Random Circuits

qiskit.circuit.random.random_circuit(num_qubits, depth, max_operands=4, measure=False, conditional=False, reset=False, seed=None, num_operand_distribution=None)

Generate random circuit of arbitrary size and form.

This function will generate a random circuit by randomly selecting gates from the set of standard gates in qiskit.circuit.library.standard_gates. For example:

from qiskit.circuit.random import random_circuit
 
circ = random_circuit(2, 2, measure=True)
circ.draw(output='mpl')

Parameters

• num_qubits (int) – number of quantum wires
• depth (int) – layers of operations (i.e. critical path length)
• max_operands (int) – maximum qubit operands of each gate (between 1 and 4)
• measure (bool) – if True, measure all qubits at the end
• conditional (bool) – if True, insert middle measurements and conditionals
• reset (bool) – if True, insert middle resets
• seed (int) – sets random seed (optional)
• num_operand_distribution (dict) – a distribution of gates that specifies the ratio of 1-qubit, 2-qubit, 3-qubit, …, n-qubit gates in the random circuit. Expect a deviation from the specified ratios that depends on the size of the requested random circuit. (optional)

Returns

constructed circuit

Return type

QuantumCircuit

Raises

CircuitError – when invalid options given

qiskit.circuit.random.random_circuit_from_graph(interaction_graph, min_2q_gate_per_edge=1, max_operands=2, measure=False, conditional=False, reset=False, seed=None, insert_1q_oper=True, prob_conditional=0.1, prob_reset=0.1)

Generate random circuit of arbitrary size and form which strictly respects the interaction graph passed as argument. Interaction Graph is a graph G=(V, E) where V are the qubits in the circuit, and, E is the set of two-qubit gate interactions between two particular qubits in the circuit.

This function will generate a random circuit by randomly selecting 1Q and 2Q gates from the set of standard gates in qiskit.circuit.library.standard_gates. The user can attach a numerical value as a metadata to the edge of the graph indicating the edge weight for that particular edge, These edge weights would be normalized to the probabilities of the edges getting selected. If all the edge weights are passed as None, then the probability of each qubit-pair of getting selected is set to 1/N, where N is the number of edges in the interaction_graph passed in, i.e each edge is drawn uniformly. If any weight of an edge is set as zero, that particular edge will not be included in the output circuit.

Passing a list of tuples of control qubit, target qubit and associated probability is also acceptable as a way to specify an interaction graph.

If numerical values are present as probabilities but some/any are None, or negative, when max_operands=2 this will raise a ValueError.

If max_operands is set to 1, then there are no 2Q operations, so no need to take care of the edges, in such case the function will return a circuit from the random_circuit function, which would be passed with the max_operands as 1.

If max_operands is set to 2, then in every while-iteration 2Q gates are chosen randomly, and qubit-pairs which exist in the input interaction graph are also chosen randomly based on the probability attached to the qubit-pair. Then in a for-iteration 2Q gates are applied to the randomly chosen qubit-pairs with the aim to reach the count of 2Q on any qubit-pair to min_2q_gate_per_edge criteria as soon as possible within a particular iteration. Now, if some qubits are still idle after applying 2Q gates for that particular iteration, then randomly chosen 1Q gates are applied to those idle qubits if insert_1q_oper is set to True.

Example:

from qiskit.circuit.random.utils import random_circuit_from_graph
import rustworkx as rx
pydi_graph = rx.PyDiGraph()
pydi_graph.add_nodes_from(range(7))
cp_map = [(0, 1, 0.18), (1, 2, 0.15), (2, 3, 0.15), (3, 4, 0.22), (4, 5, 0.13), (5, 6, 0.17)]
pydi_graph.add_edges_from(cp_map)
# cp_map can be passed in directly as interaction_graph
qc = random_circuit_from_graph(pydi_graph, min_2q_gate_per_edge=2, seed=12345)
qc.draw(output='mpl')

Parameters

• interaction_graph (PyGraph | PyDiGraph | List[Tuple[int, int, float]]) – Interaction Graph
• min_2q_gate_per_edge (int) – Minimum number of times every qubit-pair must be used in the random circuit. (optional, default:1)
• max_operands (int) – maximum qubit operands of each gate(should be 1 or 2) (optional, default:2)
• measure (bool) – if True, measure all qubits at the end. (optional, default: False)
• conditional (bool) – if True, insert middle measurements and conditionals. (optional, default: False)
• reset (bool) – if True, insert middle resets. (optional, default: False)
• seed (int) – sets random seed. (If None, a random seed is chosen) (optional)
• insert_1q_oper (bool) – Insert 1Q operations to the circuit. (optional, default: True)
• prob_conditional (float) – Probability less than 1.0, this is used to control the occurrence of conditionals in the circuit. (optional, default: 0.1)
• prob_reset (float) – Probability less than 1.0, this is used to control the occurrence of reset in the circuit. (optional, default: 0.1)

Returns

constructed circuit

Return type

QuantumCircuit

Raises

• CircuitError – When max_operands is not 1 or 2.
• CircuitError – When max_operands is set to 1, but no 1Q operations are allowed by setting insert_1q_oper to false.
• CircuitError – When the interaction graph has no edges, so only 1Q gates are possible in the circuit, but insert_1q_oper is set to False.
• CircuitError – When an invalid interaction graph object is passed.
• ValueError – Given max_operands=2, when any edge have probability None but not all, or any of the probabilities are negative.

qiskit.circuit.random.random_clifford_circuit(num_qubits, num_gates, gates='all', seed=None)

Generate a pseudo-random Clifford circuit.

This function will generate a Clifford circuit by randomly selecting the chosen amount of Clifford gates from the set of standard gates in qiskit.circuit.library.standard_gates. For example:

from qiskit.circuit.random import random_clifford_circuit
 
circ = random_clifford_circuit(num_qubits=2, num_gates=6)
circ.draw(output='mpl')

Parameters

• num_qubits (int) – number of quantum wires.
• num_gates (int) – number of gates in the circuit.
• gates (list[str]) – optional list of Clifford gate names to randomly sample from. If "all" (default), use all Clifford gates in the standard library.
• seed (int | np.random.Generator) – sets random seed/generator (optional).

Returns

constructed circuit

Return type

QuantumCircuit