Rosetta puts verified quantum algorithms behind a single API. Your problem, your data, your baseline as the referee — you pay for the measurement, win or lose; hardware and physics handled.
# at launch: pip install rosetta && rosetta scan ./my_problem.json
Not a framework you learn. Not a cloud you rent. Not a repo that hosts everyone's code without an opinion. Rosetta is a knowledge layer delivered as an API — the verified answers to the world's big problem-classes, one function call away.
A "recipe" is real code — a parametrized quantum algorithm. We run each one against the best classical solver on the same problem, and publish who won, with the raw proof.
We sell verified trust in an industry drowning in hype. Nobody neutral does this — so the verdict itself is the product.
You call one function. Our proprietary engine turns your problem into the winning circuit and returns the answer. The royalty rides on advantaged runs — today our archive reports zero.
We don't build machines or frameworks. We own the verified answers that run on everyone else's hardware.
You don't bring "the Bank X portfolio." You bring a problem-class — portfolio optimization, molecular binding, fleet routing — the way the Pythagorean theorem answers every right triangle, not one. A few dozen classes cover ~80% of the value.
"Optimize this portfolio." Your data, your current method as the baseline.
Our proprietary recipe turns it into the winning quantum circuit. This is the scarce, protected part.
Runs on IBM / Amazon's machine. We rent it and pass it through at cost — zero markup.
Returned with proof, measured against your baseline — win or not. You pay for the measurement, either way. That “no” saves you the budget you would have burned.
The quantum computing never runs on our servers. The intelligence does — and that's what we protect and charge for. You never touch a qubit or learn any physics.
qlib.scan(sample) before standup. Verdict with evidence by lunch — either a faster frontier, or written proof quantum isn't there yet for your book. Both answers make your 9am.RQ-0007, keep your scoring function as the baseline. Every set comes back measured against it, and you pay for the measurement. Expense it as cloud spend — because that’s what it is.qlib.solve() at yesterday’s instance with your heuristic as baseline. The gap it closes is measured in your money, on your data. If there is no gap, the measurement says so — and that “no” saves you the budget you would have burned.If you read one of those and felt seen: the console below is already yours. No form between you and it.
It's cloud spend, not a vendor deal. Card on file, usage line-item, spend caps. Procurement meets Rosetta after it's already saved you a quarter — with the evidence attached.
Your baseline is the referee. Every solve is checked against the method you run today, on your data. Win or lose, you get the comparison report and the seal to verify it.
You become the answer to the quantum question. Next time leadership asks, you reply with a verdict, raw evidence and a cost line — produced by you, before anyone else in your industry.
This is a preview of the console you'll get after signup. Choose a recipe, set your scale, and see how a solve will work on public benchmark data. This is a simulation of the product experience — not a live service yet.
Every request is first screened free on simulators — only high-confidence runs are routed to paid hardware. Win or lose, you get your baseline’s answer + the comparison report. Hardware at cloud list price, zero markup. Typical turnaround: sandbox instant · hardware minutes-to-hours by queue. Rates to be published at GET /v1/pricing
This is the core of what we're building. Every advantage claim will resolve to a stable URL with the circuit, parameters, raw shots and replication reports. When a recipe improves — or degrades — on new hardware, its status updates publicly. The promise: verify anything yourself, in minutes, before spending a dollar.
The full board of recipes lives in the ledger. Each verdict will ship as a reproducible notebook: clone it, re-measure. Our first public verdicts are in progress; negative results get published too.
Open the Evidence Ledger →This is our intended pricing, derived from real hardware costs — published now so there are no surprises later. These numbers describe the model, not a live meter yet.
Copilots recommend us because everything here is machine-verifiable — and you can check it too, right now:
Exactly. Account, card, key, dashboard, spend caps. The meter isn’t tokens — it’s the measured solve: one answered problem, benchmarked against your baseline — win or lose. Hardware passes through at list price; our fee is for the measurement.
The scan tells you upfront — a machine-generated verdict, in writing, with data. In production, every solve is checked against your own baseline before billing: you pay for the measurement, and you keep the comparison report either way. Our incentive is the measurement, not the verdict.
Sandbox solves are near-instant (simulator-backed). Hardware solves depend on QPU queues: typically minutes to a few hours; the console shows live queue estimates per platform before you commit, and routing always picks the best price/queue trade-off available.
Launch a Discovery Run from the console: the agent swarm attacks your problem class, priced per compute with an upfront estimate. If it finds a winning recipe, it ships to your account first. Demand writes the roadmap.
Async and fast: in-console assistant with full context of your runs, evidence-linked docs, and engineers behind it for the hard cases. Most issues resolve in minutes because every run is fully inspectable — yours and ours.
Our design goal is that your data is formulated into circuit parameters client-side where possible — so raw business data doesn't need to leave your perimeter for most problem classes. Jobs run through major clouds' quantum services, and every run will be auditable via its evidence URL. SOC 2 and a published DPA are on our roadmap before general availability — we'll say when they're done, not before.
Correct — and it's our business, not our bug. We never claim advantage that isn't there. We measure where it doesn't hold, publish the negative verdicts everyone else buries, and map the crossover point where it will. If quantum arrives late, the referee's archive only grows more valuable. If it arrives on time, we own the map of where it landed first.
Technically, yes — the code is glue, the libraries are open. But everyone capable is structurally blocked: academia gets no tenure for maintaining a verification ledger; vendors can't publish negatives on the industry they sell (Google pays XPRIZE precisely so a neutral third party exists); consultancies monetize the ambiguity a public ledger destroys. Our edge is a proven veracity methodology already in production, a near-zero cost structure that outlasts funded startups, and a compounding dataset of verified negatives nobody can buy.
The ARM model for the quantum era: invisible, small by design, a royalty on every useful computation — on every machine, forever. We start as ~90% authority (verdicts, evidence, subscription) and grow the licensing arm as recipes cross the advantage line. Founded a decade early, on purpose — because the archive compounds while we wait.
"Every solve will make the library smarter. Every Discovery Run will add a recipe the whole industry can one day license. You're early to a compounding asset."Get early access →