Physically Constrained Ensemble Gaussian Process Modelling for Expensive Quantum Systems with Heteroskedastic Noise
arXiv:2606.11240v1 Announce Type: cross Abstract: Accurate modeling of quantum many-body systems often requires computationally expensive simulations such as Density Matrix Renormalization Group (DMRG) or Quantum Monte Carlo (QMC) calculations. These methods, while precise, impose significant time and resource constraints, limiting their use in exhaustive parameter exploration. Moreover, these expensive simulations can contain variable errors over the large unknown parameter space, which needs to be quantified and propagated. Thus, predictive modelling is required to estimate the functional sp
The increasing computational demands of simulating quantum systems necessitate more efficient and intelligent modeling techniques as quantum research accelerates.
Improving the efficiency and accuracy of quantum system simulations is crucial for advancements in materials science, quantum computing, and drug discovery, enabling faster research cycles and reducing computational costs.
New methodologies combining advanced AI models with physically constrained data offer a path to significantly reduce the computational expense and time barriers in quantum research.
- · Quantum computing researchers
- · Materials science
- · AI/ML in scientific computing
- · High-performance computing
- · Traditional brute-force simulation methods
- · Research groups with limited compute resources
Faster and cheaper development of new quantum materials and devices will become possible.
This could accelerate the timeline for practical quantum computing applications and novel material discoveries.
Reduced resource requirements for fundamental research might democratize advanced quantum science, allowing more diverse groups to contribute.
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