mlsanityData to 13 Oct 2026

Solving the encoding bottleneck: of the HHL algorithm, by the HHL algorithm

arXiv 2502.13534 ↗First posted 19 Feb 2025quant-ph cs.AI cs.LGcited by 0

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Abstract (arXiv)

The Harrow-Hassidim-Lloyd (HHL) algorithm offers exponential speedup for solving the quantum linear-system problem. But some caveats for the speedup could be hard to met. One of the difficulties is the encoding bottleneck, i.e., the efficient preparation of the initial quantum state. To prepare an arbitrary N-dimensional state exactly, existing state-preparation approaches generally require a runtime of O(N), which will ruin the speedup of the HHL algorithm. Here we show that the states can be prepared approximately with a runtime of O(poly(log N)) by employing a slightly modified version of the HHL algorithm itself. Thus, applying this approach to prepare the initial state of the original HHL algorithm can preserve the exponential speedup advantage. It can also serve as a standalone solution for other applications demanding fast state preparation.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 19 Feb 2025.

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References · 5

  1. 2020Variational Quantum Algorithmscited by 689
  2. 2020The power of quantum neural networkscited by 378
  3. 2020Robust data encodings for quantum classifierscited by 91
  4. 2022Quantum activation functions for quantum neural networkscited by 10
  5. 2022Accelerating the training of single-layer binary neural networks using the HHL quantum algorithmcited by 3

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