qml.estimator.templates.QROM

class QROM(num_bitstrings, size_bitstring, num_bit_flips=None, restored=True, select_swap_depth=None, wires=None)

Bases: ResourceOperator

Resource class for the Quantum Read-Only Memory (QROM) template.

Parameters:

• num_bitstrings (int) – the number of bitstrings that are to be encoded

• size_bitstring (int) – the length of each bitstring

• num_bit_flips (int | None) – The total number of \(1\)’s in the dataset. Defaults to (num_bitstrings * size_bitstring) // 2, which is half the dataset.

• restored (bool) – Determine if allocated qubits should be reset after the computation (at the cost of higher gate counts). Defaults to True.

• select_swap_depth (int | None) – A parameter \(\lambda\) that determines if data will be loaded in parallel by adding more rows following Figure 1.C of Low et al. (2024). Can be None, 1 or a positive integer power of two. Defaults to None, which sets the depth that minimizes T-gate count.

• wires (WiresLike | None) – The wires the operation acts on (control and target), excluding any additional qubits allocated during the decomposition (e.g select-swap wires).

Resources:

The resources for QROM are derived from the following references:

• restored=False: Uses the Select-Swap tree decomposition from Figure 1.C of Low et al. (2018), further optimized using the measurement-based uncomputation technique described in Berry et al. (2019).

• restored=True: Uses the standard QROM resource accounting from Figure 4 of Berry et al. (2019).

See also

The associated PennyLane operation QROM

Example

The resources for this operation are computed using:

>>> import pennylane.estimator as qre
>>> qrom = qre.QROM(
...     num_bitstrings=10,
...     size_bitstring=4,
... )
>>> print(qre.estimate(qrom))
--- Resources: ---
Total wires: 11
    algorithmic wires: 8
    allocated wires: 3
    zero state: 3
    any state: 0
Total gates : 85
'Toffoli': 8,
'CNOT': 36,
'X': 17,
'Hadamard': 24

Attributes

resource_keys
resource_params	Returns a dictionary containing the minimal information needed to compute the resources.

resource_keys = {'num_bit_flips', 'num_bitstrings', 'restored', 'select_swap_depth', 'size_bitstring'}

resource_params

Returns a dictionary containing the minimal information needed to compute the resources.

Returns:

A dictionary containing the resource parameters:

• num_bitstrings (int): the number of bitstrings that are to be encoded

• size_bitstring (int): the length of each bitstring

• num_bit_flips (int | None): The total number of \(1\)’s in the dataset. Defaults to (num_bitstrings * size_bitstring) // 2, which is half the dataset.

• restored (bool): Determine if allocated qubits should be reset after the computation (at the cost of higher gate counts). Defaults to True.

• select_swap_depth (int | None): A parameter \(\lambda\) that determines if data will be loaded in parallel by adding more rows following Figure 1.C of Low et al. (2024). Can be None, 1 or a positive integer power of two. Defaults to None, which sets the depth that minimizes T-gate count.

Return type:

dict

Methods

adjoint_resource_decomp(target_resource_params)	Returns a list representing the resources of the adjoint of the operator.
controlled_resource_decomp(num_ctrl_wires, ...)	Returns a list representing the resources for a controlled version of the operator.
resource_decomp(num_bitstrings, size_bitstring)	Returns a list of GateCount objects representing the operator's resources.
resource_rep(num_bitstrings, size_bitstring)	Returns a compressed representation containing only the parameters of the Operator that are needed to compute a resource estimation.
single_controlled_res_decomp(num_bitstrings, ...)	The resource decomposition for QROM controlled on a single wire.

classmethod adjoint_resource_decomp(target_resource_params)

Returns a list representing the resources of the adjoint of the operator. Each object represents a quantum gate and the number of times it occurs in the decomposition.

Parameters:

target_resource_params (dict) – A dictionary containing the resource parameters of the target operator.

Resources:

This resources are based on Appendix C of arXiv:1902.02134.

Returns:

A list of GateCount objects, where each object represents a specific quantum gate and the number of times it appears in the decomposition.

Return type:

list[GateCount]

classmethod controlled_resource_decomp(num_ctrl_wires, num_zero_ctrl, target_resource_params)

Returns a list representing the resources for a controlled version of the operator.

Parameters:

• num_ctrl_wires (int) – the number of qubits the operation is controlled on

• num_zero_ctrl (int) – the number of control qubits, that are controlled when in the \(|0\rangle\) state

• target_resource_params (dict) – A dictionary containing the resource parameters of the target operator.

Resources:

The resources for QROM are taken from the following two papers: Low et al. (2024) (Figure 1.C) for restored = False and Berry et al. (2019) (Figure 4) for restored = True.

Note: we use the single-controlled unary iterator trick to implement the Select. This implementation assumes we have access to \(n\) additional work qubits, where \(n = \lceil \log_{2}(N) \rceil\) and \(N\) is the number of batches of unitaries to select.

Returns:

A list of GateCount objects, where each object represents a specific quantum gate and the number of times it appears in the decomposition.

Return type:

list[GateCount]

classmethod resource_decomp(num_bitstrings, size_bitstring, num_bit_flips=None, select_swap_depth=None, restored=True)

Returns a list of GateCount objects representing the operator’s resources.

Parameters:

• num_bitstrings (int) – the number of bitstrings that are to be encoded

• size_bitstring (int) – the length of each bitstring

• num_bit_flips (int | None) – The total number of \(1\)’s in the dataset. Defaults to (num_bitstrings * size_bitstring) // 2, which is half the dataset.

• select_swap_depth (int | None) –

  A parameter \(\lambda\) that determines if data will be loaded in parallel by adding more rows following Figure 1.C of Low et al. (2024). Can be None, 1 or a positive integer power of two. Defaults to None, which sets the depth that minimizes T-gate count.

• restored (bool) – Determine if allocated qubits should be reset after the computation (at the cost of higher gate counts). Defaults to True.

Resources:

The resources for QROM are derived from the following references:

• restored=False: Uses the Select-Swap tree decomposition from Figure 1.C of Low et al. (2018), further optimized using the measurement-based uncomputation technique described in Berry et al. (2019).

• restored=True: Uses the standard QROM resource accounting from Figure 4 of Berry et al. (2019).

Note: we use the unary iterator trick to implement the Select. This implementation assumes we have access to \(n - 1\) additional work qubits, where \(n = \left\lceil \log_{2}(N) \right\rceil\) and \(N\) is the number of batches of unitaries to select.

Returns:

A list of GateCount objects, where each object represents a specific quantum gate and the number of times it appears in the decomposition.

Return type:

list[GateCount]

classmethod resource_rep(num_bitstrings, size_bitstring, num_bit_flips=None, restored=True, select_swap_depth=None)

Returns a compressed representation containing only the parameters of the Operator that are needed to compute a resource estimation.

Parameters:

• num_bitstrings (int) – the number of bitstrings that are to be encoded

• size_bitstring (int) – the length of each bitstring

• num_bit_flips (int | None) – The total number of \(1\)’s in the dataset. Defaults to (num_bitstrings * size_bitstring) // 2, which is half the dataset.

• restored (bool) – Determine if allocated qubits should be reset after the computation (at the cost of higher gate counts). Defaults to True.

• select_swap_depth (int | None) –

  A parameter \(\lambda\) that determines if data will be loaded in parallel by adding more rows following Figure 1.C of Low et al. (2024). Can be None, 1 or a positive integer power of two. Defaults to None, which sets the depth that minimizes T-gate count.

Returns:

the operator in a compressed representation

Return type:

CompressedResourceOp

classmethod single_controlled_res_decomp(num_bitstrings, size_bitstring, num_bit_flips=None, select_swap_depth=None, restored=True)

The resource decomposition for QROM controlled on a single wire.

code/api/pennylane.estimator.templates.QROM

 

Download Python script

 

Download Notebook

 

View on GitHub