cirq.ResultDict

A Result created from a dict mapping measurement keys to measured values.

Inherits From: Result

cirq.ResultDict(
    *,
    params: Optional[cirq.ParamResolver] = None,
    measurements: Optional[Mapping[str, np.ndarray]] = None,
    records: Optional[Mapping[str, np.ndarray]] = None
) -> None

Stores results of executing a circuit for multiple repetitions with one fixed set of parameters. The values for each measurement key are stored as a 2D numpy array. The first (row) index in each array is the repetition number, and the second (column) index is the qubit.

params A ParamResolver of settings used for this result.
measurements A dictionary from measurement gate key to measurement results. The value for each key is a 2-D array of booleans, with the first index running over the repetitions, and the second index running over the qubits for the corresponding measurements.
records A dictionary from measurement gate key to measurement results. The value for each key is a 3D array of booleans, with the first index running over the repetitions, the second index running over "instances" of that key in the circuit, and the last index running over the qubits for the corresponding measurements.

params A ParamResolver of settings used when sampling result.
data Measurements converted to a pandas dataframe.

The rows in the returned data frame correspond to repetitions of the circuit, and the columns correspond to measurement keys, where each element is a big-endian integer representation of measurement outcomes for the measurement key in that repetition. To convert these ints to bits see cirq.big_endian_int_to_bits and similar functions.

measurements A mapping from measurement gate key to measurement results.

The value for each key is a 2-D array of booleans, with the first index running over the repetitions, and the second index running over the qubits for the corresponding measurements.

records A mapping from measurement key to measurement records.

The value for each key is a 3-D array of booleans, with the first index running over circuit repetitions, the second index running over instances of the measurement key in the circuit, and the third index running over the qubits for the corresponding measurements.

repetitions

Methods

dataframe_from_measurements

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@staticmethod
dataframe_from_measurements(
    measurements: Mapping[str, np.ndarray]
) -> pd.DataFrame

Converts the given measurements to a pandas dataframe.

This can be used by subclasses as a default implementation for the data property. Note that subclasses should typically memoize the result to avoid recomputing.

histogram

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histogram(
    *,
    key: TMeasurementKey,
    fold_func: Callable[[Tuple], T] = cirq.big_endian_bits_to_int
) -> collections.Counter

Counts the number of times a measurement result occurred.

For example, suppose that:

- fold_func is not specified
- key='abc'
- the measurement with key 'abc' measures qubits a, b, and c.
- the circuit was sampled 3 times.
- the sampled measurement values were:
    1. a=1 b=0 c=0
    2. a=0 b=1 c=0
    3. a=1 b=0 c=0

Then the counter returned by this method will be:

collections.Counter({
    0b100: 2,
    0b010: 1
})

Where '0b100' is binary for '4' and '0b010' is binary for '2'. Notice that the bits are combined in a big-endian way by default, with the first measured qubit determining the highest-value bit.

Args
key Keys of measurements to include in the histogram.
fold_func A function used to convert a sampled measurement result into a countable value. The input is a list of bits sampled together by a measurement. If this argument is not specified, it defaults to interpreting the bits as a big endian integer.

Returns
A counter indicating how often a measurement sampled various results.

multi_measurement_histogram

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multi_measurement_histogram(
    *,
    keys: Iterable[TMeasurementKey],
    fold_func: Callable[[Tuple], T] = cast(Callable[[Tuple], T], _tuple_of_big_endian_int)
) -> collections.Counter

Counts the number of times combined measurement results occurred.

This is a more general version of the 'histogram' method. Instead of only counting how often results occurred for one specific measurement, this method tensors multiple measurement results together and counts how often the combined results occurred.

For example, suppose that:

- fold_func is not specified
- keys=['abc', 'd']
- the measurement with key 'abc' measures qubits a, b, and c.
- the measurement with key 'd' measures qubit d.
- the circuit was sampled 3 times.
- the sampled measurement values were:
    1. a=1 b=0 c=0 d=0
    2. a=0 b=1 c=0 d=1
    3. a=1 b=0 c=0 d=0

Then the counter returned by this method will be:

collections.Counter({
    (0b100, 0): 2,
    (0b010, 1): 1
})

Where '0b100' is binary for '4' and '0b010' is binary for '2'. Notice that the bits are combined in a big-endian way by default, with the first measured qubit determining the highest-value bit.

Args
fold_func A function used to convert sampled measurement results into countable values. The input is a tuple containing the list of bits measured by each measurement specified by the keys argument. If this argument is not specified, it defaults to returning tuples of integers, where each integer is the big endian interpretation of the bits a measurement sampled.
keys Keys of measurements to include in the histogram.

Returns
A counter indicating how often measurements sampled various results.

__add__

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__add__(
    other: 'cirq.Result'
) -> 'cirq.Result'

__eq__

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__eq__(
    other: Any
) -> bool

Return self==value.