Quantum computing
SVEN HOPPE/DPA/ALAMY
spotlight
Bavarian science minister Markus Blume views part of a quantum computer with Dieter Kranzlmüller (left) at the Leibniz Supercomputing Center.
THE RACE TO FIND QUANTUM
COMPUTING’S SWEET SPOT
When quantum machines are finally able to challenge conventional
computers, where will they have most impact? By Michael Brooks
M
ost researchers have never seen
a quantum computer. Winfried
Hensinger has five. “They’re all
terrible,” he says. “They can’t do
anything useful.”
In fact, all quantum computers could be
described as terrible. Decades of research
have yet to yield a machine that can kick off
the promised revolution in computing. But
enthusiasts aren’t concerned —and development is proceeding better than expected,
researchers say.
“I’m not trying to take away from how
much work there is to do, but we’re surprising
ourselves about how much we’ve done,” says
Jeannette Garcia, senior research manager for
quantum applications and software at technology giant IBM in San Jose, California.
Hensinger, a physicist at the University of
Sussex in Brighton, UK, published a proof of
principle in February for a large-scale, modular
quantum computer1. His start-up company,
Universal Quantum in Haywards Heath, UK,
is now working with engineering firm RollsRoyce in London and others to begin the long
and arduous process of building it.
If you believe the hype, computers that
exploit the strange behaviours of the atomic
realm could accelerate drug discovery, crack
encryption, speed up decision-making in
financial transactions, improve machine
learning, develop revolutionary materials and
even address climate change. The surprise is
that those claims are now starting to seem a
lot more plausible — and perhaps even too
conservative.
According to computational mathematician Steve Brierley, whatever the quantum
sweet spot turns out to be, it could be more
spectacular than anything we can imagine
today — if the field is given the time it needs.
“The short-term hype is a bit high,” says
.
d
e
v
r
e
s
e
r
s
t
h
g
i
r
l
l
A
.
d
e
t
i
m
i
L
e
r
u
t
a
N
r
e
g
n
i
r
p
S
3
2
0
2
©
Nature | Vol 617 | 25 May 2023 | S1
Quantum computing
spotlight
Brierley, who is founder and chief executive
of quantum-computing firm Riverlane in
Cambridge, UK. “But the long-term hype is
nowhere near enough.”
Justified scepticism
Until now, there has been good reason to be
sceptical. Researchers have obtained only
mathematical proofs that quantum computers will offer large gains over current, classical computers in simulating quantum physics
and chemistry, and in breaking the public-key
cryptosystems used to protect sensitive communications such as online financial transactions. “All of the other use cases that people
talk about are either more marginal, more
speculative, or both,” says Scott Aaronson, a
computer scientist at the University of Texas
at Austin. Quantum specialists have yet to
achieve anything truly useful that could not
be done using classical computers.
The problem is compounded by the
difficulty of building the hardware itself.
Quantum computers store data in quantum
binary digits called quantum bits, or qubits,
that can be made using various technologies,
including superconducting rings; optical
traps; and photons of light. Some technologies require cooling to near absolute zero, others operate at room temperature. Hensinger’s
blueprint is for a machine the size of a football pitch, but others could end up installed
in cars. Researchers cannot even agree on
how the performance of quantum computers
should be measured.
Whatever the design, the clever stuff happens when qubits are carefully coaxed into
‘superposition’ states of indefinite character — essentially a mix of digital ones and
zeroes, rather than definitely being one or
the other. Running algorithms on a quantum
computer involves directing the evolution of
these superposition states. The quantum rules
of this evolution allow the qubits to interact to
perform computations that are, in practical
terms, impossible using classical computers.
That said, useful computations are possible only on quantum machines with a huge
number of qubits, and those do not yet exist.
What’s more, qubits and their interactions
must be robust against errors introduced
through the effects of thermal vibrations, cosmic rays, electromagnetic interference and
other sources of noise. These disturbances can
cause some of the information necessary for
the computation to leak out of the processor,
a situation known as decoherence. That can
mean dedicating a large proportion of the
qubits to error-correction routines that keep
a computation on track.
This is where the scepticism about quantum
“We can unlock extra
performance in the
hardware, and make it do
things people didn’t expect.”
Yet, such calculations also offer a source of
optimism. Although 20 million qubits looks
out of reach, it’s a lot less than the one billion
qubits of previous estimates4. And researcher
Michael Beverland at Microsoft Quantum, who
was first author of the 2022 preprint2, thinks
that some of the obstacles facing quantum
chemistry calculations can be overcome
through hardware breakthroughs.
For instance, Nicole Holzmann, who leads the
applications and algorithms team at Riverlane,
and her colleagues have shown that quantum
algorithms to calculate the ground-state energies of around 50 orbital electrons can be made
radically more efficient5. Previous estimates
of the runtime of such algorithms had come
in at more than 1,000 years. But Holzmann
and her colleagues found that tweaks to the
routines — altering how the algorithmic tasks
are distributed around the various quantum
logic gates, for example — cut the theoretical
runtime to just a few days. That’s a gain in speed
of around five orders of magnitude. “Different
options give you different results,” Holzmann
says, “and we haven’t thought about many of
these options yet.”
Quantum hop
At IBM, Garcia is starting to exploit these
gains. In many ways, it’s easy pickings: the
potential quantum advantage isn’t limited to calculations involving vast arrays of
molecules.
One example of a small-scale but classically intractable computation that might be
possible on a quantum machine is finding the
energies of ground and excited states of small
photoactive molecules, which could improve
lithography techniques for semiconductor
.
d
e
v
r
e
s
e
r
s
t
h
g
i
r
l
l
A
.
d
e
t
i
m
i
L
e
r
u
t
a
N
r
e
g
n
i
r
p
S
3
2
0
2
©
S2 | Nature | Vol 617 | 25 May 2023
computing begins. The world’s largest quantum computer in terms of qubits is IBM’s
Osprey, which has 433. But even with 2 million
qubits, some quantum chemistry calculations
might take a century, according to a 2022 preprint2 by researchers at Microsoft Quantum
in Redmond, Washington, and ETH Zurich in
Switzerland. Research published in 2021 by
scientists Craig Gidney at Google in Santa Barbara, California, and Martin Ekerå at the KTH
Royal Institute of Technology in Stockholm,
estimates that breaking state-of-the-art cryptography in 8 hours would require 20 million
qubits3.
manufacturing and revolutionize drug design.
Another is simulating the singlet and triplet
states of a single oxygen molecule, which is
of interest to battery researchers.
In February, Garcia’s team published6 quantum simulations of the sulfonium ion (H3S+).
That molecule is related to triphenyl sulfonium (C18H15S�), a photo-acid generator used
in lithography that reacts to light of certain
wavelengths. Understanding its molecular
and photochemical properties could make
the manufacturing technique more efficient,
for instance. When the team began the work,
the computations looked impossible, but
advances in quantum computing over the
past three years have allowed the researchers
to perform the simulations using relatively
modest resources: the H3S+ computation ran
on IBM’s Falcon processor, which has just
27 qubits.
Part of the IBM team’s gains are the result of
measures that reduce errors in the quantum
computers. These include error mitigation, in
which noise is cancelled out using algorithms
similar to those in noise-cancelling headphones, and entanglement forging, which
identifies parts of the quantum circuit that can
be separated out and simulated on a classical
computer without losing quantum information. The latter technique, which effectively
doubles the available quantum resources, was
invented only last year7.
Michael Biercuk, a quantum physicist at
the University of Sydney in Australia, who is
chief executive and founder of Sydney-based
start-up firm Q-CTRL, says such operational
tweaks are ripe for exploration. Biercuk’s
work aims to dig deeper into the interfaces
between the quantum circuits and the classical computers used to control them, as well
as understand the details of other components that make up a quantum computer.
There is a “lot of space left on the table”, he
says; early reports of errors and limitations
have been naive and simplistic. “We are seeing
that we can unlock extra performance in the
hardware, and make it do things that people
didn’t expect.”
Similarly, Riverlane is making the daunting requirements for a useful quantum computer more manageable. Brierley notes that
drug discovery and materials-science applications might require quantum computers
that can perform a trillion decoherence-free
operations by current estimates — and that’s
good news. “Five years ago, that was a million
trillion,” he says.
Some firms are so optimistic that they are
even promising useful commercial applications in the near future. Helsinki-based
start-up Algorithmiq, for instance, says it will
IBM RESEARCH/SPL
A circuit design for IBM’s five-qubit superconducting quantum computer.
be able to demonstrate practical quantum
advances in drug development and discovery in five years’ time. “We’re confident about
that,” says Sabrina Maniscalco, Algorithmiq’s
co-founder and chief executive, and a physicist
at the University of Helsinki.
The long game
Maniscalco is just one of many who think that
the first commercial applications of quantum
computing will be in speeding up or gaining
better control over molecular reactions. “If
anything is going to give something useful in
the next five years, it will be chemistry calculations,” says Ronald de Wolf, senior researcher
at CWI, a research institute for mathematics and computer science in Amsterdam.
That’s because of the relatively low resource
requirements, adds Shintaro Sato, head of the
Quantum Laboratory at Fujitsu Research in
Tokyo. “This would be possible using quantum
computers with a relatively small number of
qubits,” he says.
Financial applications, such as risk management, as well as materials science and logistics optimization also have a high chance of
benefiting from quantum computation in the
near term, says Biercuk. Still, no one is taking
their eyes off the longer-term, more speculative applications — including quantum versions of machine learning.
“There’s not going to be
this one point when
suddenly all problems
can be solved.”
Machine-learning algorithms perform tasks
such as image recognition by finding hidden
structures and patterns in data, then creating
mathematical models that allow the algorithm
to recognize the same patterns in other data
sets. Success typically involves vast numbers
of parameters and voluminous amounts of
training data. But with quantum versions of
machine learning, the huge range of different
states open to quantum particles means that
the routines could require fewer parameters
and much less training data.
In exploratory work with South Korean
car manufacturer Hyundai, Jungsang Kim at
Duke University in Durham, North Carolina,
and researchers at the firm IonQ in College
Park, Maryland, developed quantum
machine-learning algorithms that can tell
the difference between ten road signs in laboratory tests (see go.nature.com/42tt7nr).
Their quantum-based model used just 60
parameters to achieve the same accuracy
as a classical neural network using 59,000
parameters. “We also need far fewer training
iterations,” Kim says. “A model with 59,000
parameters requires at least 100,000 training data sets to train it. With quantum, your
number of parameters is very small, so your
training becomes extremely efficient as well.”
Quantum machine learning is nowhere near
being able to outperform classical algorithms,
but there is room to explore, Kim says.
In the meantime, this era of quantum inferiority represents an opportunity to validate
the performance of quantum algorithms and
machines against classical computers, so that
researchers can be sure about what they are
delivering in the future, Garcia says. “That is
what will give us confidence when we start
pushing past what is classically possible.”
For most applications, that won’t be any
time soon. Silicon Quantum Computing, a
Sydney-based start-up, has been working
closely with finance and communications
firms and anticipates many years to go before
payday, says director Michelle Simmons, who
is also a physicist at the University of New
South Wales in Sydney.
That’s not a problem, Simmons adds: Silicon
Quantum Computing has patient investors.
So, too, does Riverlane, says Brierley. “People
do understand that this is a long-term play.”
And despite all the hype, it’s a slow-moving
one as well, Hensinger adds. “There’s not
going to be this one point when suddenly we
have a rainbow coming out of our lab and all
problems can be solved,” he says. Instead,
it will be a slow process of improvement,
spurred on by fresh ideas for what to do with
the machines — and by clever coders developing new algorithms. “What’s really important right now is to build a quantum-skilled
workforce,” he says.
Michael Brooks is a science writer based in
Lewes, UK.
1. Akhtar, M. et al. Nature Commun. 14, 531 (2023).
2. Beverland, M. E. et al. Preprint at
https://arxiv.org/abs/2211.07629 (2022).
3. Gidney, C. & Ekerå, M. Quantum 5, 433 (2021).
4. Gheorghiu, V. & Mosca, M. Preprint at
https://arxiv.org/abs/1902.02332 (2019).
5. Blunt, N. S. et al. J. Chem. Theory Comput. 18,
7001–7023 (2022).
6. Motta, M. et al. Chem. Sci. 14, 2915–2927 (2023).
7. Eddins, A. et al. PRX Quantum 3, 010309 (2022).
.
d
e
v
r
e
s
e
r
s
t
h
g
i
r
l
l
A
.
d
e
t
i
m
i
L
e
r
u
t
a
N
r
e
g
n
i
r
p
S
3
2
0
2
©
Nature | Vol 617 | 25 May 2023 | S3