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get_detector_error_model

get_detector_error_model(stim_circuit: stim.Circuit, allow_non_deterministic_observables: bool = True, decompose_errors: bool = False, flatten_loops: bool = False, allow_gauge_detectors: bool = False, approximate_disjoint_errors: bool = False, ignore_decomposition_failures: bool = False, block_decomposition_from_introducing_remnant_edges: bool = False) -> stim.DetectorErrorModel
Return a stim.DetectorErrorModel describing the error processes in the circuit. Unlike the stim.Circuit.detector_error_model() method, this method allows for non-deterministic observables when allow_gauge_detectors is set to true. This is achieved by converting logical observables into detectors, calling the stim.Circuit.detector_error_model(allow_gauge_detectors=True), and then reconverting the detectors back into observables in the detector error model. WARNING: If the circuit has distance one, i.e. if there are errors that only flip logical observables, this method will return an incorrect detector error model. Parameters:
  • stim_circuit (stim.Circuit) — The stim circuit to compute the detector error model for.
  • allow_non_deterministic_observables (bool) — Defaults to true. When set to true, the detector error model allows for non-deterministic observables. This is achieved by converting logical observables into detectors, calling the stim.Circuit.detector_error_model(allow_gauge_detectors=True), and then reconverting the detectors back into observables in the detector error model.
  • decompose_errors (bool) — Defaults to false. When set to true, the error analysis attempts to decompose the components of composite error mechanisms (such as depolarization errors) into simpler errors, and suggest this decomposition via stim.target_separator() between the components. For example, in an XZ surface code, single qubit depolarization has a Y error term which can be decomposed into simpler X and Z error terms. Decomposition fails (causing this method to throw) if it’s not possible to decompose large errors into simple errors that affect at most two detectors. When allow_non_deterministic_observables is set to true, decomposition is not supported.
  • flatten_loops (bool) — Defaults to false. When set to true, the output will not contain any repeat blocks. When set to false, the error analysis watches for loops in the circuit reaching a periodic steady state with respect to the detectors being introduced, the error mechanisms that affect them, and the locations of the logical observables. When it identifies such a steady state, it outputs a repeat block. This is massively more efficient than flattening for circuits that contain loops, but creates a more complex output. Irrelevant unless allow_non_deterministic_observables=False.
  • allow_gauge_detectors (bool) — Defaults to false. When set to false, the error analysis verifies that detectors in the circuit are actually deterministic under noiseless execution of the circuit. When set to true, these detectors are instead considered to be part of degrees freedom that can be removed from the error model. For example, if detectors D1 and D3 both anti-commute with a reset, then the error model has a gauge error(0.5) D1 D3. When gauges are identified, one of the involved detectors is removed from the system using Gaussian elimination. Note that logical observables are still verified to be deterministic, even if this option is set.
  • approximate_disjoint_errors (bool) — Defaults to false. When set to false, composite error mechanisms with disjoint components (such as PAULI_CHANNEL_1(0.1, 0.2, 0.0)) can cause the error analysis to throw exceptions (because detector error models can only contain independent error mechanisms). When set to true, the probabilities of the disjoint cases are instead assumed to be independent probabilities. For example, a PAULI_CHANNEL_1(0.1, 0.2, 0.0) becomes equivalent to an X_ERROR(0.1) followed by a Z_ERROR(0.2). This assumption is an approximation, but it is a good approximation for small probabilities. This argument can also be set to a probability between 0 and 1, setting a threshold below which the approximation is acceptable. Any error mechanisms that have a component probability above the threshold will cause an exception to be thrown.
  • ignore_decomposition_failures (bool) — Defaults to False. When this is set to True, circuit errors that fail to decompose into graphlike detector error model errors no longer cause the conversion process to abort. Instead, the undecomposed error is inserted into the output. Whatever tool the detector error model is then given to is responsible for dealing with the undecomposed errors (e.g. a tool may choose to simply ignore them). Irrelevant unless decompose_errors=True.
  • block_decomposition_from_introducing_remnant_edges (bool) — Defaults to False. Requires that both A B and C D be present elsewhere in the detector error model in order to decompose A B C D into A B ^ C D. Normally, only one of A B or C D needs to appear to allow this decomposition. Remnant edges can be a useful feature for ensuring decomposition succeeds, but they can also reduce the effective code distance by giving the decoder single edges that actually represent multiple errors in the circuit (resulting in the decoder making misinformed choices when decoding). Irrelevant unless decompose_errors=True.