI will significantly
benefit from access to quantum computing. This is why I named our lab Quantum
AI. Quantum algorithms have fundamental scaling laws on their side, as we’re
seeing with RCS. There are similar scaling advantages for many foundational
computational tasks that are essential for AI. So quantum computation will be
indispensable for collecting training data that’s inaccessible to classical
machines, training and optimizing certain learning architectures, and modeling
systems where quantum effects are important. This includes helping us discover
new medicines, designing more efficient batteries for electric cars, and
accelerating progress in fusion and new energy alternatives. Many of these
future game-changing applications won’t be feasible on classical computers;
they’re waiting to be unlocked with quantum computing.
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