·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
OPTScientist: Multi-Agent Discovery of Typed Optimizer Programs for Transformer Pretraining5h◆More Is Not More: What Matters for Diversity in LLM Opinions?5h◆Evaluation and Prognostic Validation of Deep Regression Models for WSI-Based Gene-Expression Prediction5h◆SESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flows5h◆Non-Zipfian Distribution of Stopwords or Function Words and Subset Selection Models5h◆Exact ReLU realization of affine one-dimensional refinement iterates via residual memory and offset frames5h◆Offline RL with Hierarchical Action Chunking5h◆Statistical Inference for Generative Model Comparison5h◆Content Anonymization for Privacy in Long-form Audio5h◆Enhancing Membership Inference Attacks on Diffusion Models from a Frequency-Domain Perspective5h◆Gibbs randomness-compression proposition5h◆Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation5h◆Approximate Quantum State Preparation Through Proximal Policy Optimization5h◆Transformer-based Diffusion models for Hydrological Time Series Probabilistic Imputation and Forecasting5h◆Instance Hardness-Based Relevance for Imbalanced Regression5h◆Stochastic Sampling is Epistemically Shallow: The Dimensionality Gap Between Temperature Variation and Model Diversity in LLMs5h◆DecodeShare: Tracing the Shared Subspace of LLM Decode-Time Decisions5h◆PlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs5h◆Incomplete Prompt Jailbreaks in Large Language Models5h◆Directional Hallucinations: Ideological Drift in News-Grounded LLM Question Answering5h◆OPTScientist: Multi-Agent Discovery of Typed Optimizer Programs for Transformer Pretraining5h◆More Is Not More: What Matters for Diversity in LLM Opinions?5h◆Evaluation and Prognostic Validation of Deep Regression Models for WSI-Based Gene-Expression Prediction5h◆SESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flows5h◆Non-Zipfian Distribution of Stopwords or Function Words and Subset Selection Models5h◆Exact ReLU realization of affine one-dimensional refinement iterates via residual memory and offset frames5h◆Offline RL with Hierarchical Action Chunking5h◆Statistical Inference for Generative Model Comparison5h◆Content Anonymization for Privacy in Long-form Audio5h◆Enhancing Membership Inference Attacks on Diffusion Models from a Frequency-Domain Perspective5h◆Gibbs randomness-compression proposition5h◆Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation5h◆Approximate Quantum State Preparation Through Proximal Policy Optimization5h◆Transformer-based Diffusion models for Hydrological Time Series Probabilistic Imputation and Forecasting5h◆Instance Hardness-Based Relevance for Imbalanced Regression5h◆Stochastic Sampling is Epistemically Shallow: The Dimensionality Gap Between Temperature Variation and Model Diversity in LLMs5h◆DecodeShare: Tracing the Shared Subspace of LLM Decode-Time Decisions5h◆PlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs5h◆Incomplete Prompt Jailbreaks in Large Language Models5h◆Directional Hallucinations: Ideological Drift in News-Grounded LLM Question Answering5h◆
News/Approximate Quantum State Preparation Through Proximal Policy Optimization
arxiv
PublishedJuly 24, 2026 at 4:00 AM
—neutral

Approximate Quantum State Preparation Through Proximal Policy Optimization

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2607.21121v1 Announce Type: cross Abstract: In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed. QSP is a challenging task, since the search space grows exponentially with the number of qubits, making the identification of the optim

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Mentioned models
01
  • 01
    proximal policy optimization
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#quantum-physics#reinforcement-learning#state-preparation

No replies yet. Be first.

Mentioned models
01
  • 01
    proximal policy optimization
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#quantum-physics#reinforcement-learning#state-preparation

Related coverage

More from ARXIV
arxivOPTScientist: Multi-Agent Discovery of Typed Optimizer Programs for Transformer Pretraining5harxivMore Is Not More: What Matters for Diversity in LLM Opinions?5harxivEvaluation and Prognostic Validation of Deep Regression Models for WSI-Based Gene-Expression Prediction5harxivSESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flows5h
The Bubble Brief
WEEKLY

Read quantum-physics insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews