AI that matters, from the architect's desk. Curated and engineered by Saaket Varma, PhD — no hype, just signal.
HOW TO READ THIS Read downward from the QFT phase-gate cascade to execution on IBM hardware at up to 100 qubits, then the measured fidelity at 80 qubits.
Researchers ran Quantum Fourier Transform benchmarks on IBM hardware at up to 100 qubits, with the correct output still distinguishable above noise. A direct n²−n CX construction matches direct all-to-all requirements on linear hardware, while the convolutional variant used in the experiments adds two CX gates; process fidelity fell to 1.8% at 80 qubits. Treat this as evidence of improving algorithm-to-hardware mapping, not fault-tolerant readiness; evaluate quantum claims using both achieved scale and end-to-end fidelity.
HOW TO READ THIS Read downward from revenue to warrant remeasurement to GAAP loss; bars share a dollar scale, and the bracket identifies the warrant charge within the loss.
IonQ reported $80.1 million in quarterly revenue, up 287% year over year, and raised its 2026 outlook to $280–290 million. Its $1.87 billion GAAP net loss was driven mostly by a $1.65 billion non-cash warrant-liability remeasurement; the $120.3 million adjusted EBITDA loss is the cleaner operating signal. Buyers should separate commercial demand from the financial sustainability of the vendors supplying it.
HOW TO READ THIS Read downward from Liquid AI’s open-weight release through its parameter and context bars to the planning and tool loop inside a phone, as described in the [release](https://www.liquid.ai/blog/lfm2-5-2-6b).
Liquid AI released LFM2.5-2.6B, an open-weight model with a 128K context window, tool calling, and agentic training designed for fully local execution. Phone-class agents can reduce API costs, latency, and exposure of sensitive data, making on-device inference an architectural option rather than a demo. Benchmark it against your actual tool loops and hardware before moving workloads off the cloud.
HOW TO READ THIS Read downward from the shared classical sign to qubit encoding, context-selected noisy measurements on fresh copies, and the conditional reconstruction benefit described in the [arXiv preprint](https://arxiv.org/abs/2608.05240).
Quantum Random Access Quantization proposes encoding context-dependent binary weight signs in measurement-incompatible qubit states, with lower ideal reconstruction risk than shared-sign one-bit quantization under stated assumptions. It is still a simulator-only result constrained by fresh-copy readout and noise, but it offers a specific advantage claim that experiments can disprove. Watch for hardware validation and end-to-end efficiency before treating it as a practical compression method.
A document-translation toolkit for workflows where preserving usable document structure matters as much as translating the text.
An agent framework aimed at reducing the orchestration boilerplate required to build and operate multi-agent workflows.
A collection of specialized agent roles that can accelerate prototyping when teams need reusable job definitions rather than generic assistants.
A shared React and TypeScript client for browser and Electron delivery, reducing duplicated work across web and desktop workplace interfaces.