MSGID: 1:317/3 64703607
PID: hpt/lnx 1.9.0-cur 2019-01-08
TID: hpt/lnx 1.9.0-cur 2019-01-08
 Scientists propose revolution in complex systems modelling with quantum
technologies 

  Date:
      May 25, 2023
  Source:
      University of Manchester
  Summary:
      Scientists have made a significant advancement with quantum
      technologies that could transform complex systems modelling with
      an accurate and effective approach that requires significantly
      re-duced memory.


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FULL STORY
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Scientists have made a significant advancement with quantum technologies
that could transform complex systems modelling with an accurate and
effective approach that requires significantly reduced memory.

Complex systems play a vital role in our daily lives, whether that be
predicting traffic patterns, weather forecasts, or understanding financial
markets. However, accurately predicting these behaviours and making
informed decisions relies on storing and tracking vast information from
events in the distant past -- a process which presents huge challenges.

Current models using artificial intelligence see their memory requirements
increase by more than a hundredfold every two years and can often involve
optimisation over billions -- or even trillions -- of parameters. Such
immense amounts of information lead to a bottleneck where we must
trade-off memory cost against predictive accuracy.

A collaborative team of researchers from The University of Manchester,
the University of Science and Technology of China (USTC), the Centre for
Quantum Technologies (CQT) at the National University of Singapore and
Nanyang Technological University (NTU) propose that quantum technologies
could provide a way to mitigate this trade-off.

The team have successfully implemented quantum models that can simulate
a family of complex processes with only a single qubit of memory --
the basic unit of quantum information -- offering substantially reduced
memory requirements.

Unlike classical models that rely on increasing memory capacity as more
data from past events are added, these quantum models will only ever
need one qubit of memory.

The development, published in the journal Nature Communications,
represents a significant advancement in the application of quantum
technologies in complex system modelling.

Dr Thomas Elliott, project leader and Dame Kathleen Ollerenshaw Fellow at
The University of Manchester, said: "Many proposals for quantum advantage
focus on using quantum computers to calculate things faster. We take
a complementary approach and instead look at how quantum computers can
help us reduce the size of the memory we require for our calculations.

"One of the benefits of this approach is that by using as few qubits
as possible for the memory, we get closer to what is practical with
near-future quantum technologies. Moreover, we can use any extra qubits
we free up to help mitigate against errors in our quantum simulators."
The project builds on an earlier theoretical proposal by Dr Elliott and
the Singapore team. To test the feasibility of the approach, they joined
forces with USTC, who used a photon-based quantum simulator to implement
the proposed quantum models.

The team achieved higher accuracy than is possible with any classical
simulator equipped with the same amount of memory. The approach can be
adapted to simulate other complex processes with different behaviours.

Dr Wu Kang-Da, post-doctoral researcher at USTC and joint first author
of the research, said: "Quantum photonics represents one of the least
error-prone architectures that has been proposed for quantum computing,
particularly at smaller scales. Moreover, because we are configuring
our quantum simulator to model a particular process, we are able to
finely-tune our optical components and achieve smaller errors than
typical of current universal quantum computers."  Dr Chengran Yang,
Research Fellow at CQT and also joint first author of the research, added:
"This is the first realisation of a quantum stochastic simulator where the
propagation of information through the memory over time is conclusively
demonstrated, together with proof of greater accuracy than possible with
any classical simulator of the same memory size."  Beyond the immediate
results, the scientists say that the research presents opportunities
for further investigation, such as exploring the benefits of reduced
heat dissipation in quantum modelling compared to classical models.

Their work could also find potential applications in financial modelling,
signal analysis and quantum-enhanced neural networks.

Next steps include plans to explore these connections, and to scale
their work to higher-dimensional quantum memories.

    * RELATED_TOPICS
          o Computers_&_Math
                # Quantum_Computers # Computers_and_Internet #
                Spintronics_Research # Encryption # Computer_Modeling #
                Hacking # Computer_Science # Mathematical_Modeling
    * RELATED_TERMS
          o Quantum_entanglement o Mathematical_model o
          Introduction_to_quantum_mechanics o Technology o MRAM o
          Quantum_computer o Security_engineering o Computer_simulation

==========================================================================
Story Source: Materials provided by University_of_Manchester. Original
written by Jessica Marsh. Note: Content may be edited for style and
length.


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Journal Reference:
   1. Kang-Da Wu, Chengran Yang, Ren-Dong He, Mile Gu, Guo-Yong Xiang,
   Chuan-
      Feng Li, Guang-Can Guo, Thomas J. Elliott. Implementing
      quantum dimensionality reduction for non-Markovian stochastic
      simulation. Nature Communications, 2023; 14 (1) DOI:
      10.1038/s41467-023-37555-0
==========================================================================

Link to news story:
https://www.sciencedaily.com/releases/2023/05/230525140312.htm

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