With the help of quantum technology, scientists have made substantial progress that could change the way complex systems are modelled using a precise and efficient method that uses a lot less memory.
Whether it’s forecasting weather, traffic patterns, or financial markets, complex systems are essential to our daily lives. To effectively predict these behaviors and make wise decisions, it is necessary to store and track a great amount of data from long-ago occurrences, but this process is extremely difficult.
The amount of memory needed by current artificial intelligence models doubles every two years and frequently involves optimisation across trillions or even billions of parameters. Due to the bottleneck caused by the enormous amount of information, we must trade off memory usage and prediction 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.
With just one qubit of memory the fundamental unit of quantum information the team has successfully constructed quantum models that can simulate a family of complex processes with far less memory usage.
These quantum models will never require more than one qubit of memory, in contrast to classical models that depend on memory size growing as more information about previous events is added.
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. Wu Kang-Da
The discovery, which was reported in the journal Nature Communications, marks a noteworthy development in the use of quantum technology for complex system modeling.
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. They collaborated with USTC, which implemented the suggested quantum models using a photon-based quantum simulator, to assess the viability of the strategy.
The team’s accuracy was higher than what could have been accomplished with a traditional simulator with the same memory. The method can be modified to model various intricate systems with distinct behaviors.
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 effects, the researchers claim that the findings offers prospects for additional study, such as examining the advantages of quantum modeling over conventional modeling in terms of reduced heat loss. Additionally, their work may have applications in signal processing, quantum-enhanced neural networks, and financial modeling.
The following steps will involve exploring these links and expanding their research to higher-dimensional quantum memories.
