YSM Issue 93.2

FOCUS
Physics
THE PROMISE OF TWO-COPPER-PAIR TUNNELING
BY SHOUMIK CHOWDHURY
Every day, hundreds of Yale students
take classes in Davies Auditorium and
work in the Center for Engineering,
Innovation, and Design. Likely only several
will know that just a few stories above them—
on the 4th floor of Becton Center—reside
some of the world’s most powerful quantum
computers. These devices are housed in
the Yale Quantronics Laboratory—Qulab
for short—and are operated by cooling
superconducting circuits in microwave
cavities down to millikelvin temperatures, at
which point their behavior is aptly described
by the laws of quantum mechanics.
First proposed in the 1960s by physicist
Richard Feynman, using quantum
mechanical systems (such as atoms) for
computation is not a new concept. Much of
the progress in experimentally implementing
these devices, however, has come in the last
twenty years, with superconducting circuits
(behaving as artificial atoms) emerging as a
leading platform for quantum information
processing. Unfortunately, quantum bits,
or qubits, built from these circuits are still
highly sensitive to various types of noise
from the environment. This has driven
widespread effort in the field to build better
qubits. Recently, a team of researchers at
Qulab—led by principal investigator Michel
Devoret and graduate student Clarke
Smith—designed a new type of protected
superconducting qubit that is robust at the
hardware-level against several different
noise channels.
Notions of Quantum Computing
Quantum computers are based on a
fundamentally different set of rules than
so-called classical computers—a broad
label characterizing most devices in use
today. Classical data are stored in bits,
and a single binary digit can take on two
logical values: 0 or 1. In practice, this could
be realized by the passage of current, and
the lack thereof, through a wire, or by the
magnetization state of a small region of a
hard drive. At the lowest level, under many
layers of abstraction, all classical algorithms
and operations reduce to manipulating
some pattern of bit strings from an input
state to an output state. The key takeaway
here is that bits take on definite values.
In contrast, quantum computers encode
information in the quantum states of
a system—for instance, in the states
representing the lowest two energy levels
of an atom. These two states, which we
can abstractly label as |0> and |1>, form
what is known as qubit subspace, and by
sending appropriate pulses of light to the
atom, one can perform logical operations
on the qubit. The key difference from the
classical model, however, is that we can also
form admixtures of the two states, called
superpositions, of the form α|0> + β|1>. The
outcomes of measuring such superposition
states are determined by rules of probability,
giving either |0> and |1> with probabilities
|α|2 and |β|2 respectively. While this may
seem counterintuitive, it turns out that
several classes of problems are very wellsuited
to a quantum computer that does
not have definite 0 or 1 bits. Some notable
examples include cryptography and prime
number factorization, optimization and
machine learning, and simulating quantum
mechanical systems (such as molecules) for
applications in fundamental physics and
chemistry. However, the aforementioned
sensitivity of quantum information means
that quantum computers are also more
susceptible to noise and other errors that
arise from coupling to the environment. Any
spurious interaction can lead to unwanted
changes to the desired quantum state, and
thus introduces errors into a calculation.
The fragility of quantum information has
led to what Michel Devoret describes as a
two-pronged effort in the field. “The first
approach is to discover a better method
for quantum error correction … while the
second [approach] is to design physical
qubits with better lifetimes and faster
gate operations,” Devoret said. Research
into quantum error correction (QEC)
involves finding ways to encode a logical
bit of quantum information across many
physical qubits—the benefit is then that
the information becomes more robust
to noise, being distributed non-locally
across the system. However, these kinds of
QEC protocols are often very theoretical
in nature, and the authors of the present
study chose to focus on the more tractable
18 Yale Scientific Magazine September 2020 www.yalescientific.org