BitArray
qiskit.primitives.BitArray(array, num_bits)
Bases: ShapedMixin
Stores an array of bit values.
This object contains a single, contiguous block of data that represents an array of bitstrings. The last axis is over packed bits, the second last axis is over shots, and the preceding axes correspond to the shape of the pub that was executed to sample these bits.
Parameters
- array (NDArray[np.uint8]) – The
uint8data array. - num_bits (int(opens in a new tab)) – How many bit are in each outcome.
Raises
- TypeError(opens in a new tab) – If the input is not a NumPy array with type
numpy.uint8. - ValueError(opens in a new tab) – If the input array has fewer than two axes, or the size of the last axis is not the smallest number of bytes that can contain
num_bits.
Attributes
array
The raw NumPy array of data.
ndim
num_bits
The number of bits in the register that this array stores data for.
For example, a ClassicalRegister(5, "meas") would result in num_bits=5.
num_shots
The number of shots sampled from the register in each configuration.
More precisely, the length of the second last axis of array.
shape
size
Methods
bitcount
bitcount()
Compute the number of ones appearing in the binary representation of each shot.
Returns
A numpy.uint64-array with shape (*shape, num_shots).
Return type
ndarray(opens in a new tab)[Any(opens in a new tab), dtype(opens in a new tab)[uint64]]
from_bool_array
static from_bool_array(array, order='big')
Construct a new bit array from an array of bools.
Parameters
- array (ndarray(opens in a new tab)[Any(opens in a new tab), dtype(opens in a new tab)[bool_(opens in a new tab)]]) – The array to convert, with “bitstrings” along the last axis.
- order (Literal(opens in a new tab)['big', 'little']) – One of
"big"or"little", indicating whetherarray[..., 0]correspond to the most significant bits or the least significant bits of each bitstring, respectively.
Returns
A new bit array.
Return type
from_counts
static from_counts(counts, num_bits=None)
Construct a new bit array from one or more Counts-like objects.
The counts can have keys that are (uniformly) integers, hexstrings, or bitstrings. Their values represent numbers of occurrences of that value.
Parameters
- counts (Mapping[str(opens in a new tab) |int(opens in a new tab), int(opens in a new tab)] | Iterable[Mapping[str(opens in a new tab) |int(opens in a new tab), int(opens in a new tab)]]) – One or more counts-like mappings with the same number of shots.
- num_bits (int(opens in a new tab) | None) – The desired number of bits per shot. If unset, the biggest value found sets this value.
Returns
A new bit array with shape () for single input counts, or (N,) for an iterable of counts.
Raises
- ValueError(opens in a new tab) – If different mappings have different numbers of shots.
- ValueError(opens in a new tab) – If no counts dictionaries are supplied.
Return type
from_samples
static from_samples(samples, num_bits=None)
Construct a new bit array from an iterable of bitstrings, hexstrings, or integers.
All samples are assumed to be integers if the first one is. Strings are all assumed to be bitstrings whenever the first string doesn’t start with "0x".
Consider pairing this method with reshape() if your samples represent nested data.
Parameters
- samples (Iterable[str(opens in a new tab)] | Iterable[int(opens in a new tab)]) – A list of bitstrings, a list of integers, or a list of hexstrings.
- num_bits (int(opens in a new tab) | None) – The desired number of bits per sample. If unset, the biggest sample provided is used to determine this value.
Returns
A new bit array.
Raises
ValueError(opens in a new tab) – If no strings are given.
Return type
get_bitstrings
get_bitstrings(loc=None)
Return a list of bitstrings.
Parameters
loc (int(opens in a new tab) |tuple(opens in a new tab)[int(opens in a new tab), ...] | None) – Which entry of this array to return a dictionary for. If None, counts from all positions in this array are unioned together.
Returns
A list of bitstrings.
Return type
get_counts
get_counts(loc=None)
Return a counts dictionary with bitstring keys.
Parameters
loc (int(opens in a new tab) |tuple(opens in a new tab)[int(opens in a new tab), ...] | None) – Which entry of this array to return a dictionary for. If None, counts from all positions in this array are unioned together.
Returns
A dictionary mapping bitstrings to the number of occurrences of that bitstring.
Return type
dict(opens in a new tab)[str(opens in a new tab), int(opens in a new tab)]
get_int_counts
get_int_counts(loc=None)
Return a counts dictionary, where bitstrings are stored as ints.
Parameters
loc (int(opens in a new tab) |tuple(opens in a new tab)[int(opens in a new tab), ...] | None) – Which entry of this array to return a dictionary for. If None, counts from all positions in this array are unioned together.
Returns
A dictionary mapping ints to the number of occurrences of that int.
Return type
dict(opens in a new tab)[int(opens in a new tab), int(opens in a new tab)]
reshape
reshape(*shape)
Return a new reshaped bit array.
The num_shots axis is either included or excluded from the reshaping procedure depending on which picture the new shape is compatible with. For example, for a bit array with shape (20, 5) and 64 shots, a reshape to (100,) would leave the number of shots intact, whereas a reshape to (200, 32) would change the number of shots to 32.
Parameters
*shape (int(opens in a new tab) |Iterable(opens in a new tab)[int(opens in a new tab) | Iterable[ShapeInput]]) – The new desired shape.
Returns
A new bit array.
Raises
ValueError(opens in a new tab) – If the size corresponding to your new shape is not equal to either size, or the product of size and num_shots.
Return type