quchip.results.trajectories

Native trajectories with captured model context and lazy analysis views.

Classes

ClickRecords(times, channels, weights, ...)	Jump events of a stochastic solve, in one layout on both backends.
MeasurementRecords(values, channels, edges, ...)	Diffusive measurement currents of a stochastic solve, in one layout on both backends.
TrajectoryResult(native, problem, *[, ...])	Keep a native stochastic result with its storage and weights unchanged.

class quchip.results.trajectories.MeasurementRecords(values, channels, edges, sampling, efficiencies, phases, heterodyne)

Bases: object

Diffusive measurement currents of a stochastic solve, in one layout on both backends.

A monitored channel with collapse operator L, efficiency η and phase φ records the current 2 Re⟨c⟩ + ξ on each interval, in 1/sqrt(ns), with c = √η e^(−iφ) L. Here L includes the square root of its rate. The homodyne noise ξ has variance 1/Δt. A QuTiP heterodyne solve records the quadratures 2 Re⟨c⟩ + ξ_X and 2 Im⟨c⟩ + ξ_Y, and each noise has variance 2/Δt. The values keep this native normalization and the native sampling of the signal on each interval.

Variables:

• values (array_like) – Current of each run, channel and interval, with shape (runs, channels, intervals). A QuTiP heterodyne solve adds a quadrature axis of length 2 before the intervals.

• channels (tuple of str or None) – Channel of each row, as TrajectoryResult.monitor_labels gives them. None when a traced efficiency leaves the native order unknown.

• edges (array_like, shape (intervals + 1,)) – Interval edges in ns: the save times of the solve.

• sampling ({"start", "middle", "end", "mean"}) – Signal sample of each interval. QuTiP takes the signal at the start or the end of an interval, or the mean of both, as its option store_measurement selects, and adds the noise increment divided by Δt. Dynamiqs divides the increment of the integrated record by Δt, so the signal is its mean over the interval.

• efficiencies (tuple) – Efficiency η of each row.

• phases (tuple) – Phase φ of each row, in rad, relative to the integration frame.

• heterodyne (bool) – Whether values has a quadrature axis.

values: Any

channels: tuple[str, ...] | None

edges: Any

sampling: str

efficiencies: tuple[Any, ...]

phases: tuple[Any, ...]

heterodyne: bool

class quchip.results.trajectories.ClickRecords(times, channels, weights, no_click_weight, full)

Bases: object

Jump events of a stochastic solve, in one layout on both backends.

An ensemble mean over runs is the weighted sum Σ_r w_r x_r, plus the no-click run with its weight no_click_weight when the native sampling solves that run apart: QuTiP improved_sampling and the dynamiqs Event(smart_sampling=True). The weights then sum to 1 − no_click_weight.

Variables:

• times (tuple of array_like) – Click times of each run in ns, in increasing order.

• channels (tuple of tuple of str) – Channel of each click, from TrajectoryResult.channel_labels.

• weights (array_like, shape (runs,)) – Sampling weight of each run.

• no_click_weight (float) – Weight of the no-click run that the native sampling solves apart, or 0.

• full (array_like of bool, shape (runs,)) – Whether a run filled its dynamiqs nmaxclick buffer. Its evolution may stop before the end of the solve. QuTiP has no buffer.

times: tuple[Any, ...]

channels: tuple[tuple[str, ...], ...]

weights: Any

no_click_weight: float

full: Any

counts(channel=None)

Return the number of clicks of each run, shape (runs,).

Parameters:

channel (str or None, default=None) – Count only this channel. None counts every channel.

class quchip.results.trajectories.TrajectoryResult(native, problem, *, label_targets=None)

Bases: SimulationResult

Keep a native stochastic result with its storage and weights unchanged.

Parameters:

• native (object) – QuTiP or Dynamiqs stochastic result, including seeds/keys and records.

• problem (SolveProblem) – Captured model, observables, channel order, and integration frame.

• label_targets (LabelTargets or None, optional) – Snapshot of the solve’s chip that prepares label targets.

Variables:

• native (object) – Unmodified native result, with native record conventions and event padding.

• times (array_like, shape (T,)) – Requested save times in ns.

• channel_labels (tuple of str) – Unique resolved channel keys in native jump-operator order.

• monitor_labels (tuple of str or None) – Native record channel order for an explicit selection, or None when unknown.

average()

Return native ensemble averages, including native trajectory weights.

run(index)

Return the analysis of one kept conditional trajectory.

Parameters:

index (int) – Zero-based trajectory index of a run that native storage keeps.

expect(key, index=None)

Return a named native ensemble expectation, shape (T,).

Parameters:

• key (object or str) – Captured observable key.

• index (int or None, default None) – Entry in a list-valued observable. Trajectory selection uses run().

records()

Return the diffusive measurement currents of every kept run.

QuTiP keeps them with the native option store_measurement. result.native keeps the unmodified native fields.

clicks()

Return the jump events of every kept run, with the sampling weight of each run.

run_expect(key, index=None)

Return a named expectation of every kept run, shape (runs, T).

Row i equals run(i).expect(key, index). The native sampling weights do not enter; expect() gives the weighted ensemble mean.

Parameters:

• key (object or str) – Captured observable key.

• index (int or None, default None) – Entry in a list-valued observable.