qml.estimator.resources_base.Resources¶

class Resources(zeroed_wires, any_state_wires=0, algo_wires=0, gate_types=None)[source]¶

Bases: object

Stores the estimated resource requirements of a quantum circuit.

The estimate() function returns an object of this class. It contains estimates of all resource types tracked by the resource estimation pipeline, including the number of gates and the number of wires.

Parameters:

zeroed_wires (int) – Number of allocated wires returned in the zeroed state.
any_state_wires (int) – Number of allocated wires returned in an unknowned state.
algo_wires (int) – Number of algorithmic wires, default value is 0.
gate_types (dict) – A dictionary mapping operations (ResourceOperator) to their number of occurences in the decomposed circuit.

Example

import pennylane.estimator as qre

def circuit():
    qre.Hadamard()
    qre.CNOT()
    qre.RX(precision=1e-8)
    qre.RX(precision=1e-6)
    qre.AliasSampling(num_coeffs=3)

>>> res = qre.estimate(circuit, gate_set={"RX", "Toffoli", "T", "CNOT", "Hadamard"})()
>>> print(res)
--- Resources: ---
 Total wires: 123
   algorithmic wires: 2
   allocated wires: 121
     zero state: 58
     any state: 63
 Total gates : 2.248E+3
   'RX': 2,
   'Toffoli': 65,
   'T': 868,
   'CNOT': 639,
   'Hadamard': 674

You can also access a more detailed breakdown of resources using the gate_breakdown() method

>>> print(res.gate_breakdown())
RX total: 2
    RX {'precision': 1e-08}: 1
    RX {'precision': 1e-06}: 1
Toffoli total: 65
    Toffoli {'elbow': None}: 4
    Toffoli {'elbow': 'left'}: 61
T total: 868
CNOT total: 639
Hadamard total: 674

Attributes

gate_counts

Produce a dictionary which stores the gate counts using the operator names as keys.

gate_counts¶

Produce a dictionary which stores the gate counts using the operator names as keys.

Returns:

A dictionary with operator names (str) as keys: and the number of occurrences in the circuit (int) as values.

Return type:

dict

Methods

`add_parallel`(other)	Add two Resources objects in parallel.
`add_series`(other)	Add two Resources objects in series.
`gate_breakdown`([gate_set])	Generates a string breakdown of gate counts by type and parameters, optionally for a specific set of gates.
`multiply_parallel`(scalar)	Scale a Resources object in parallel
`multiply_series`(scalar)	Scale a Resources object in series

add_parallel(other)[source]¶

Add two Resources objects in parallel.

When combining resources for parallel execution, the following rules apply:

Zeroed wires: The maximum of the zeroed auxiliary wires is used, as they can be reused across parallel circuits.
Any state wires: The any_state wires are added together, as they cannot be reused between circuits.
Algorithmic wires: The algo_wires are added together, as each circuit is a separate unit running simultaneously.
Gates: The gates from each circuit are added together.

Parameters:: other (Resources) – other resource object to combine with
Returns:: combined resources
Return type:: Resources

Example

>>> import pennylane.estimator as qre
>>> gate_set = {"X", "Y", "Z", "CNOT", "T", "S", "Hadamard"}
>>> res1 = qre.estimate(qre.Toffoli(), gate_set)
>>> res2 = qre.estimate(qre.QFT(num_wires=4), gate_set)
>>> res_in_parallel = res1.add_parallel(res2)
>>> print(res_in_parallel)
--- Resources: ---
 Total wires: 9
    algorithmic wires: 7
    allocated wires: 2
         zero state: 2
         any state: 0
 Total gates : 838
  'T': 796,
  'CNOT': 28,
  'Z': 2,
  'S': 3,
  'Hadamard': 9

add_series(other)[source]¶

Add two Resources objects in series.

When combining resources for serial execution, the following rules apply:

Zeroed wires: The total zeroed auxiliary wires are the maximum of the zeroed wires in each circuit, as they can be reused.
Any state wires: The any_state wires are added together, as they cannot be reused.
Algorithmic wires: The total algo_wires are the maximum of the algo_wires from each circuit.
Gates: The gates from each circuit are added together.

Parameters:: other (Resources) – the other resource object to add in series with
Returns:: combined resources
Return type:: Resources

Example

>>> import pennylane.estimator as qre
>>> gate_set = {"X", "Y", "Z", "CNOT", "T", "S", "Hadamard"}
>>> res1 = qre.estimate(qre.Toffoli(), gate_set)
>>> res2 = qre.estimate(qre.QFT(num_wires=4), gate_set)
>>> res_in_series = res1.add_series(res2)
>>> print(res_in_series)
--- Resources: ---
 Total wires: 6
    algorithmic wires: 4
    allocated wires: 2
         zero state: 2
         any state: 0
 Total gates : 838
  'T': 796,
  'CNOT': 28,
  'Z': 2,
  'S': 3,
  'Hadamard': 9

gate_breakdown(gate_set=None)[source]¶

Generates a string breakdown of gate counts by type and parameters, optionally for a specific set of gates.

Parameters:: gate_set (list) – A list of gate names to break down. If None, details will be provided for all gate types.

Example

>>> import pennylane.estimator as qre
>>> def circ():
...     qre.SemiAdder(10)
...     qre.Toffoli()
...     qre.RX(precision=1e-5)
...     qre.RX(precision=1e-7)
>>> res1 = qre.estimate(circ, gate_set=['Toffoli', 'RX', 'CNOT', 'Hadamard'])()
>>> print(res1.gate_breakdown())
RX total: 2
    RX {'precision': 1e-05}: 1
    RX {'precision': 1e-07}: 1
Toffoli total: 10
    Toffoli {'elbow': 'left'}: 9
    Toffoli {'elbow': None}: 1
CNOT total: 60
Hadamard total: 27

multiply_parallel(scalar)[source]¶

Scale a Resources object in parallel

Parameters:: scalar (int) – integer value by which to scale the resources
Returns:: scaled resources
Return type:: Resources

Example

>>> import pennylane.estimator as qre
>>> gate_set = {"X", "Y", "Z", "CNOT", "T", "S", "Hadamard"}
>>> res1 = qre.estimate(qre.Toffoli(), gate_set)
>>> res_in_parallel = res1.multiply_parallel(3)
>>> print(res_in_parallel)
--- Resources: ---
 Total wires: 11
    algorithmic wires: 9
    allocated wires: 2
         zero state: 2
         any state: 0
 Total gates : 72
  'T': 12,
  'CNOT': 30,
  'Z': 6,
  'S': 9,
  'Hadamard': 15

multiply_series(scalar)[source]¶

Scale a Resources object in series

Parameters:: scalar (int) – integer value by which to scale the resources
Returns:: scaled resources
Return type:: Resources

Example

>>> import pennylane.estimator as qre
>>> gate_set = {"X", "Y", "Z", "CNOT", "T", "S", "Hadamard"}
>>> res1 = qre.estimate(qre.Toffoli(), gate_set)
>>> res_in_series = res1.multiply_series(3)
>>> print(res_in_series)
--- Resources: ---
 Total wires: 5
    algorithmic wires: 3
    allocated wires: 2
         zero state: 2
         any state: 0
 Total gates : 72
  'T': 12,
  'CNOT': 30,
  'Z': 6,
  'S': 9,
  'Hadamard': 15

code/api/pennylane.estimator.resources_base.Resources

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qml.estimator.resources_base.Resources¶

Attributes

Methods

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