Simulation economics
Cut the simulation cost
of superconducting design.
Most of a superconducting design budget is spent re-solving the same physics at thousands of points — every pad geometry in a cQED layout, every bias point in an SFQ cell. A learned surrogate solves that physics once, at training time, then answers the whole sweep at millisecond latency, reserving your solver for the candidates that deserve it.
Why simulation gets expensive
A single high-fidelity solve is affordable. The cost explodes because design questions are sweeps, not solves: every pad geometry that lands a qubit on its target frequency, every bias point that keeps a logic cell inside its margins, every junction spread a fab run might ship with.
At minutes per solve and thousands of points per question, teams end up rationing their own engineering curiosity — exploring less of the design space than they should because compute is metered in cluster-hours.
A worked example · qubit EM design
Live models, published validation
Cheap only counts if the answers hold up.
Every model ships with a validity report: held-out validation against its reference solver, the classical baseline it had to beat, a risk percentile on every prediction — and abstention in the regions the reference physics itself cannot label.
RSFQ screening — Fluxus
Margin, timing, yield & design-rule screening for superconducting logic (MIT-LL SFQ5ee+)
When a surrogate is the wrong tool
If you need one certification-grade answer, run the full simulator — that is what it is for. Surrogates also stay inside the input ranges they were validated on; they will not extrapolate to physics they were never trained against.
The economic case is exploration: screen the whole design space for the cost of a lunch, then spend your solver budget on the handful of candidates that survive.
Where the savings come from
Training happens once, on physics generated by the reference solver. Serving a trained model costs milliseconds of CPU time per design — so the screening loop that used to be metered in cluster-hours becomes effectively free, and your solver budget concentrates on the candidates that matter.
FAQ
Simulation cost, answered plainly.
Why are physics simulations so expensive?
A single high-fidelity solve (electromagnetic eigenmode, transient circuit, FEM) costs minutes to hours of CPU time, and real design questions are never a single solve — they are sweeps: every pad geometry, every bias point, every junction-spread variation a fab run might ship with. Multiply solver time by thousands of design points and you get cluster bills and week-long queues.
What is a simulation surrogate?
A surrogate is a neural network trained on many runs of a reference simulator — an electromagnetic eigenmode solver for qubit chips, or JoSIM for SFQ logic — until it reproduces that solver's outputs across a validated range of inputs. Inference takes microseconds to milliseconds instead of minutes, which is what makes large sweeps cheap. Cogitan trains surrogates purely on synthetic physics — never on customer data.
How accurate are surrogate models?
Accuracy is per-model and must be measured on held-out cases, not claimed. Cogitan publishes a validity report with every model, including the classical baseline it was measured against — and we publish that comparison whether or not it flatters us. RSFQ screening reaches 99.1% chain functional match across 222 held-out chains under a grouped cell-level split with no variant of a held-out cell seen in training; on single-cell margin heads a gradient-boosted-tree baseline is currently the more accurate model, and the manifest says so. Inputs outside a model's validated envelope are refused rather than answered, and every prediction carries a risk percentile.
How do engagements work?
A fixed-scope feasibility study first: we take your simulator, build the labeled dataset, benchmark a surrogate against a strong classical baseline, and deliver the validity report — you see the evidence before committing. If the numbers justify it, the surrogate ships as a managed endpoint your team calls at millisecond latency, with retraining as your process or design library evolves.
When is a surrogate the wrong tool?
When you need one high-stakes, certification-grade solve, run the full simulator. Surrogates are also restricted to the input ranges they were validated on — they will not extrapolate to novel physics. The economic case is exploration: screening a large design space cheaply, then spending your solver budget on the handful of candidates that matter. Our feasibility study will tell you honestly if a classical model or your existing solver is the better answer.
Do I need my own GPUs or solver licenses?
No. The delivered surrogate runs as a managed endpoint on Cogitan infrastructure (or on-prem where the data can't leave), and screening results come back at interactive latency. Your solver seats get reserved for the shortlisted candidates that deserve a full-fidelity run.
See the economics live, on real models.
demo.cogitan.ai