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