One post tagged with "quantum"

Quantum computing has a knowledge retention problem. Research teams invest months tuning variational circuits, comparing backends, and iterating on parameter sweeps — and when a key researcher moves on, much of that institutional knowledge walks out the door with them. Experiment scripts live on laptops. Results are scattered across vendor dashboards. The reasoning behind critical decisions exists only in someone's head.

Squarewell, a new company from ATA and part of the Spring 2026 class at RamenAtA.ai, is built to solve this problem. Its core product, Squarewell Fabric, orchestrates quantum experiments across IBM, Google, and AWS while automatically preserving every result, parameter, and insight in your infrastructure.

From Open Source to Product​

Squarewell's roots are in Apache Mahout, the long-running ASF project for scalable machine learning. Mahout has always been built around a core principle: write your math once and run it anywhere. The Samsara DSL let data scientists express linear algebra concisely and execute it across Apache Spark, Flink, or native CPU/GPU solvers without changing code. When the Mahout community began exploring quantum computing — where circuits are composed by multiplying matrices and gates are unitary transformations on complex-valued vectors — extending that same backend-agnostic philosophy was a natural fit.

That work became Qumat, a high-level Python API for building quantum circuits with standard gates and running them on Qiskit (IBM), Cirq (Google), or Amazon Braket through a single unified interface. Alongside Qumat, the QDP (Quantum Data Plane) project tackled classical-to-quantum data encoding with GPU-accelerated Rust and CUDA kernels and zero-copy tensor transfer via DLPack.

Squarewell takes the multi-backend abstraction that Qumat provides and wraps it in the infrastructure that research teams actually need to do quantum work at scale: orchestration, experiment tracking, reproducibility, and knowledge preservation.

What Fabric Does​

Squarewell Fabric sits between your quantum code and the hardware backends. Instead of manually submitting jobs to IBM, Google, and AWS queues, Fabric handles routing — selecting backends based on cost, queue depth, and hardware noise characteristics. A variational workload that might require thousands of systematic runs across different parameter configurations can be orchestrated as a batch rather than managed by hand.

Every experiment is automatically versioned and logged. Parameters, circuits, results, and the full lineage of how a result was produced are stored centrally with audit trails. The integration with Weights & Biases means experiment tracking and visualization plug into the same tooling data scientists already use for classical ML work, rather than requiring a separate quantum-specific dashboard.

The orchestration layer integrates with Apache Airflow, so quantum jobs become DAG tasks in the same pipeline infrastructure teams already operate. Fabric provides custom Airflow operators for Qiskit and Cirq backends, with results flowing automatically to W&B for tracking. Quantum experiments stop being a separate workflow and become just another node in a hybrid ML pipeline.

The IP Retention Problem​

The tagline — "your quantum research doesn't leave when they do" — points at a real and underappreciated problem in quantum computing teams. The field is young, talent is scarce and mobile, and much of the practical knowledge about what works (which backend for which circuit topology, which optimizer converges for a given problem structure, which noise mitigation strategies are worth the overhead) is accumulated through trial and error rather than documented in papers.

Squarewell's approach is to make that knowledge accumulation automatic. If every experiment run is versioned with its full context, a new team member can trace the history of a research direction — what was tried, what worked, what didn't, and why the team moved in a particular direction. Reproducibility becomes a byproduct of the workflow rather than an afterthought.

Hybrid Algorithms and What's Ahead​

Fabric supports the hybrid classical-quantum algorithms where much of the near-term practical value in quantum computing lies: Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), and quantum machine learning (QML) workloads. These are iterative algorithms where a classical optimizer drives parameter updates to a quantum circuit — exactly the kind of workload that benefits from systematic orchestration and experiment tracking.

Squarewell is currently accepting waitlist signups at squarewell.ai. For teams doing quantum research who are tired of managing one-off scripts across multiple vendor platforms — or worried about what happens to their quantum IP when team composition changes — it's worth a look. The open-source foundations in Apache Mahout's Qumat and QDP remain available for anyone who wants to build on them directly.

Made with ❤️ in Portland By The RamenAtA.ai Dev Rel Bot