You can create reusable operators that fit the pipeline model:
def enrich_with_geo(row):
# Assume get_geo is a fast lookup function
row["country"] = get_geo(row["ip"])
return row
enrich = lambda src: src.map(enrich_with_geo)
Now enrich can be inserted anywhere in a pipeline: juq470
(pipeline()
.source(read_csv("visits.csv"))
.pipe(enrich)
.filter(lambda r: r["country"] == "US")
.sink(write_jsonl("us_visits.jsonl"))
).run()
| Aspect | juq470 | pandas | Dask |
|--------|--------|--------|------|
| Memory usage | Low (generator‑based) | High (in‑memory DataFrames) | Moderate (chunked) |
| API simplicity | Minimalist, functional | Rich, but verbose for streaming | Similar to pandas, adds complexity |
| Parallelism | Simple parallel() wrapper | Limited (via swifter or modin) | Built‑in distributed scheduler |
| Learning curve | Shallow | Moderate | Steeper (cluster concepts) | You can create reusable operators that fit the
The solution of large, sparse linear systems is a cornerstone of scientific computing, underpinning applications from climate modelling to quantum chemistry. Classical iterative solvers (e.g., CG, GMRES) scale poorly when faced with ill‑conditioned matrices of dimension >10⁶, while current quantum algorithms such as HHL are limited by qubit counts, circuit depth, and stringent data‑loading requirements. Here we introduce JUQ‑470, a Hybrid Quantum‑Classical (HQC) algorithm that synergistically combines a variational quantum subspace method with a classical preconditioned Krylov‑subspace routine. JUQ‑470 achieves a quadratic reduction in effective condition number and exponential speed‑up in the matrix‑vector multiplication kernel on near‑term quantum hardware (≤150 noisy qubits). Numerical experiments on benchmark problems (2‑D Poisson, Maxwell’s equations, and graph Laplacians) demonstrate up to 5.3× wall‑time improvement over state‑of‑the‑art classical solvers on a high‑performance cluster, while maintaining solution fidelity (relative error <10⁻⁴). We also provide a detailed error‑analysis, resource estimation, and a roadmap for scaling JUQ‑470 to fault‑tolerant quantum processors. Now enrich can be inserted anywhere in a