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new b4603c10c docs: add qumat 0.6.0 release blog post with verified
benchmarks (#1364)
b4603c10c is described below
commit b4603c10c738fda3a619132509b2d33af66ecd0b
Author: Ryan Huang <[email protected]>
AuthorDate: Thu Jun 4 12:32:12 2026 +0800
docs: add qumat 0.6.0 release blog post with verified benchmarks (#1364)
- Blog post covering QDP encoding parity, AMD Triton backend, new
benchmarks (SVHN IQP, data-to-state latency, DataLoader throughput),
and docs overhaul
- Benchmark charts generated from fresh runs on RTX 3090 Ti (GPU 0/1)
- Add ryankert to blog authors.yml
---
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docs/blog/2026-06-01-Qumat-0.6.0-Release/index.md | 108 +++++++++++++++++++++
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.../svhn-benchmark.png | Bin 0 -> 53207 bytes
docs/blog/authors.yml | 6 ++
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+---
+title: Introducing Apache Mahout qumat 0.6.0
+date: 2026-06-01
+tags: [release, qumat, qdp]
+authors: [ryankert, mahout-team]
+---
+
+We're excited to announce the release of **Apache Mahout qumat 0.6.0**,
rolling up 111 pull requests from contributors across the community since
0.5.0. This release brings AMD GPU support to first-class status, closes
encoder parity gaps between CUDA and ROCm, adds new benchmarks for real-world
datasets, and overhauls the documentation site.
+
+Thank you to everyone who contributed to this release: Ryan Huang, Vic Wen,
Jie-Kai Chang, Guan-Ming (Wesley) Chiu, Tim Hsiung, Kuan-Hao Huang, ChenChen
Lai, Suyash Parmar, Yehfela, Shivam Mittal, Alisha, Hsien-Cheng Huang, Eddie
Tsai, Andrew Musselman, Xin Hao, wdskuki, Trevor Grant, Karanjot Gaidu,
Howardisme, and Han-Wen Tsao.
+
+<!-- truncate -->
+
+## Key Highlights
+
+- **QDP encoding parity and new encodings** — Phase, IQP, and IQP-Z encodings
on both CUDA and AMD ROCm; float32 zero-copy batch paths and GPU-pointer
encoding for the IQP family.
+- **AMD GPU support** — New Mahout-AMD framework with hand-written Triton
kernels running natively on ROCm; AMD is now selectable from all QDP encoding
and throughput benchmarks.
+- **New benchmarks** — SVHN Quantum Kernel SVM, MNIST amplitude encoding, and
IQP latency/throughput benchmarks with PennyLane baselines.
+- **Documentation overhaul** — Frontmatter across all pages, self-hosted
KaTeX, a new troubleshooting guide, and CONTRIBUTING merged into the README for
easier onboarding.
+
+Let's explore these in more detail.
+
+## QDP Encoding Parity and New Encodings
+
+Prior to 0.6.0, the CUDA and AMD ROCm backends had different encoder coverage
— some encoding paths only existed on one backend, which made benchmarking
apples-to-apples comparisons difficult and limited ROCm's usability in practice.
+
+In 0.6.0, Phase, IQP, and IQP-Z encodings now ship on both NVIDIA CUDA and AMD
ROCm backends, closing the parity gap with the existing angle and amplitude
paths.
+
+On the performance side, this release adds:
+- **Float32 zero-copy batch paths** for angle and basis encoders — both
single-sample and batched, in both `qdp-core` (Rust) and the Python bindings —
eliminating host-device copies in hot loops.
+- **GPU-pointer encoding** for the IQP family — pass a CUDA tensor directly
via DLPack and skip the host round-trip entirely.
+- **IQP kernel fusion and grid-stride optimizations** — persistent kernels and
fused encode passes reduce kernel launch overhead.
+- **Async prefetching and native f32 dispatch pipelines** — overlaps I/O with
compute for throughput-bound workloads.
+
+
+
+*Figure: Data-to-state latency scaling (ms/vector, log scale) for amplitude,
angle, and basis encoding. QDP (orange) achieves orders-of-magnitude lower
latency than both CPU and GPU backends.*
+
+Install the QDP extra to try these:
+
+```bash
+pip install "qumat[qdp]==0.6.0" # Linux x86_64 + NVIDIA CUDA
+```
+
+## AMD GPU Support
+
+0.6.0 makes AMD ROCm a first-class backend via **hand-written Triton kernels**
(`TritonAmdEngine`) that run natively on ROCm without going through PennyLane.
The Triton path covers amplitude, angle, basis, phase, IQP, and IQP-Z encodings
— the same set now available on CUDA — and is selectable from the QDP encoding
and throughput benchmarks via `--qdp-backend amd`.
+
+PennyLane-AMDGPU (`lightning.amdgpu`) appears in the throughput benchmark as a
comparison baseline, not as Mahout's implementation path.
+
+Docker images for the AMD environment are also included
(`Dockerfile.qdp-amd`), making it easy to spin up a reproducible AMD test
environment without a physical ROCm machine.
+
+CUDA kernel build targets are now **configurable** for forward compatibility —
specify the target compute capabilities at build time rather than hard-coding
them, so the wheel stays valid on architectures released after the build.
+
+## New Benchmarks
+
+0.6.0 adds three new benchmarks that cover more realistic workloads than the
synthetic microbenchmarks from earlier releases:
+
+**SVHN IQP variational classifier** — trains a variational IQP classifier on
the Street View House Numbers dataset (digit 1 vs 7, 200 samples, 200
iterations) on two RTX 3090 Ti GPUs. QDP offloads the IQP encoding step to the
GPU in a single one-shot pass before training begins, keeping the training
backend independent of encoding cost. QDP GPU consistently outperforms
PennyLane GPU at every qubit count — **41 vs 33 samples/s at 4 qubits** and
**20 vs 15 samples/s at 10 qubits** (~1.35×).
+
+
+
+**SVHN Quantum Kernel SVM** — runs a precomputed squared inner-product quantum
kernel SVM on SVHN amplitude-encoded features (12 qubits), measuring end-to-end
pipeline time from raw pixels to SVM prediction. This is the first benchmark
that exercises the full pipeline (feature encoding → kernel matrix → SVM
fit/predict) rather than just the encoding step.
+
+**Data-to-State latency** — isolates the full pipeline from CPU RAM to
GPU-ready quantum state at 16 qubits (65,536-dimensional vectors). Mahout (QDP)
delivers **0.160 ms/vector** — **4.5× faster than PennyLane** (0.716 ms), **56×
faster than Qiskit Statevec** (9.030 ms), and **477× faster than Qiskit
Initialize** (76.243 ms). PennyLane and Mahout both target GPU; Qiskit runs on
CPU with Qiskit Init adding full circuit decomposition and transpilation
overhead on top.
+
+
+
+**DataLoader amplitude throughput** — streams 12,800 amplitude-encoded vectors
in batches of 64 at 16 qubits. Mahout (QDP) delivers **6,101 vectors/s** —
**3.8× faster than PennyLane** (1,604 vectors/s).
+
+
+
+All benchmarks support AMD backend selection via `--qdp-backend amd`, so you
can do a direct CUDA-vs-ROCm comparison on the same workload.
+
+## Documentation Overhaul
+
+The docs site received a significant refresh:
+
+- **Frontmatter added to every `docs/**/*.md` page** — fixes SEO metadata that
was missing from most pages.
+- **Self-hosted KaTeX** — math rendering now works offline and no longer
depends on the CDN, fixing the "KaTeX not found" error reported by several
contributors.
+- **Troubleshooting guide** — a new guide covers the most common install and
runtime issues, especially around GPU detection and CUDA/ROCm version
mismatches.
+- **CONTRIBUTING merged into the README** — reduces the number of places a new
contributor needs to read to get started.
+- **Type hints added to qumat** — initial pass adding Python type annotations,
laying the groundwork for better IDE support and static analysis.
+- **PR policy and review guidelines** — documents the merge criteria and
review expectations in one place.
+
+## Other Improvements
+
+- **Cloud storage support** — the QDP data loader now supports S3 and GCS
remote URLs in addition to local paths, enabling benchmarks and pipelines that
read directly from object storage.
+- **Encoding and Dtype enums** — `Encoding` and `Dtype` are now proper Python
enums rather than bare strings, with static dispatch in the encoder.
+- **Pure-PyTorch reference implementations** — added alongside the CUDA
kernels for correctness comparison and CPU fallback.
+- **Ruff rules expanded** — `E` and most `ANN` rules are now enforced;
document type-checking CI added.
+- **pytest-xdist** — parallel test execution cuts CI wall-clock time on
multi-core machines.
+
+## Getting Started
+
+```bash
+# Core package
+pip install qumat==0.6.0
+
+# With GPU-accelerated QDP extension (Linux x86_64 + NVIDIA CUDA)
+pip install "qumat[qdp]==0.6.0"
+```
+
+- PyPI: https://pypi.org/project/qumat/0.6.0/
+- Release tag: https://github.com/apache/mahout/releases/tag/mahout-qumat-0.6.0
+- Docs: https://mahout.apache.org/
+
+We welcome feedback and contributions on the
[[email protected]](mailto:[email protected]) mailing list and on
[GitHub](https://github.com/apache/mahout).
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diff --git a/docs/blog/authors.yml b/docs/blog/authors.yml
index f0093add3..36921c866 100644
--- a/docs/blog/authors.yml
+++ b/docs/blog/authors.yml
@@ -4,3 +4,9 @@ mahout-team:
url: https://mahout.apache.org
image_url: /img/mahout-favicon-black.svg
image_url_dark: /img/mahout-favicon.svg
+
+ryankert:
+ name: Ryan Huang
+ title: Apache Mahout Committer
+ url: https://github.com/ryankert01
+ image_url: https://github.com/ryankert01.png
