Tech Briefs Magazine - February 2021 - 43

pact, faster, more intuitive, and easier to
understand for programmers.
Utilizing the potential of quantum
computation not only requires the latest
technology but also a quantum programming language to describe quantum algorithms. In principle, an algorithm is a
" recipe " for solving a problem; a programming language describes the algorithm so that a computer can perform
the necessary calculations.
Today, quantum programming languages are tied closely to specific hardware; they describe precisely the behavior of the underlying circuits. For programmers, these " hardware description
languages " are cumbersome and errorprone, since the individual programming instructions must be extremely
detailed and thus explicitly describe the

minutiae needed to implement quantum algorithms.
Silq is not designed primarily around
the construction and functionality of the
hardware but on the mindset of the programmers when they want to solve a
problem, without requiring them to
understand every detail of the computer
architecture and implementation. Highlevel programming languages are more
expressive, meaning that they can describe even complex tasks and algorithms
with less code. This makes them more
comprehensible and easier to use for programmers. They can also be used with different computer architectures.
A conventional computer calculates a
task in several intermediate steps, creating
intermediate results or temporary values.
In order to relieve the memory, classical

computers automatically erase these values. Computer scientists refer to this as
" garbage collection " since the superfluous temporary values are disposed of. In
the case of quantum computers, this disposal is trickier due to quantum entanglement: the previously calculated values can
interact with the current ones, interfering
with the correct calculation. Accordingly,
cleaning up such temporary values on
quantum computers requires a more
advanced technique of so-called " uncomputation. " Silq automatically identifies
and erases values that are no longer
needed by using only programming commands that are free of any special quantum operations - they are " qfree. "
For more information, contact Florian
Meyer at florian.meyer@hk.ethz.ch; +41 44
632 33 84.

AI Improves Performance of Lithium-Ion Batteries and
Fuel Cells
Improvements could include making smartphones charge faster and increasing the time
between charges for electric vehicles.
Imperial College London, United Kingdom

new machine learning algorithm
allows researchers to explore possible
designs for the microstructure of fuel cells
and lithium-ion batteries before running
3D simulations that help researchers
make changes to improve performance.
Fuel cells use clean hydrogen fuel -
which can be generated by wind and solar
energy - to produce heat, electricity, and

lithium-ion batteries like those found in
smartphones, laptops, and electric cars.
Their performance is closely related to
their microstructure - how the pores
(holes) inside their electrodes are shaped
and arranged can affect how much power
fuel cells can generate and how quickly
batteries charge and discharge. Because
the micrometer-scale pores are so small,

their specific shapes and sizes can be difficult to study at a high enough resolution
to relate them to overall cell performance.
Researchers have applied machine
learning techniques to explore these
pores virtually and run 3D simulations to
predict cell performance based on their
microstructure. The researchers used a
novel machine learning technique called

Label = 1

Training set

Discriminator

Label = 0
Generator
D1

The structure of the machine learning algorithm and the approach it uses to learn the " essence " of the microstructural data. (Image: Sam Cooper,
Imperial College London)

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