arXiv:2603.14515v2 Announce Type: replace Abstract: Neural-network wave functions in Variational Monte Carlo (VMC) have achieved great success in accurately representing both ground and excited states. However, achieving sufficient numerical accuracy in state overlaps requires increasing the number of Monte Carlo samples, and consequently the computational cost, with the number of states. We present a nearly constant sample-size approach, Multi-State Importance Sampling (MSIS), that leverages samples from all states to estimate pairwise overlap. To efficiently evaluate all states for all sampl
The continuous advancements in AI and computational methods are pushing the boundaries of scientific simulation, making more efficient techniques for quantum mechanics crucial in the near future.
Efficiently simulating quantum systems, particularly excited states, is pivotal for breakthroughs in materials science, drug discovery, and quantum computing, impacting multiple high-tech sectors.
This new method allows for more computationally efficient and accurate simulation of multiple quantum states simultaneously, reducing the cost and time involved in complex scientific research.
- · Materials Scientists
- · Pharmaceutical Industry
- · Quantum Computing Researchers
- · AI/ML Research Institutions
- · Traditional quantum simulation methods
- · Research groups reliant on older computational techniques
Accelerated discovery of novel materials and drug candidates due to more efficient quantum simulations.
Reduced R&D costs for industries relying on molecular and quantum-level understanding, leading to faster product cycles.
Potential for new quantum computing architectures or materials with unprecedented properties, profoundly altering technological landscapes.
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