This work addresses the challenge of molecular geometry optimization at post-Hartree–Fock levels of theory using quantum computing methodologies. We present a novel potentially quasi-adiabatic approach based on the Variational Quantum Eigensolver (VQE) algorithm for efficient exploration of potential energy surfaces. The method combines quantum-circuit emulators with classical optimization routines and uses the Hellmann–Feynman theorem, including Pulay corrections, together with a finite-difference scheme to compute forces. Our implementation uses GPU-accelerated quantum-circuit operations within a parallelized framework, achieving computational efficiency by warm-starting the wavefunction and reoptimizing it at each subsequent geometry-optimization step. Benchmark results on small molecular systems demonstrate the feasibility of this hybrid quantum-classical approach, with detailed comparisons against established quantum chemistry methods, including CASCI and DFT optimizations. We discuss the current limitations, computational advantages, and potential future developments of this methodology in the context of quantum chemistry for drug discovery applications.
Leonardo U. Masci, Fabio Tarocco, Domenico Bonanni et al.· Journal of Chemical Theory a...· 0 citations
The transition to post-quantum cryptography (PQC) is driving demand for implementations that can meet the computational requirements of real-world applications. Among the proposed PQC constructions, Learning With Errors (LWE) based key encapsulation mechanisms (KEMs) are particularly attractive due to their strong security foundations, but they incur substantial computational costs from matrix operations and large-scale cryptographically secure random number generation. These characteristics position GPU acceleration as an effective approach for lowering the computational overhead of lattice based cryptographic schemes. In this work, we present a portable GPU implementation of a plain LWE based KEM using OpenMP Target offloading. Unlike most existing GPU implementations, which rely on CUDA specific optimizations, our approach uses a single source code base that executes on both NVIDIA and AMD accelerators. We evaluate the proposed implementation on different accelerator architectures, analyzing performance benchmarking, runtime profiling, scalability analysis, and energy to solution measurements. Experimental results show that OpenMP Target offloading delivers substantial acceleration over a multicore CPU baseline while preserving source level portability across heterogeneous GPU ecosystems. Cross platform analysis identifies NVIDIA GH200 and AMD MI300X as the most effective platforms for this memory bound workload, while profiling indicates that memory system organization and CPU GPU interaction play a more critical role than peak compute capability alone. These findings demonstrate that portable GPU acceleration can significantly reduce the computational overhead of PQC while avoiding vendor lock in, thereby facilitating the deployment of quantum resistant cryptographic infrastructures.
Tiziana Liberati, N. Shukla, Simone Rizzo et al.· arXiv.org· 0 citations
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