Research track · Science & climate
Weather & scientific simulation research
Investigate where quantum subroutines could fit scientific pipelines.
Why this horizon. Linear-system algorithms such as HHL promise speed-ups only under strict conditions on input loading and output reading that real simulations rarely meet.
- The problem
- Scientific simulations are compute-heavy and dominated by classical HPC.
- Why quantum or hybrid methods may be relevant
- Specific subroutines (e.g. linear systems) are studied theoretically.
- Classical baseline
- Existing HPC codes and their published performance.
- What QAISS could provide
- Literature reviews, small-scale prototypes, and feasibility notes.
- What evidence would count as success
- Clear, peer-reviewable feasibility assessments — positive or negative.
- Limitations & open questions
- Theoretical speedups often carry input/output costs that erase practical gains.
- Current status
- Planned No experiments have been published for this track yet.
Workflow on QAISS
From question to checkable result.
- STEP 1
Identify the sub-routine (linear solve, Fourier transform, sampling).
- STEP 2
Check the conditions: sparsity, condition number, how data is loaded and read out.
- STEP 3
Prototype a tiny instance on a simulator; compare with the HPC code.
- STEP 4
Publish a feasibility note — negative results included.
Sources
Read the literature.
- Quantum algorithm for linear systems of equationsA. W. Harrow, A. Hassidim, S. Lloyd · Phys. Rev. Lett. 103, 150502 · 2009
- Read the fine printS. Aaronson · Nature Physics 11, 291–293 · 2015
- Quantum computers for weather and climate prediction: the good, the bad and the noisyF. Tennie, T. Palmer · arXiv:2210.17460 · 2022
Cited for context. QAISS is not affiliated with the authors or publishers.
No quantum advantage, production savings, medical benefit, financial return, or security guarantee is claimed for this track.