{"ID":2837669,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19291","arxiv_id":"2511.19291","title":"TorchQuantumDistributed","abstract":"TorchQuantumDistributed (tqd) is a PyTorch-based [Paszke et al., 2019] library for accelerator-agnostic differentiable quantum state vector simulation at scale. This enables studying the behavior of learnable parameterized near-term and fault- tolerant quantum circuits with high qubit counts.","short_abstract":"TorchQuantumDistributed (tqd) is a PyTorch-based [Paszke et al., 2019] library for accelerator-agnostic differentiable quantum state vector simulation at scale. This enables studying the behavior of learnable parameterized near-term and fault- tolerant quantum circuits with high qubit counts.","url_abs":"https://arxiv.org/abs/2511.19291","url_pdf":"https://arxiv.org/pdf/2511.19291v1","authors":"[\"Oliver Knitter\",\"Jonathan Mei\",\"Masako Yamada\",\"Martin Roetteler\"]","published":"2025-11-24T16:37:28Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cs.CE\",\"cs.LG\"]","methods":"[]","has_code":false}