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Accelerating Quantum Monte Carlo Simulations with Emerging Architectures

Akila Gothandaraman

Published Jan 1, 2009• 1 citation
OpenAlex

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Quantum annealing (QA) is used to find the global optimum for combinatorial optimization problems. QA can be simulated on a computer using quantum Monte Carlo (QMC) simulation while spending a huge processing time. We propose a custom architecture to accelerate simulated QA. The proposed architecture is implemented using “open computing language (OpenCL)” on an Intel Aria10 FPGA. It is possible to design the most appropriate architecture for different optimization problems by changing the design parameters of the OpenCL code. We achieved over 1.94 times speed-up compared to CPU-based implementation.
Quantum Monte Carlo (QMC) methods deliver highly accurate electronic structure calculations but are computationally intensive. The quantum Monte Carlo kernel library (QMCkl) provides a modular, portable collection of high-performance kernels implementing the core building blocks of QMC calculations. It offers a C-compatible application programming interface, supports the TREXIO standard for input, and covers essential QMC kernels including atomic and molecular orbitals, cusp corrections, the Jastrow factor, and the necessary derivatives also to perform variational and structural optimization. QMCkl separates algorithmic development from hardware-specific tuning by combining human-readable reference implementations with performance-optimized kernels that produce identical numerical results. The library enables consistent, efficient, and reproducible simulations across different QMC codes and architectures and achieves substantial speedups in the evaluation of the energy and its derivatives. Beyond QMC, QMCkl can accelerate deterministic quantum chemistry workflows and visualization tools, promoting cross-code interoperability and simplifying high-performance scientific software development.