Applicability boundaries and validation status

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oracq can express open algorithms, assemble modules, and generate executable small instances. Whether it suits a specific problem also depends on the input model, the scale, the numerical approximation, and the readout requirements.

Layer	What has been validated	What cannot be inferred from it
RIR	Serialization, layout, aliasing, control guards, module calls, and open bindings	Mathematical correctness of arbitrary quantum algorithms
Basic algorithms	Small-scale matrix/probability witnesses cross-checked against real-backend complex amplitudes	Large-scale running cost or quantum advantage
Arithmetic and math functions	Reversible constructions under finite bit patterns plus some numerical witnesses	Uniform error bounds over all word lengths and approximation intervals
Hamiltonian methods	Pauli evolution, Trotter composition, and interface checks	General QSP kernels and automatic error configuration
QLSS/QODE/QPDE	Input adaptation, modular generation, and partial real execution	Full solver accuracy, success channels, and convergence guarantees
QHAM	Algebraic closure at finite HAM truncation and small executions	Automatic convergence certification against the original PDE

Scale limits

A single register or fused view is at most 64 bits. Some algorithm convenience interfaces place the target or signal in one register and therefore hit this limit first; the RIR itself can use multiple registers.

The reference executor and the backend adapters have step-count, state-count, and QRAM materialization budgets. Downstream OriginIR parsers expand modules. That a program can keep a compact RIR does not mean it can also run cheaply in the current simulators.

Mathematical claims

Claims such as Hermitian, dissipation, sparsity, element bounds, spectral bounds, and ancilla re-cleaning are handled by the corresponding algorithms. The Python protocols can check that interfaces exist and that return shapes match, but cannot prove the mathematical truth of a claim.

eps is not a language-core type. Users should keep the generation configuration and validate, in the concrete application, the errors of spatial discretization, numerical representation, algorithmic truncation, and post-selected readout.

Results and readout

An output-state oracle may carry a success signal. The normalization direction, the success probability, and the physical norm of the original vector are different pieces of information. After measurement or post-selection, the full classical field values cannot be recovered from the target amplitudes alone.

The interfaces, witness coverage, and documentation build results of the new algorithms are recorded in the validation record of this release.