Dr Thomas Elliott, project leader and Dame Kathleen Olleenshaw Fellow at The University of Manchester, said: “Many proposals for quantum excellence focus on using quantum computers to calculate things more quickly. We’re taking a complementary approach and looking instead at how a quantum computer can help us reduce the size of memory we need for our calculations.
“One advantage of this approach is that by using as few qubits as possible for memory, we get closer to what is practical with quantum technologies in the future. Plus, we can use any extra qubits we free up to help reduce errors in our quantum simulators.”
This project builds on previous theoretical proposals by Dr Elliott and the Singapore team. To test the feasibility of the approach, they teamed up with USTC, which uses a photon-based quantum simulator to implement the proposed quantum model.
The team achieved higher accuracy than would be possible with any classic simulator equipped with the same amount of memory. This approach can be adapted to simulate other complex processes with different behavior.
Dr Wu Kang-Da, post-doctoral researcher at USTC and first co-author of the study, said: “Quantum photonics represents one of the most error-prone architectures that have been proposed for quantum computing, especially at smaller scales. Additionally, because we configure our quantum simulator to model specific processes, we can fine-tune our optical components and achieve smaller errors than is typical of today’s universal quantum computers.”
Dr Chengran Yang, Research Fellow at CQT and first author of the study, added: “This is the first realization of a quantum stochastic simulator where the propagation of information through memory over time is convincingly demonstrated, together with evidence of higher accuracy. than possible with any classic simulator with the same memory size.
Beyond the immediate results, the scientists say the research presents opportunities for further investigation, such as exploring the benefits of reduced heat dissipation in quantum modeling compared to classical models. Their work could also find potential applications in financial modeling, signal analysis, and quantum-enhanced neural networks.
Next steps include plans to explore these connections, and scale their work to higher dimensional quantum memory.