Large Language Model Scaling Laws for Neural Quantum States in Quantum Chemistry

• URL: http://arxiv.org/abs/2509.12679v1
• Date: Tue, 16 Sep 2025 05:04:36 GMT
• Title: Large Language Model Scaling Laws for Neural Quantum States in Quantum Chemistry
• Abstract summary: Scaling laws have been used to describe how large language model (LLM) performance scales with model size, training data size, or amount of computational resources.<n>We identify scaling laws that predict the performance, as measured by absolute error and V-score, for transformer-based neural quantum states.<n>We find that the relationship between model size and training time is highly dependent on loss metric and ansatz, and does not follow the approximately linear relationship found for language models.
• Score: 1.693459173653219
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scaling laws have been used to describe how large language model (LLM) performance scales with model size, training data size, or amount of computational resources. Motivated by the fact that neural quantum states (NQS) has increasingly adopted LLM-based components, we seek to understand NQS scaling laws, thereby shedding light on the scalability and optimal performance--resource trade-offs of NQS ansatze. In particular, we identify scaling laws that predict the performance, as measured by absolute error and V-score, for transformer-based NQS as a function of problem size in second-quantized quantum chemistry applications. By performing analogous compute-constrained optimization of the obtained parametric curves, we find that the relationship between model size and training time is highly dependent on loss metric and ansatz, and does not follow the approximately linear relationship found for language models.

Related papers

• SGD in Multiclass Logistic Regression: Sequential Learning and Scaling Laws [55.81298869922287]
  We study the training dynamics of multiclass logistic regression on high-dimensional Gaussian mixture models with a large number of classes.<n>We establish precise scaling laws governing the cross-entropy risk under gradient-based optimization.
  arXiv  Detail & Related papers  (2026-09-07T18:30:09Z)
• Adaptive Neural Quantum States: A Recurrent Neural Network Perspective [0.7234862895932991]
  We show an Adaptive scheme to optimize Neural-network quantum states (NQS)<n>NQS are powerful neural-network ans"atzes that have emerged as promising tools for studying quantum many-body physics.
  arXiv  Detail & Related papers  (2025-07-24T18:00:03Z)
• GWQ: Gradient-Aware Weight Quantization for Large Language Models [56.22507677736051]
  Large language models (LLMs) show impressive performance in solving complex language tasks.<n> compressing LLMs to low bits can enable to deploy on resource-constrained devices.<n>We propose gradient-aware weight quantization (GWQ), the first quantization approach for low-bit weight quantization.
  arXiv  Detail & Related papers  (2024-10-30T11:16:04Z)
• Scaling Laws for Post Training Quantized Large Language Models [41.78467383320145]
  Generalization abilities of well-trained large language models (LLMs) are known to scale predictably as a function of model size.<n>The quality of LLMs after post-training compression remains highly unpredictable, often requiring case-by-case validation in practice.
  arXiv  Detail & Related papers  (2024-10-15T23:34:22Z)
• Scaling Laws For Mixed Quantization [14.27345780977423]
  Post-training quantization of Large Language Models (LLMs) has proven effective in reducing memory and computational requirements for inference.<n>We introduce two critical metrics, named the quantization ratio ($Q_r$) and quantization block size ($Q_b$)<n>We propose a unified scaling law on post-training quantization (PTQ) that can predict loss degeneration for varying $Q_r$ and $Q_b$.
  arXiv  Detail & Related papers  (2024-10-09T09:45:01Z)
• Neural Scaling Laws From Large-N Field Theory: Solvable Model Beyond the Ridgeless Limit [0.0]
  We use large-N field theory methods to solve a model proposed by Maloney, Roberts and Sully. We uncover a duality transformation at the diagrams level which explains the symmetry between model and training data set sizes.
  arXiv  Detail & Related papers  (2024-05-29T18:00:01Z)
• Observational Scaling Laws and the Predictability of Language Model Performance [51.2336010244645]
  We propose an observational approach that bypasses model training and instead builds scaling laws from 100 publically available models. We show that several emergent phenomena follow a smooth, sigmoidal behavior and are predictable from small models. We show how to predict the impact of post-training interventions like Chain-of-Thought and Self-Consistency as language model capabilities continue to improve.
  arXiv  Detail & Related papers  (2024-05-17T17:49:44Z)
• A Dynamical Model of Neural Scaling Laws [79.59705237659547]
  We analyze a random feature model trained with gradient descent as a solvable model of network training and generalization. Our theory shows how the gap between training and test loss can gradually build up over time due to repeated reuse of data.
  arXiv  Detail & Related papers  (2024-02-02T01:41:38Z)
• Towards Neural Variational Monte Carlo That Scales Linearly with System Size [67.09349921751341]
  Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
  arXiv  Detail & Related papers  (2022-12-21T19:00:04Z)
• A Solvable Model of Neural Scaling Laws [72.8349503901712]
  Large language models with a huge number of parameters, when trained on near internet-sized number of tokens, have been empirically shown to obey neural scaling laws. We propose a statistical model -- a joint generative data model and random feature model -- that captures this neural scaling phenomenology. Key findings are the manner in which the power laws that occur in the statistics of natural datasets are extended by nonlinear random feature maps.
  arXiv  Detail & Related papers  (2022-10-30T15:13:18Z)
• Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments [42.793379799720434]
  We investigate whether scaling laws can be used to accelerate model development. We find that scaling laws emerge at finetuning time in some NLP tasks. For tasks where scaling laws exist, they can be used to predict the performance of larger models.
  arXiv  Detail & Related papers  (2022-02-13T19:13:00Z)
• Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition [67.95996816744251]
  State-of-the-art language models (LMs) represented by long-short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming increasingly complex and expensive for practical applications. Current quantization methods are based on uniform precision and fail to account for the varying performance sensitivity at different parts of LMs to quantization errors. Novel mixed precision neural network LM quantization methods are proposed in this paper.
  arXiv  Detail & Related papers  (2021-11-29T12:24:02Z)
• Low-bit Quantization of Recurrent Neural Network Language Models Using Alternating Direction Methods of Multipliers [67.688697838109]
  This paper presents a novel method to train quantized RNNLMs from scratch using alternating direction methods of multipliers (ADMM) Experiments on two tasks suggest the proposed ADMM quantization achieved a model size compression factor of up to 31 times over the full precision baseline RNNLMs.
  arXiv  Detail & Related papers  (2021-11-29T09:30:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.