YSM Issue 90.1

physics
FOCUS
In 2011, Canadian tech company
D-Wave stunned the world by announcing
that it would market a
functioning quantum computer. Soon,
companies ranging from Google to NASA
bought versions of the device, and scientists
began scrambling to evaluate what
potentially was the biggest technological
breakthrough of the century. One
third-party test, in which the new quantum
computer solved a complex math
problem 3,600 times faster than a cutting-edge
IBM supercomputer, seemed
to substantiate D-Wave’s claims of quantum
computation. Other tests found
no evidence of quantum activity at all.
Quantum computing, an idea which has
captivated physicists and computer scientists
alike since its conception in the 1980s,
has proven difficult to realize in practice.
Because quantum computers rely on the
uncertainty built into the laws of quantum
physics, they are extremely sensitive to
their environments. A small imperfection
in even a single component of the design
can be devastating. One technical challenge
is that heat energy can disrupt the
fragile quantum states, so quantum technology
is usually cooled almost to absolute
zero (-273 degrees Celsius). D-Wave’s
quantum computer is small enough to
hold in the palm of your hand but has to
be housed in a 10-foot-tall refrigerator.
Yale researchers, led by Professor of
Electrical Engineering and Physics Hong
Tang, have developed a new version of a
device called a piezo-optomechanical resonator
that could allow quantum computers
to operate at higher temperatures. The
paper, which is co-authored by graduate
students Xu Han and Chang-Ling Zou, describes
an improved method of connecting
information in physical and electrical
domains. This advance could be used as
the basis for reliable memory storage for
quantum computers—an important step
towards stronger quantum computing.
From Schrodinger’s cat to national
security
Quantum computing fundamentally
differs from classical computing in that
it relies on the non-intuitive quantum
properties of light and matter. In familiar
classical computation, information is
stored as bits which can take on the values
0 and 1—they are simple on/off electrical
switches, and it is easy to check their
positions. The computer then performs
tasks using sequences of logical operations
on the bits. For example, it might
say that if bit A is 0, then bit B should
be set to 0, but if bit A is 1, then bit B
should be set to 1; or that bit C should be
set to 1 only if bits A and B are different.
In quantum computing, by contrast, the
situation is not so straightforward. First of
all, information is stored in qubits (short
for “quantum bits”) which have more than
two possible values: 0, 1, and a combination
of 0 and 1. These qubits are particles
with distinct measurable quantum states
corresponding to “0” and “1,” but one of
the principles of quantum physics is that
sometimes we can predict the result of
a measurement only in terms of probabilities.
So in quantum mechanics, even
though sometimes we might know that
we will always measure the particle as “0,”
there can also exist a scenario in which
there is a 50 percent chance of finding the
particle in the “0” state and a 50 percent
chance of finding it in the “1” state. The
surprising part is that, mathematically
speaking, the latter particle is actually in
both states equally until we measure it as
being in one or the other, and it is meaningful
to think of such a qubit as having
value ½ representing a “mixed” state even
though ½ is not a possible measurement.
Another useful property of quantum
mechanics called entanglement links the
measurements of different particles. For
example, if particles A and B are entangled,
then we might know that whenever
we measure both particles, we will get one
“0” and one “1.” In this case, measuring
one qubit immediately determines the
value of the other, and it is possible to use
this property to “teleport” information!
The unique logical underpinnings of
quantum computation allow quantum
computer to approach old problems in
new ways. Since qubits are more complex
than regular bits, quantum algorithms are
often more streamlined than their classical
counterparts, especially when searching
for optimal solutions to problems. For
example, if we want to find a car that is
hidden behind one door out of a million, a
classical computer would have to check the
doors one by one, and, in the worst-case
scenario, it would have to make a million
queries. A quantum computer, by contrast,
can use a probabilistic algorithm to find
the car in at most only a thousand queries.
Quantum computation has potential applications
in many problems that would
take classical computers longer than the
age of the Earth. In the best-known example
of this principle of “quantum speedup,”
computer scientists have created a quantum
algorithm that can factor large numbers
(essentially a needle-in-a-haystack
problem like the car example above) exponentially
faster than is possible for any
classical algorithm. Although this problem
may not seem very exciting, it in fact underlies
many more complex processes such
as cryptography. Similar principles apply
to choosing cost-effective combinations of
building materials and even to identifying
keywords for news articles. Unsurprisingly,
quantum computation is often the best
way to model complex natural systems.
We have made significant progress over
the past few decades towards meeting the
challenges of quantum computing. As early
as the mid-1990s, we have manipulated
qubits and written codes to correct spontaneous
errors in quantum computers. In
the 2000s, we demonstrated long-distance
entanglement. In 2013, Hong Tang and his
team contributed to the corpus of knowledge
when they determined a method
for measuring quantum systems without
permanently altering them. Now, in 2016,
the Tang Lab at Yale has once again expanded
the quantum computing toolbox,
this time in the stubbornly challenging
field of information storage and transfer.
A new approach to quantum memory
You probably carry around in your
pocket a crucial piece of the new Yale
device: Smartphones contain the materials
that Tang and his team used to bridge
the mechanical-electrical gap. Piezoelectrics
are materials, usually crystals, that
accumulate charge when compressed,
twisted or bent. For instance, when
a piezoelectric sheet is creased, a net
negative charge forms at the fold, and
net positive charges form at the ends.
Conversely, when an external magnetic
field causes charges in a piezoelectric to
www.yalescientific.org
December 2016
Yale Scientific Magazine
23