REAGAN - Real-life applications with Gaussian boson sampling

Lead Research Organisation: Imperial College London
Department Name: Physics

Abstract

Since entering the information age, quantum science has already revolutionised the world in recent years. New opportunities to use the quantum advantages in real-life applications will impact on major industries across pharmaceuticals, defence, finance, and information technology. In this proposal, I aim to develop a high-dimension fully programmable Gaussian Boson Sampling (GBS) photonic processor, which is a near-term special-purpose model of photonic quantum computing serving as a promising candidate in solving real-life problems. There are two main goals here: (1) building a universal temporally encoded GBS device, and (2) using it in real-life quantum biomedical tasks. Thanks to the programmable and scalable silicon nitride photonic integrated circuit (PIC), I can build a large scale fully programmable and software scalable GBS device. The universality of this device is exhibited by supporting any arbitrary graph or Hamiltonian encoded on it, and this offers great versatility in the scope of problems that it can encode. Then, I will use this universal GBS device to solve drug design and vibronic spectra problems, and this GBS device can provide a computational speedup to perform these tasks. Many patients who have diseases like, Alzheimer's, ischaemia or cancers will be benefit from this GBS-accelerated drug design. Besides, this GBS device will provide a more efficient analysis technique for molecular spectroscopy, and enable wider researchers have a better understanding of non-Condon effects. These real-life applications can immediately stimulate the innovation of relevant biomedical and chemistry industries and research institutions, and will be adopted once maturity is reached.

Publications

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Chen K (2024) Unlocking the potential of photoexcited molecular electron spins for room temperature quantum information processing in Materials for Quantum Technology

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Yu S (2024) A von-Neumann-like photonic processor and its application in studying quantum signature of chaos. in Light, science & applications

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Yu S (2024) Shedding Light on the Future: Exploring Quantum Neural Networks through Optics in Advanced Quantum Technologies

 
Description A new application of photons to explore the topology of networks. This is important for a range of applications in science and technology, from the ways in which molecules interact to the way in which communications flow through the internet.
Exploitation Route Analysis of networks.
Sectors Digital/Communication/Information Technologies (including Software)

Environment
 
Title multi-core QPU 
Description The photonic multi-core quantum processing unit (QPU) is a scalable and programmable quantum computing architecture designed to leverage the power of multi-core photonic circuits for enhanced computational efficiency. By integrating multiple processing cores within a photonic chip, this system enables parallel quantum operations, high-speed information processing, and flexible reconfigurability, making it a promising platform for both quantum computing and quantum simulation. ? Each core is built upon a network of programmable interferometers, electro-optic modulators (EOMs), and phase shifters, allowing dynamic control over quantum states and enabling complex quantum information processing. The multi-core architecture enhances scalability by allowing multiple quantum operations to be executed simultaneously, reducing computational overhead and enabling efficient implementation of large-scale quantum algorithms. ? One of the key advantages of this system is its modular and reconfigurable design, which allows seamless integration of additional functional modules, such as in-line squeezing, non-Gaussian operations, and optical memory loops, to further expand its capabilities. The high level of programmability in this platform makes it well-suited for a wide range of applications, including quantum machine learning, quantum optimization, and topological data analysis. By harnessing the speed and coherence of photonic quantum states, the photonic multi-core QPU represents a significant step toward practical quantum computing, offering a highly flexible and efficient approach to processing quantum information at scale. 
Type Of Material Improvements to research infrastructure 
Year Produced 2025 
Provided To Others? No  
Impact The development of the photonic multi-core QPU has significant implications for both fundamental research and practical applications in quantum computing. By introducing a scalable, reconfigurable, and high-performance photonic architecture, this system enables efficient quantum information processing across a wide range of domains. Key impacts include: 1. Advancing Quantum Computing Scalability: The multi-core design allows for parallel quantum operations, overcoming traditional bottlenecks in photonic quantum computing and paving the way for large-scale implementations. 2. Enhancing Quantum Machine Learning and Simulation: The flexibility of the QPU supports advanced applications such as quantum neural networks, quantum optimization, and topological data analysis, providing new tools for solving complex computational problems. 3. Bridging Theory and Experiment: This platform enables real-world implementation of theoretical quantum algorithms, allowing researchers to test quantum models and explore new physics, including quantum entanglement, non-Gaussian state engineering, and quantum dynamics. 4. Improving Practical Quantum Applications:The modular architecture makes it possible to incorporate in-line squeezing, non-Gaussian operations, and optical memory, expanding its usability for practical quantum technologies such as secure communication, metrology, and fault-tolerant quantum computing. 5. Accelerating Industry and Academic Collaborations:This tool provides a versatile and programmable environment for cross-disciplinary research, fostering collaboration between quantum computing, photonics, and artificial intelligence communities. By offering a highly adaptable and powerful quantum platform, the photonic multi-core QPU pushes the boundaries of photonic quantum computing, making quantum technologies more accessible and applicable to real-world challenges. 
 
Description QONN 
Organisation National University of Singapore
Country Singapore 
Sector Academic/University 
PI Contribution In our recent work on quantum optical neural networks, my primary contribution was in designing the experimental architecture. I developed the framework for implementing the neural network using a photonic quantum processor, ensuring that the system could effectively integrate key quantum optical components such as programmable interferometers, nonlinear activation mechanisms, and photon-based encoding schemes. Additionally, I contributed to defining the theoretical model and outlining the necessary experimental conditions to achieve efficient quantum learning and processing. While I did not conduct the physical experiment, my work laid the foundation for the practical realization and future scalability of the quantum optical neural network.
Collaborator Contribution In this collaboration, contributions from my collaborator in NUS mainly focused on the theoretical analysis and complexity evaluation of the system. They provided insights into the computational complexity of quantum optical neural networks, helping to establish their potential advantages over classical counterparts. Additionally, they contributed to the formulation of the theoretical framework, analyzing how quantum resources-such as entanglement, squeezing, and non-Gaussian operations-affect the network's expressivity and learning efficiency. Their work also included evaluating the scalability and feasibility of implementing complex quantum neural architectures within realistic photonic hardware constraints. These contributions were essential in guiding the experimental design and optimizing the network's performance for practical quantum machine learning applications.
Impact We have published a paper titled "Shedding light on the future: Exploring quantum neural networks through optics" in Advanced Quantum Technologies.
Start Year 2024
 
Description QTDA 
Organisation Queen Mary University of London
Country United Kingdom 
Sector Academic/University 
PI Contribution In our recent experiment on quantum topological network analysis, my primary contributions included designing and implementing the photonic quantum computing architecture, developing the experimental setup, and conducting the quantum sampling process. I played a key role in encoding network structures into the photonic quantum processor and utilizing Gaussian Boson Sampling (GBS) to extract relevant topological features. Additionally, I contributed to the development of data analysis techniques to interpret the quantum sampling results and their connection to network topology. This work not only demonstrated the practical application of photonic quantum computing in complex network analysis but also helped establish a novel approach to studying topological phase transitions using quantum photonics.
Collaborator Contribution In this project, my collaborators provided valuable expertise in the theoretical and computational aspects of network science. Their contributions were instrumental in defining the network models used in our study, identifying relevant topological features, and developing analytical methods to interpret the quantum sampling results. Additionally, they played a key role in formulating the connection between network topology and the photonic quantum computing framework, helping to refine the methodologies for extracting meaningful insights from our experimental data. This collaboration significantly enhanced the interdisciplinary impact of our work, bridging quantum photonics with complex network analysis.
Impact We wrote a journal paper titled "topological network analysis using a programmable photonic quantum processor", and submit to Nature at the moment (will appear on arXiv soon).
Start Year 2024
 
Description QTDA 
Organisation University of Cambridge
Country United Kingdom 
Sector Academic/University 
PI Contribution In our recent experiment on quantum topological network analysis, my primary contributions included designing and implementing the photonic quantum computing architecture, developing the experimental setup, and conducting the quantum sampling process. I played a key role in encoding network structures into the photonic quantum processor and utilizing Gaussian Boson Sampling (GBS) to extract relevant topological features. Additionally, I contributed to the development of data analysis techniques to interpret the quantum sampling results and their connection to network topology. This work not only demonstrated the practical application of photonic quantum computing in complex network analysis but also helped establish a novel approach to studying topological phase transitions using quantum photonics.
Collaborator Contribution In this project, my collaborators provided valuable expertise in the theoretical and computational aspects of network science. Their contributions were instrumental in defining the network models used in our study, identifying relevant topological features, and developing analytical methods to interpret the quantum sampling results. Additionally, they played a key role in formulating the connection between network topology and the photonic quantum computing framework, helping to refine the methodologies for extracting meaningful insights from our experimental data. This collaboration significantly enhanced the interdisciplinary impact of our work, bridging quantum photonics with complex network analysis.
Impact We wrote a journal paper titled "topological network analysis using a programmable photonic quantum processor", and submit to Nature at the moment (will appear on arXiv soon).
Start Year 2024