Physics-informed Generative Machine Learning Accelerates Quantum-Centric Supercomputing for Challenging Problems
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Quantum-centric supercomputing offers a potential route to solving complex computational problems, but relies on extracting meaningful data from inherently noisy quantum hardware. Chayan Patra, Dibyendu Mondal, and Sonaldeep Halder, along with their colleagues, address this challenge by developing a new workflow, PIGen-SQD, that combines the power of machine learning with established physics principles.
The team’s approach uses generative models, guided by insights into the underlying quantum system, to efficiently identify the most important configurations for subsequent analysis. This innovative method significantly reduces the computational cost of simulating complex systems while maintaining a high degree of accuracy, representing a substantial step towards reliable quantum simulations on future, large-scale hardware. Hybrid Quantum-Classical Electronic Structure Methods Scientists are developing innovative methods that combine the power of classical and quantum computers to solve the complex problem of determining the electronic structure of molecules. These approaches aim to improve the accuracy and efficiency of calculations, particularly for large and complex molecules where traditional methods become impractical. Researchers are exploring techniques like Configuration Interaction and Coupled Cluster, which provide highly accurate results but demand significant computational resources. Quantum computing offers potential solutions by leveraging algorithms such as the Variational Quantum Eigensolver and Quantum Diagonalization. Furthermore, scientists are integrating machine learning techniques, including Restricted Boltzmann Machines and generative models, to accelerate calculations and improve the representation of complex wavefunctions. Dimensionality reduction techniques, like Principal Component Analysis, further streamline the process by reducing the number of variables involved.
This research focuses on creating hybrid algorithms that combine the strengths of both classical and quantum computers, paving the way for more accurate simulations and a deeper understanding of chemical systems. Machine learning serves as a powerful tool for accelerating calculations and enhancing accuracy, addressing the challenge of efficiently representing complex wavefunctions.
Fermionic State Reconstruction via Generative Supercomputing Scientists have created PIGen-SQD, a new quantum computing workflow that enhances the accuracy and scalability of simulating molecular systems. This method addresses a critical challenge in quantum centric supercomputing, which involves obtaining reliable results from quantum hardware.
The team pioneers a strategy that combines generative machine learning with physics-informed configuration screening, enabling efficient exploration of the vast computational space and reducing the computational cost of calculations. The research team employs perturbative measures to establish a strong initial overlap with the target state, complementing existing state-preparation techniques. They then utilize Restricted Boltzmann Machines to learn the complex relationships within the system and reduce the dimensionality of the calculation, focusing on important configurations for subsequent diagonalization. Experiments demonstrate that PIGen-SQD reduces the computational space needed for accurate calculations by up to 70% while simultaneously achieving energies with an order of magnitude greater accuracy. This success stems from the strategic integration of classical many-body theory, quantum sampling, and the generative capabilities of machine learning, creating a robust and scalable approach to tackling computationally demanding problems in quantum chemistry.
Generative Learning Shrinks Quantum Simulation Complexity Scientists have developed PIGen-SQD, a new quantum computing framework that significantly improves the efficiency and accuracy of simulating molecular systems. This method addresses a key challenge in quantum centric supercomputing, which involves extracting meaningful data from quantum hardware prone to errors.
The team achieved a reduction in the size of the computational space needed for accurate calculations by up to 70% compared to standard methods. The breakthrough centers on a novel configuration recovery strategy that combines physics-informed analysis with generative machine learning. Researchers leveraged Restricted Boltzmann Machines to learn the complex relationships within the system, allowing them to focus computational resources on the most chemically relevant portions of the vast computational space. This approach ensures the initial data has strong overlap with the target state, guiding the machine learning models to explore only the dominant sectors of the computational space. Experiments on strongly correlated molecular systems demonstrate that PIGen-SQD yields energies an order of magnitude more accurate than conventional methods, effectively reconstructing fermionic states by strategically combining classical many-body theory, quantum sampling, and the generative power of machine learning. Physics-Informed Quantum Sampling for Molecular Systems PIGen-SQD represents a significant advance in quantum computing for chemistry, demonstrating a novel workflow that combines quantum sampling with machine learning and established many-body physics principles. Researchers developed a method that leverages the strengths of both quantum and classical computation to accurately model complex molecular systems, specifically addressing the challenge of obtaining reliable results from noisy quantum hardware. The core achievement lies in a physics-informed pre-processing step, which uses perturbative measures to identify dominant configurations, combined with generative machine learning models to efficiently explore the most chemically relevant sectors of the computational space. Numerical experiments demonstrate that PIGen-SQD substantially improves upon traditional Sample-based Diagonalization methods, achieving up to one order of magnitude better energy accuracy and simultaneously reducing the computational resources required for calculations by up to 70%. This improvement stems from a more accurate initial overlap with the target state, providing a strong foundation for configuration recovery and ultimately leading to more reliable and efficient simulations, establishing a promising pathway toward chemically reliable simulations on utility-scale quantum hardware. 👉 More information 🗞 Physics Informed Generative Machine Learning for Accelerated Quantum-centric Supercomputing 🧠 ArXiv: https://arxiv.org/abs/2512.06858 Tags: