arXiv (All)
open access
Reducing quantum and classical resources for quantum-centric supercomputing workloads on near-term hardware
Sample-based Krylov quantum diagonalization (SKQD) is a paradigmatic example of a quantum-centric supercomputing workflow that combines the convergence structure of Krylov quantum diagonalization with classical sampling-based post-processing. It provides convergence guarantees assuming that the impo
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- DF-SQD: Deterministic Fields for Sampling-Based Quantum Diagonalization#821218
- Hybrid quantum-classical algorithms for high performance computing#357463
- Maximizing quantum hardware utilization via multiprogramming circuits and shot-wise distribution#185577
- Exponential quantum advantage in processing massive classical data#1157271
- An Iterative Method with Asymptotic Orthogonality for Simultaneous Eigenpair Computation#925887
Record · ID 973572
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive