task_id stringlengths 17 19 | prompt stringlengths 122 728 | canonical_solution stringlengths 97 1.53k | test stringlengths 192 1.54k | entry_point stringlengths 3 50 | difficulty_scale stringclasses 3
values |
|---|---|---|---|---|---|
qiskitHumanEval/100 | Create a pass manager to decompose the single qubit gates into gates of the dense subset ['t', 'tdg', 'h'] in the given circuit.
You must implement this using a function named `sol_kit_decomp` with the following arguments: circuit. | from qiskit.transpiler.passes import SolovayKitaev
from qiskit.transpiler import PassManager
from qiskit.circuit import QuantumCircuit
def sol_kit_decomp(circuit):
pm = PassManager([SolovayKitaev()])
circ_dec = pm.run(circuit)
return circ_dec
| from qiskit.transpiler.passes import SolovayKitaev
from qiskit.transpiler import PassManager
from qiskit.circuit import QuantumCircuit
def check(candidate):
import numpy as np
from qiskit.circuit.library import EfficientSU2
from qiskit.quantum_info import Operator
circ = EfficientSU2(3).decompose()
... | sol_kit_decomp | basic |
qiskitHumanEval/101 | Return the circuit for the graph state of the coupling map of the Fake Kyoto backend. Hint: Use the networkx library to convert the coupling map to a dense adjacency matrix.
You must implement this using a function named `get_graph_state` with no arguments. | from qiskit_ibm_runtime.fake_provider import FakeKyoto
from qiskit.circuit.library import GraphState
import networkx as nx
from qiskit.circuit import QuantumCircuit
def get_graph_state():
backend = FakeKyoto()
coupling_map = backend.coupling_map
G = nx.Graph()
G.add_edges_from(coupling_map)
adj_mat... | from qiskit_ibm_runtime.fake_provider import FakeKyoto
from qiskit.circuit.library import GraphState
import networkx as nx
from qiskit.circuit import QuantumCircuit
def check(candidate):
from collections import OrderedDict
from qiskit.transpiler.passes import UnitarySynthesis
from qiskit.transpiler import P... | get_graph_state | intermediate |
qiskitHumanEval/102 | Transpile the circuit for the phi plus bell state for FakeKyoto, FakeKyiv and FakeAuckland using the level 1 preset pass manager and return the backend name with the lowest number of instructions.
You must implement this using a function named `backend_with_least_instructions` with no arguments. | from qiskit.circuit import QuantumCircuit
from qiskit_ibm_runtime.fake_provider import FakeKyoto, FakeKyiv, FakeAuckland
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
def backend_with_least_instructions():
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)
backends = [FakeKyiv(... | from qiskit.circuit import QuantumCircuit
from qiskit_ibm_runtime.fake_provider import FakeKyoto, FakeKyiv, FakeAuckland
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
def check(candidate):
backend_can = candidate()
assert backend_can in "fake_auckland" or "auckland" in backend_c... | backend_with_least_instructions | basic |
qiskitHumanEval/103 | Return the list of names of all the fake providers of type FakeBackendV2 which contains ecr gates in its available operations.
You must implement this using a function named `fake_providers_v2_with_ecr` with no arguments. | import importlib
import inspect
from qiskit_ibm_runtime.fake_provider import fake_backend
def fake_providers_v2_with_ecr():
fake_provider_module = importlib.import_module("qiskit_ibm_runtime.fake_provider")
fake_providers = {}
for name, obj in inspect.getmembers(fake_provider_module):
if inspect.is... | import importlib
import inspect
from qiskit_ibm_runtime.fake_provider import fake_backend
def check(candidate):
providers_can = candidate()
providers_exp = [
"FakeCusco",
"FakeKawasaki",
"FakeKyiv",
"FakeKyoto",
"FakeOsaka",
"FakePeekskill",
"FakeQuebec",
... | fake_providers_v2_with_ecr | basic |
qiskitHumanEval/104 | Transpile the 4-qubit QFT circuit using preset passmanager with optimization level 3 and seed transpiler = 1234 in FakeOsaka, FakeSherbrooke and FakeBrisbane. Compute the cost of the instructions by penalizing the two qubit gate with a cost of 5, rz gates with a cost 1 and other gates with a cost 2 and return the value... | from qiskit_ibm_runtime.fake_provider import FakeOsaka, FakeSherbrooke, FakeBrisbane
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit.circuit.library import QFT
def backend_with_lowest_complexity():
qc = QFT(4)
backends = [FakeOsaka(), FakeSherbrooke(), FakeBrisbane()]... | from qiskit_ibm_runtime.fake_provider import FakeOsaka, FakeSherbrooke, FakeBrisbane
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit.circuit.library import QFT
def check(candidate):
complexity_can = candidate()
complexity_exp = 194
assert complexity_can == complexi... | backend_with_lowest_complexity | intermediate |
qiskitHumanEval/105 | Initialize a CNOTDihedral element from a QuantumCircuit consist of 2-qubits with cx gate on qubit 0 and 1 and t gate on qubit 0 and return.
You must implement this using a function named `initialize_cnot_dihedral` with no arguments. | from qiskit import QuantumCircuit
from qiskit.quantum_info import CNOTDihedral
def initialize_cnot_dihedral():
circ = QuantumCircuit(2)
# Apply gates
circ.cx(0, 1)
circ.t(0)
elem = CNOTDihedral(circ)
return elem
| from qiskit import QuantumCircuit
from qiskit.quantum_info import CNOTDihedral
def check(candidate):
result = candidate()
assert isinstance(result, CNOTDihedral), f'Expected result to be CNOTDihedral, but got {type(result)}'
assert result.linear.tolist() == [[1, 0], [1, 1]]
assert str(result.poly) == "0... | initialize_cnot_dihedral | basic |
qiskitHumanEval/106 | Create two Quantum Circuits of 2 qubits. First quantum circuit should have a cx gate on qubits 0 and 1 and a T gate on qubit 0. The second one is the same but with an additional X gate on qubit 1. Convert the two quantum circuits into CNOTDihedral elements and return the composed circuit.
You must implement this using ... | from qiskit import QuantumCircuit
from qiskit.quantum_info import CNOTDihedral
def compose_cnot_dihedral():
circ1 = QuantumCircuit(2)
# Apply gates
circ1.cx(0, 1)
circ1.t(0)
elem1 = CNOTDihedral(circ1)
circ2 = circ1.copy()
circ2.x(1)
elem2 = CNOTDihedral(circ2)
composed_elem = elem1... | from qiskit import QuantumCircuit
from qiskit.quantum_info import CNOTDihedral
def check(candidate):
result = candidate()
assert isinstance(result, CNOTDihedral), f'Expected result to be CNOTDihedral, but got {type(result)}'
assert result.linear.tolist() == [[1, 0], [0, 1]]
assert str(result.poly) == "0... | compose_cnot_dihedral | intermediate |
qiskitHumanEval/107 | Create two ScalarOp objects with dimension 2 and coefficient 2, compose them together, and return the resulting ScalarOp.
You must implement this using a function named `compose_scalar_ops` with no arguments. | from qiskit.quantum_info import ScalarOp
def compose_scalar_ops():
op1 = ScalarOp(2, 2)
op2 = ScalarOp(2, 2)
composed_op = op1.compose(op2)
return composed_op
| from qiskit.quantum_info import ScalarOp
def check(candidate):
result = candidate()
assert isinstance(result, ScalarOp), f'Expected result to be ScalarOp, but got {type(result)}'
assert result.coeff == 4
assert result.input_dims() == (2,)
check(compose_scalar_ops) | compose_scalar_ops | basic |
qiskitHumanEval/108 | Initialize Choi matrices for the given data1 and data2 as inputs. Compute data1 adjoint, and then return the data1 Choi matrix, its adjoint and the composed choi matrices in order.
You must implement this using a function named `initialize_adjoint_and_compose` with the following arguments: data1, data2. | from qiskit.quantum_info import Choi
import numpy as np
def initialize_adjoint_and_compose(data1, data2):
choi1 = Choi(data1)
choi2 = Choi(data2)
adjoint_choi1 = choi1.adjoint()
composed_choi = choi1.compose(choi2)
return choi1, adjoint_choi1, composed_choi
| from qiskit.quantum_info import Choi
import numpy as np
def check(candidate):
data = np.eye(4)
choi, adjoint_choi, composed_choi = candidate(data, data)
assert isinstance(choi, Choi), f'Expected choi to be Choi, but got {type(choi)}'
assert choi.dim == (2, 2), f'Expected dimensions to be (2, 2), but got... | initialize_adjoint_and_compose | basic |
qiskitHumanEval/109 | Create a parameterized quantum circuit using minimum resources whose statevector output cover the equatorial plane of the surface of the bloch sphere.
You must implement this using a function named `circuit` with no arguments. | from qiskit.circuit import QuantumCircuit, Parameter
def circuit():
qc = QuantumCircuit(1)
qc.h(0)
theta = Parameter('th')
qc.rz(theta,0)
return qc
| from qiskit.circuit import QuantumCircuit, Parameter
def check(candidate):
import numpy as np
from qiskit.quantum_info import Statevector
def statevector_to_bloch_angles(state_vector):
alpha = state_vector[0]
beta = state_vector[1]
norm = np.sqrt(np.abs(alpha)**2 + np.abs(beta)**2)
... | circuit | intermediate |
qiskitHumanEval/110 | Given a clifford circuit return a list of n random clifford circuits which are equivalent to the given circuit up to a relative and absolute tolerance of 0.4.
You must implement this using a function named `equivalent_clifford_circuit` with the following arguments: circuit, n. | from qiskit import QuantumCircuit
from qiskit.quantum_info import random_clifford, Operator
def equivalent_clifford_circuit(circuit, n):
op_or = Operator(circuit)
num_qubits = circuit.num_qubits
qc_list = []
counter = 0
while counter< n:
qc = random_clifford(num_qubits).to_circuit()
... | from qiskit import QuantumCircuit
from qiskit.quantum_info import random_clifford, Operator
def check(candidate):
from qiskit.quantum_info import Clifford
qc_comp = random_clifford(5).to_circuit()
op_comp = Operator(qc_comp)
can_circ_list = candidate(qc_comp, 10)
for item in can_circ_list:
a... | equivalent_clifford_circuit | intermediate |
qiskitHumanEval/111 | Return an ansatz to create a quantum dataset of pure states distributed equally across the bloch sphere. Use minimum number of gates in the ansatz.
You must implement this using a function named `circuit` with no arguments. | from qiskit.circuit import QuantumCircuit, Parameter
def circuit():
qc = QuantumCircuit(1)
p1 = Parameter("p1")
p2 = Parameter("p2")
qc.rx(p1,0)
qc.ry(p2,0)
return qc
| from qiskit.circuit import QuantumCircuit, Parameter
def check(candidate):
assert candidate().num_parameters >= 2 , "The circuit doesn't cover the bloch sphere."
assert candidate().num_parameters <= 5 , "The circuit is too long"
check(circuit) | circuit | intermediate |
qiskitHumanEval/112 | Create a quantum circuit using LieTrotter for a list of Pauli strings and times. Each Pauli string is associated with a corresponding time in the 'times' list. The function should return the resulting QuantumCircuit.
You must implement this using a function named `create_product_formula_circuit` with the following argu... | from qiskit.quantum_info import Operator
from qiskit.circuit.library import PauliEvolutionGate
from qiskit.synthesis import LieTrotter
from qiskit import QuantumCircuit
from qiskit.quantum_info import Pauli, SparsePauliOp
def create_product_formula_circuit(pauli_strings, times, order, reps):
qc = QuantumCircuit(le... | from qiskit.quantum_info import Operator
from qiskit.circuit.library import PauliEvolutionGate
from qiskit.synthesis import LieTrotter
from qiskit import QuantumCircuit
from qiskit.quantum_info import Pauli, SparsePauliOp
def check(candidate):
pauli_strings = ["X", "Y", "Z"]
times = [1.0, 2.0, 3.0]
order = ... | create_product_formula_circuit | intermediate |
qiskitHumanEval/113 | Remove barriers from the given quantum circuit and calculate the depth before and after removal.
Return a PropertySet with 'depth_before', 'depth_after', and 'width' properties.
The function should only remove barriers and not perform any other optimizations.
You must implement this using a function named `calculate_de... | from qiskit import QuantumCircuit
from qiskit.transpiler import PassManager, PropertySet
from qiskit.transpiler.passes import RemoveBarriers
def calculate_depth_after_barrier_removal(qc):
property_set = PropertySet()
property_set["depth_before"] = qc.depth()
property_set["width"] = qc.width()
pass... | from qiskit import QuantumCircuit
from qiskit.transpiler import PassManager, PropertySet
from qiskit.transpiler.passes import RemoveBarriers
def check(candidate):
qc = QuantumCircuit(3)
qc.h(0)
qc.barrier()
qc.cx(0, 1)
qc.barrier()
qc.cx(1, 2)
qc.measure_all()
property_set = candida... | calculate_depth_after_barrier_removal | intermediate |
qiskitHumanEval/114 | Create a CouplingMap with a specific coupling list, then modify it by adding an edge and a physical qubit.
The initial coupling list is [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5]].
Add an edge (5, 6), and add a physical qubit "7".
You must implement this using a function named `create_and_modify_coupling_map` with no argu... | from qiskit.transpiler import CouplingMap
def create_and_modify_coupling_map():
coupling_list = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5]]
cmap = CouplingMap(couplinglist=coupling_list)
cmap.add_edge(5, 6)
cmap.add_physical_qubit(7)
return cmap
| from qiskit.transpiler import CouplingMap
def check(candidate):
cmap = candidate()
edges = set(cmap.get_edges())
assert edges == { (0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5,6) }
assert len(cmap.physical_qubits) == 8
check(create_and_modify_coupling_map) | create_and_modify_coupling_map | intermediate |
qiskitHumanEval/115 | Create a Target object for a 2-qubit system and add UGate and CXGate instructions with specific properties.
- Add UGate for both qubits (0 and 1) with parameters 'theta', 'phi', and 'lambda'.
- Add CXGate for qubit pairs (0,1) and (1,0).
- All instructions should have nonzero 'duration' and 'error' properties set.
You ... | from qiskit.transpiler import Target, InstructionProperties
from qiskit.circuit.library import UGate, CXGate
from qiskit.circuit import Parameter
def create_target():
gmap = Target()
theta, phi, lam = [Parameter(p) for p in ("theta", "phi", "lambda")]
u_props = {
(0,): InstructionProperties(duratio... | from qiskit.transpiler import Target, InstructionProperties
from qiskit.circuit.library import UGate, CXGate
from qiskit.circuit import Parameter
def check(candidate):
target = candidate()
assert isinstance(target, Target)
instructions = target.instructions
u_gate_instructions = [inst for inst in instr... | create_target | intermediate |
qiskitHumanEval/116 | Synthesize an evolution gate using MatrixExponential for a given Pauli string and time.
The Pauli string can be any combination of 'I', 'X', 'Y', and 'Z'.
Return the resulting QuantumCircuit.
You must implement this using a function named `synthesize_evolution_gate` with the following arguments: pauli_string, time. | from qiskit.circuit.library import PauliEvolutionGate
from qiskit.synthesis import MatrixExponential
from qiskit import QuantumCircuit
from qiskit.quantum_info import Pauli, Operator
import numpy as np
def synthesize_evolution_gate(pauli_string, time):
pauli = Pauli(pauli_string)
evolution_gate = PauliEvolutio... | from qiskit.circuit.library import PauliEvolutionGate
from qiskit.synthesis import MatrixExponential
from qiskit import QuantumCircuit
from qiskit.quantum_info import Pauli, Operator
import numpy as np
def check(candidate):
pauli_string = "X"
time = 1.0
qc = candidate(pauli_string, time)
assert isi... | synthesize_evolution_gate | intermediate |
qiskitHumanEval/117 | Decompose a 4x4 unitary using the TwoQubitBasisDecomposer with CXGate as the basis gate.
Return the resulting QuantumCircuit.
You must implement this using a function named `decompose_unitary` with the following arguments: unitary. | from qiskit.synthesis import TwoQubitBasisDecomposer
from qiskit.quantum_info import Operator, random_unitary
from qiskit.circuit.library import CXGate
from qiskit import QuantumCircuit
import numpy as np
def decompose_unitary(unitary):
decomposer = TwoQubitBasisDecomposer(CXGate())
return decomposer(unitary)
| from qiskit.synthesis import TwoQubitBasisDecomposer
from qiskit.quantum_info import Operator, random_unitary
from qiskit.circuit.library import CXGate
from qiskit import QuantumCircuit
import numpy as np
def check(candidate):
unitary = random_unitary(4)
try:
qc = candidate(unitary)
assert isins... | decompose_unitary | intermediate |
qiskitHumanEval/118 | Create a QuantumCircuit with a C3SXGate applied to the first four qubits.
You must implement this using a function named `create_c3sx_circuit` with no arguments. | from qiskit import QuantumCircuit
from qiskit.circuit.library import C3SXGate
def create_c3sx_circuit():
qc = QuantumCircuit(4)
c3sx_gate = C3SXGate()
qc.append(c3sx_gate, [0, 1, 2, 3])
return qc
| from qiskit import QuantumCircuit
from qiskit.circuit.library import C3SXGate
def check(candidate):
qc = candidate()
assert isinstance(qc, QuantumCircuit)
c3sx_instructions = [inst for inst in qc.data if isinstance(inst.operation, C3SXGate)]
assert len(c3sx_instructions) == 1
assert c3sx_i... | create_c3sx_circuit | intermediate |
qiskitHumanEval/119 | Create a QuantumCircuit with a CDKMRippleCarryAdder applied to the qubits.
The kind of adder can be 'full', 'half', or 'fixed'.
You must implement this using a function named `create_ripple_carry_adder_circuit` with the following arguments: num_state_qubits, kind. | from qiskit.circuit.library import CDKMRippleCarryAdder
from qiskit import QuantumCircuit
from qiskit.quantum_info import Operator
def create_ripple_carry_adder_circuit(num_state_qubits, kind):
adder = CDKMRippleCarryAdder(num_state_qubits, kind)
qc = QuantumCircuit(adder.num_qubits)
qc.append(adder.to_ins... | from qiskit.circuit.library import CDKMRippleCarryAdder
from qiskit import QuantumCircuit
from qiskit.quantum_info import Operator
def check(candidate):
qc_full = candidate(3, "full")
assert isinstance(qc_full, QuantumCircuit), "The function should return a QuantumCircuit"
op_full = Operator(qc_full)
... | create_ripple_carry_adder_circuit | intermediate |
qiskitHumanEval/120 | Create a QuantumCircuit with a Diagonal gate applied to the qubits.
The diagonal elements are provided in the list 'diag'.
You must implement this using a function named `create_diagonal_circuit` with the following arguments: diag. | from qiskit.circuit.library import Diagonal
from qiskit import QuantumCircuit
from qiskit.quantum_info import Operator
def create_diagonal_circuit(diag):
diagonal_gate = Diagonal(diag)
qc = QuantumCircuit(diagonal_gate.num_qubits)
qc.append(diagonal_gate.to_instruction(), range(diagonal_gate.num_qubits))
... | from qiskit.circuit.library import Diagonal
from qiskit import QuantumCircuit
from qiskit.quantum_info import Operator
def check(candidate):
diag = [1, 1j, -1, -1j]
qc = candidate(diag)
assert isinstance(qc, QuantumCircuit), "The function should return a QuantumCircuit"
op_circuit = Operator(qc)
... | create_diagonal_circuit | intermediate |
qiskitHumanEval/121 | Create a quantum circuit with one qubit and two classical bits. The qubit's operation depends on its measurement outcome: if it measures to 1 (|1> state), it flips the qubit's state back to |0> using an X gate. The qubit's initial state is randomized using a Hadamard gate. When building the quantum circuit make sure th... | from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
def conditional_two_qubit_circuit():
qr = QuantumRegister(1)
cr = ClassicalRegister(2, 'c')
qc = QuantumCircuit(qr, cr)
qc.h(qr[0])
qc.measure(qr[0], cr[0])
with qc.if_test((cr[0], 1)):
qc.x(qr[0])
qc.measure(qr[... | from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
def check(candidate):
from qiskit_aer import AerSimulator
from qiskit_ibm_runtime import Sampler
from qiskit_ibm_runtime.options import SamplerOptions
qc = candidate()
assert isinstance(qc, QuantumCircuit)
assert qc.num_qubits... | conditional_two_qubit_circuit | basic |
qiskitHumanEval/122 | Generate an EfficientSU2 circuit with the given number of qubits, 1 reps and make entanglement circular.
Then use the Qiskit Transpiler service with the AI flag turned on, use the ibm_brisbane backend and an optimization level of 3 and transpile the generated circuit.
You must implement this using a function named `ai... | from qiskit.circuit.library import EfficientSU2
from qiskit_ibm_transpiler.transpiler_service import TranspilerService
def ai_transpiling(num_qubits):
circuit = EfficientSU2(num_qubits, entanglement="circular", reps=1)
transpiler_ai_true = TranspilerService(
backend_name="ibm_brisbane",
ai=True... | from qiskit.circuit.library import EfficientSU2
from qiskit_ibm_transpiler.transpiler_service import TranspilerService
def check(candidate):
import qiskit
num_qubits = 3
backend_name = "ibm_brisbane"
ai_flag = True
optimization_level = 3
og_circuit = EfficientSU2(num_qubits, entanglement="circul... | ai_transpiling | basic |
qiskitHumanEval/123 | Instantiate a FakeBelemV2 backend and return the plot of its error_map.
You must implement this using a function named `backend_error_map` with no arguments. | from qiskit.visualization import plot_error_map
from qiskit_ibm_runtime.fake_provider import FakeBelemV2
def backend_error_map():
backend = FakeBelemV2()
return plot_error_map(backend)
| from qiskit.visualization import plot_error_map
from qiskit_ibm_runtime.fake_provider import FakeBelemV2
def check(candidate):
from matplotlib.figure import Figure
result = candidate()
assert type(result) == Figure
assert len(result.axes) == 5
assert result.get_suptitle() == "fake_belem Error Map"
... | backend_error_map | basic |
qiskitHumanEval/124 | Generate a noise model from the Fake Cairo V2 backend.
You must implement this using a function named `gen_noise_model` with no arguments. | from qiskit_ibm_runtime.fake_provider import FakeCairoV2
from qiskit_aer.noise import NoiseModel
def gen_noise_model():
backend = FakeCairoV2()
noise_model = NoiseModel.from_backend(backend)
return noise_model
| from qiskit_ibm_runtime.fake_provider import FakeCairoV2
from qiskit_aer.noise import NoiseModel
def check(candidate):
backend = FakeCairoV2()
expected_nm = NoiseModel.from_backend(backend)
noise_model = candidate()
assert type(noise_model) == NoiseModel
assert noise_model == expected_nm
check(gen_... | gen_noise_model | basic |
qiskitHumanEval/125 | Given a QuantumCircuit, convert it into a gate equivalent to the action of the input circuit and return it.
You must implement this using a function named `circ_to_gate` with the following arguments: circ. | from qiskit.converters import circuit_to_gate
def circ_to_gate(circ):
circ_gate = circuit_to_gate(circ)
return circ_gate
| from qiskit.converters import circuit_to_gate
def check(candidate):
from qiskit import QuantumCircuit, QuantumRegister
from qiskit.circuit.gate import Gate
from qiskit.quantum_info import Operator
from qiskit.circuit.library import ZGate
q = QuantumRegister(3, "q")
circ = QuantumCircuit(q)
c... | circ_to_gate | basic |
qiskitHumanEval/126 | Create two quantum operators using Hadamard gate that differ only by a global phase. Calculate the process fidelity between these two operators and return the process fidelity value.
You must implement this using a function named `calculate_phase_difference_fidelity` with no arguments. | from qiskit.circuit.library import HGate
from qiskit.quantum_info import Operator, process_fidelity
import numpy as np
def calculate_phase_difference_fidelity():
op_a = Operator(HGate())
op_b = np.exp(1j * 0.5) * Operator(HGate())
fidelity = process_fidelity(op_a, op_b)
return fidelity
| from qiskit.circuit.library import HGate
from qiskit.quantum_info import Operator, process_fidelity
import numpy as np
def check(candidate):
result = candidate()
assert isinstance(result, float)
assert abs(result - 1.0) < 1e-6
check(calculate_phase_difference_fidelity) | calculate_phase_difference_fidelity | basic |
qiskitHumanEval/127 | Using qiskit's random_circuit function, generate a circuit with 4 qubits and a depth of 3 that measures all qubits at the end. Use the seed value 17 and return the generated circuit.
You must implement this using a function named `random_circuit_depth` with no arguments. | from qiskit.circuit.random import random_circuit
from qiskit import QuantumCircuit
def random_circuit_depth():
circuit = random_circuit(4, 3, measure=True, seed = 17)
return circuit
| from qiskit.circuit.random import random_circuit
from qiskit import QuantumCircuit
def check(candidate):
from qiskit.circuit.random import random_circuit
from qiskit import QuantumCircuit
# Run the candidate function
result = candidate()
# Check if the output is a QuantumCircuit
assert isinsta... | random_circuit_depth | basic |
qiskitHumanEval/128 | Create a quantum circuit with 4 qubits and 4 classical bits. Apply Hadamard gates to the first three qubits, measure them with three classical registers,
and conditionally apply an X gate to the fourth qubit based on the XOR of the three classical bits. Finally, measure the fourth qubit into another classical register.... | from qiskit.circuit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit.circuit.classical import expr
def conditional_quantum_circuit():
qr = QuantumRegister(4, "q")
cr = ClassicalRegister(4, "c")
circ = QuantumCircuit(qr, cr)
circ.h(qr[0:3])
circ.measure(qr[0:3], cr[0:3])
_... | from qiskit.circuit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit.circuit.classical import expr
def check(candidate):
from qiskit_aer import AerSimulator
from qiskit_ibm_runtime import Sampler, SamplerOptions
from qiskit_ibm_runtime.options import SamplerOptions
circ_res = candid... | conditional_quantum_circuit | basic |
qiskitHumanEval/129 | Given the name of a quantum backend, retrieve the properties of the specified backend and identify the qubit pair with the highest error rate among its RZ gates.
Return this qubit pair along with the corresponding error rate as a tuple. If the backend doesn't support the RZ gate, return None.
You must implement this u... | from qiskit_ibm_runtime import QiskitRuntimeService
def find_highest_rz_error_rate(backend_name):
service = QiskitRuntimeService(channel="ibm_quantum")
backend = service.backend(backend_name)
backend_properties = backend.properties()
try:
rz_props = backend_properties.gate_property("rz")
e... | from qiskit_ibm_runtime import QiskitRuntimeService
def check(candidate):
service = QiskitRuntimeService(channel="ibm_quantum")
backend = service.least_busy(filters=lambda b : ("rz" in b.basis_gates))
max_rz_error_pair, max_rz_error_rate = candidate(backend.name)
assert isinstance(max_rz_error_pair... | find_highest_rz_error_rate | intermediate |
qiskitHumanEval/130 | Create a quantum circuit with 'n' qubits. Apply Hadamard gates to the second and third qubits.
Then apply CNOT gates between the second and fourth qubits, and between the third and fifth qubits.
Finally give the inverse of the quantum circuit.
You must implement this using a function named `inv_circuit` with the follow... | from qiskit.circuit import QuantumCircuit
def inv_circuit(n):
qc = QuantumCircuit(n)
for i in range(2):
qc.h(i+1)
for i in range(2):
qc.cx(i+1, i+2+1)
return qc.inverse()
| from qiskit.circuit import QuantumCircuit
def check(candidate):
n = 5
qc_inv = candidate(n)
expected_qc = QuantumCircuit(n)
for i in range(2):
expected_qc.h(i+1)
for i in range(2):
expected_qc.cx(i+1, i+2+1)
expected_qc_inv = expected_qc.inverse()
assert qc_inv, Quant... | inv_circuit | basic |
qiskitHumanEval/131 | Given the fake backend name, retrieve information about the backend's number of qubits, coupling map, and supported instructions using the Qiskit Runtime Fake Provider,
and create a dictionary containing the info. The dictionary must have the following keys: 'num_qubits' (the number of qubits), 'coupling_map'
(the co... | from qiskit_ibm_runtime.fake_provider import FakeCairoV2
def backend_info(backend_name):
backend = FakeCairoV2()
config = backend.configuration()
dict_result = {"num_qubits": config.num_qubits, "coupling_map": config.coupling_map, "supported_instructions": config.supported_instructions}
return dict_re... | from qiskit_ibm_runtime.fake_provider import FakeCairoV2
def check(candidate):
backend = FakeCairoV2()
binfo_dict = candidate(backend.name)
backend_config = backend.configuration()
assert isinstance(binfo_dict, dict)
assert binfo_dict["num_qubits"] == backend_config.num_qubits
assert binfo_dict[... | backend_info | basic |
qiskitHumanEval/132 | Generate 6 random quantum circuits, each with 3 qubits, depth of 2 and measure set to True; using the random_circuit Qiskit function, with seed 17. Then use a
preset pass manager to optimize these circuits with an optimization level of 1, the backend for the pass manager should be FakeManilaV2, and set the seed value ... | from qiskit.circuit.random import random_circuit
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_ibm_runtime import Batch, Sampler
from qiskit_ibm_runtime.fake_provider import FakeManilaV2
def run_batched_random_circuits():
fake_manila = FakeManilaV2()
pm = generate_p... | from qiskit.circuit.random import random_circuit
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_ibm_runtime import Batch, Sampler
from qiskit_ibm_runtime.fake_provider import FakeManilaV2
def check(candidate):
result = candidate()
assert isinstance(result, list)
a... | run_batched_random_circuits | basic |
qiskitHumanEval/133 | Find and return jobs submitted in the last three months using QiskitRuntimeService.
You must implement this using a function named `find_recent_jobs` with no arguments. | import datetime
from qiskit_ibm_runtime import QiskitRuntimeService
def find_recent_jobs():
three_months_ago = datetime.datetime.now() - datetime.timedelta(days=90)
service = QiskitRuntimeService()
jobs_in_last_three_months = service.jobs(created_after=three_months_ago)
return jobs_in_last_three_months... | import datetime
from qiskit_ibm_runtime import QiskitRuntimeService
def check(candidate):
result = candidate()
assert isinstance(result, list)
for job in result:
assert hasattr(job, "job_id")
assert hasattr(job, "creation_date")
assert job.creation_date.replace(tzinfo=None) >= (datet... | find_recent_jobs | basic |
qiskitHumanEval/134 | Using the QiskitRuntimeService, retrieve the backends that meet the following criteria: they are real quantum devices, they are operational, and they have a
minimum of 20 qubits. Then, return a list of dictionaries, each containing the backend's name, number of qubits, and the list of supported instruction names.
Ensu... | from qiskit_ibm_runtime import QiskitRuntimeService
def backend_info():
service = QiskitRuntimeService()
backends = service.backends(simulator=False, operational=True, min_num_qubits=20)
backend_info = []
for b in backends:
backend_info.append({"backend_name": b.name, "num_qubits": b.num_qubits... | from qiskit_ibm_runtime import QiskitRuntimeService
def check(candidate):
result = candidate()
assert isinstance(result, list)
service = QiskitRuntimeService()
backends = service.backends(simulator=False, operational=True, min_num_qubits=20)
backend_info = []
for b in backends:
back... | backend_info | basic |
qiskitHumanEval/135 | Construct a noise model with specific readout error properties for different qubits. For qubit 0, a readout of 1 has a 20% probability
of being erroneously read as 0, and a readout of 0 has a 30% probability of being erroneously read as 1. For all other qubits, a readout of 1 has a 3%
probability of being erroneously... | from qiskit_aer.noise import NoiseModel, ReadoutError
def noise_model_with_readouterror():
noise_model = NoiseModel()
p0given1_other = 0.03
p1given0_other = 0.02
readout_error_other = ReadoutError(
[
[1 - p1given0_other, p1given0_other],
[p0given1_other, 1 - p0given1_oth... | from qiskit_aer.noise import NoiseModel, ReadoutError
def check(candidate):
result = candidate()
assert isinstance(result, NoiseModel), "Result should be a NoiseModel instance"
global_error = result.to_dict()["errors"][0]['probabilities']
assert global_error == [[0.98, 0.02], [0.03, 0.97]]
... | noise_model_with_readouterror | intermediate |
qiskitHumanEval/136 | Return a list of ten density matrices which are pure up to a tolerance of ε.
You must implement this using a function named `pure_states` with the following arguments: ε. | from qiskit.quantum_info import entropy, random_density_matrix, DensityMatrix
def pure_states(ε):
entropy_list = []
while len(entropy_list) <= 9:
density_matrix = random_density_matrix(dims = 2)
if entropy(density_matrix) < ε:
entropy_list.append(density_matrix)
return entropy_l... | from qiskit.quantum_info import entropy, random_density_matrix, DensityMatrix
def check(candidate):
tol = 0.01
list_can = candidate(tol)
assert len(list_can) == 10," Length of the list is not 10"
for _, item in enumerate(list_can):
assert isinstance(item, DensityMatrix), "The list doesn't contai... | pure_states | intermediate |
qiskitHumanEval/137 | Return a dataset of density matrices whose 2-qubit entanglement of formation is within given tolerance.
You must implement this using a function named `entanglement_dataset` with the following arguments: ε. | from qiskit.quantum_info import random_density_matrix, entanglement_of_formation
def entanglement_dataset(ε):
entanglement_data = []
while len(entanglement_data) <= 9:
density_matrix = random_density_matrix(dims = 4)
if entanglement_of_formation(density_matrix)>=ε:
entanglement_data... | from qiskit.quantum_info import random_density_matrix, entanglement_of_formation
def check(candidate):
from qiskit.quantum_info import DensityMatrix
tol = 0.1
can_list = candidate(tol)
assert len(can_list) == 10, "Length of list is not 10"
for _, item in enumerate(can_list):
assert isinstanc... | entanglement_dataset | intermediate |
qiskitHumanEval/138 | Return a list of density matrices whose mutual information is greater than the given tolerance.
You must implement this using a function named `mutual_information_dataset` with the following arguments: ε. | from qiskit.quantum_info import mutual_information, random_density_matrix
def mutual_information_dataset(ε):
mutual_information_list = []
while len(mutual_information_list)<= 9:
density_matrix = random_density_matrix(dims = 4)
if mutual_information(density_matrix) >= ε:
mutual_infor... | from qiskit.quantum_info import mutual_information, random_density_matrix
def check(candidate):
from qiskit.quantum_info import mutual_information, DensityMatrix, random_density_matrix
import numpy as np
tolerances = [0.1, 0.5, 1.0] # Different values of ε to test
num_matrices = 10
for tol in tol... | mutual_information_dataset | intermediate |
qiskitHumanEval/139 | Return the schmidt decomposition coefficients and the subsystem vectors for the given density matrix and partition.
You must implement this using a function named `schmidt_test` with the following arguments: data, qargs_B. | from qiskit.quantum_info import schmidt_decomposition
def schmidt_test(data, qargs_B):
return schmidt_decomposition(data, qargs_B)
| from qiskit.quantum_info import schmidt_decomposition
def check(candidate):
from qiskit.quantum_info import random_statevector
rs = random_statevector(dims = 16)
qargs = [0,1]
schmidt_decomp = candidate(rs, qargs)
for _, item in enumerate(schmidt_decomp):
assert item[0]>=0, "Schmidt coeffici... | schmidt_test | basic |
qiskitHumanEval/140 | Return a list of ten probability vectors each of length 16 whose shannon entropy is greater than a given value.
You must implement this using a function named `shannon_entropy_data` with the following arguments: ε. | from qiskit.quantum_info import shannon_entropy
import numpy as np
def shannon_entropy_data(ε):
shannon_data = []
while len(shannon_data) <= 9:
rand_prob = np.random.randn(16)
if shannon_entropy(rand_prob) >= ε:
shannon_data.append(rand_prob)
return shannon_data
| from qiskit.quantum_info import shannon_entropy
import numpy as np
def check(candidate):
tol = 1
can_list = candidate(tol)
assert len(can_list) == 10, " The length of the list is not 10"
for _, item in enumerate(can_list):
assert shannon_entropy(item)>= tol, "Shannon entropy not greater than giv... | shannon_entropy_data | basic |
qiskitHumanEval/141 | Return a list of ten anticommutators for the given pauli.
You must implement this using a function named `anticommutators` with the following arguments: pauli. | from qiskit.quantum_info import anti_commutator, SparsePauliOp
from qiskit.quantum_info import random_pauli
import numpy as np
def anticommutators(pauli):
def is_multiple_of_identity(matrix):
identity_matrix = np.eye(matrix.shape[0])
scalar = matrix[0,0]
return np.allclose(matrix, scalar*id... | from qiskit.quantum_info import anti_commutator, SparsePauliOp
from qiskit.quantum_info import random_pauli
import numpy as np
def check(candidate):
def is_multiple_of_identity(matrix):
if matrix.shape[0] != matrix.shape[1]:
return False # Not a square matrix
identity_matrix = np.eye(ma... | anticommutators | intermediate |
qiskitHumanEval/142 | Return a list of 10 single qubit density matrices whose purity is greater than 0.5.
You must implement this using a function named `purity_dataset` with no arguments. | from qiskit.quantum_info import random_density_matrix, purity, DensityMatrix
import numpy as np
def purity_dataset():
purity_dataset_list = []
while len(purity_dataset_list)<=9:
rand_density_matrix = random_density_matrix(dims=2)
if np.abs(purity(rand_density_matrix))>=0.5:
purity_d... | from qiskit.quantum_info import random_density_matrix, purity, DensityMatrix
import numpy as np
def check(candidate):
can_list = candidate()
assert len(can_list) == 10, "Number of density matrices is not 10"
for _, item in enumerate(can_list):
assert isinstance(item, DensityMatrix)
assert np... | purity_dataset | intermediate |
qiskitHumanEval/143 | Return a list of ten pairs of one qubit state vectors whose state fidelity is greater than 0.9.
You must implement this using a function named `fidelity_dataset` with no arguments. | from qiskit.quantum_info import random_statevector, state_fidelity, Statevector
def fidelity_dataset():
fidelity_dataset_list = []
while len(fidelity_dataset_list)<=9:
sv_1 = random_statevector(2)
sv_2 = random_statevector(2)
if state_fidelity(sv_1, sv_2) >= 0.9:
fidelity_da... | from qiskit.quantum_info import random_statevector, state_fidelity, Statevector
def check(candidate):
can_list = candidate()
assert len(can_list) == 10, "Length of returned list is not 10"
for _, item in enumerate(can_list):
assert isinstance(item[0], Statevector)
assert isinstance(item[1], ... | fidelity_dataset | intermediate |
qiskitHumanEval/144 | Return a list of 10 density matrices whose concurrence is 0.
You must implement this using a function named `concurrence_dataset` with no arguments. | from qiskit.quantum_info import random_density_matrix, concurrence, DensityMatrix
def concurrence_dataset():
concurrence_dataset_list = []
while len(concurrence_dataset_list) <= 9:
rand_mat = random_density_matrix(dims = 4)
if concurrence(rand_mat) == 0:
concurrence_dataset_list.app... | from qiskit.quantum_info import random_density_matrix, concurrence, DensityMatrix
def check(candidate):
can_mat_list = candidate()
assert len(can_mat_list) == 10, "The list doesn't contain 10 elements"
for _, item in enumerate(can_mat_list):
assert isinstance(item, DensityMatrix)
assert conc... | concurrence_dataset | intermediate |
qiskitHumanEval/145 | Return the inverse qft circuit for n qubits.
You must implement this using a function named `qft_inverse` with the following arguments: n. | from qiskit.circuit.library import QFT
from qiskit import QuantumCircuit
from qiskit.quantum_info import Operator
def qft_inverse(n):
return QFT(num_qubits=n, approximation_degree=0, inverse=True)
| from qiskit.circuit.library import QFT
from qiskit import QuantumCircuit
from qiskit.quantum_info import Operator
def check(candidate):
from qiskit.circuit.library import QFT
from qiskit import QuantumCircuit
from qiskit.quantum_info import Operator
# Test multiple values of n
for n in [1, 2, 3, 5,... | qft_inverse | intermediate |
qiskitHumanEval/146 | Generate Qiskit code that sets up a StagedPassManager with a trivial layout using PassManager for the least busy backend available.
You must implement this using a function named `trivial_layout` with no arguments. | from qiskit.transpiler import PassManager, StagedPassManager
from qiskit_ibm_runtime import QiskitRuntimeService
from qiskit.transpiler.passes.layout.trivial_layout import TrivialLayout
def trivial_layout():
pm_opt = StagedPassManager()
pm_opt.layout = PassManager()
backend = QiskitRuntimeService().least_b... | from qiskit.transpiler import PassManager, StagedPassManager
from qiskit_ibm_runtime import QiskitRuntimeService
from qiskit.transpiler.passes.layout.trivial_layout import TrivialLayout
def check(candidate):
from qiskit.transpiler import PassManager, StagedPassManager
from qiskit.transpiler.passes.layout.trivia... | trivial_layout | basic |
qiskitHumanEval/147 | Add a multi-controlled-Y operation to qubit 4, controlled by qubits 0-3.
You must implement this using a function named `mcy` with the following arguments: qc. | from qiskit import QuantumCircuit
from qiskit.circuit.library import YGate
def mcy(qc):
mcy_gate = YGate().control(num_ctrl_qubits=4)
qc.append(mcy_gate, range(5))
return qc
| from qiskit import QuantumCircuit
from qiskit.circuit.library import YGate
from qiskit.quantum_info import Operator
def check(candidate):
expected = QuantumCircuit(6)
expected.h([0, 4, 5])
mcy_gate = YGate().control(num_ctrl_qubits=4)
expected.append(mcy_gate, range(5))
solution = QuantumCircuit(6... | mcy | intermediate |
qiskitHumanEval/148 | Add SWAPs to route `qc` for the `backend` object's coupling map, but don't transform any gates.
You must implement this using a function named `swap_map` with the following arguments: qc, backend. | from qiskit import QuantumCircuit
from qiskit.transpiler.passes import BasicSwap
from qiskit.converters import circuit_to_dag, dag_to_circuit
from qiskit_ibm_runtime import IBMBackend
def swap_map(qc, backend):
swap_pass = BasicSwap(coupling_map=backend.coupling_map)
dag = circuit_to_dag(qc)
mapped_dag = s... | from qiskit import QuantumCircuit
from qiskit.transpiler.passes import BasicSwap
from qiskit.converters import circuit_to_dag, dag_to_circuit
from qiskit_ibm_runtime import IBMBackend
def check(candidate):
from qiskit_ibm_runtime.fake_provider import FakeKyiv
from qiskit.circuit.random import random_circuit
... | swap_map | intermediate |
qiskitHumanEval/149 | Return the most common result as a string of `1`s and `0`s.
You must implement this using a function named `most_common_result` with the following arguments: bits. | from statistics import mode
from qiskit.primitives import BitArray
def most_common_result(bits):
return mode(bits.get_bitstrings())
| from statistics import mode
from qiskit.primitives import BitArray
def check(candidate):
test_inputs = [
{"001": 50, "101": 3},
{"1": 1},
{"01101": 302, "10010": 10, "101": 209},
]
for counts in test_inputs:
bit_array = BitArray.from_counts(counts)
expected = max(coun... | most_common_result | intermediate |
qiskitHumanEval/150 | Add a sub-circuit to the quantum circuit `qc` that applies a series of operations for `n` iterations using the `for_loop`.
In each iteration `i`, perform the following:
1. Apply a `RY` rotation on qubit 0 with an angle of `pi/n * i`.
2. Apply a Hadamard gate to qubit 0 and a CNOT gate between qubits 0 and 1 to create ... | from qiskit import QuantumCircuit
from numpy import pi
def for_loop_circuit(qc, n):
with qc.for_loop(range(n)) as i:
qc.ry(pi/n*i, 0)
qc.h(0)
qc.cx(0, 1)
qc.measure(0, 0)
with qc.if_test((qc.clbits[0], 1)):
qc.break_loop()
return qc
| from qiskit import QuantumCircuit
from numpy import pi
def check(candidate):
from qiskit.circuit.library import RYGate, HGate, CXGate, Measure
from qiskit.circuit.controlflow import IfElseOp
from qiskit.circuit import CircuitInstruction
qc = QuantumCircuit(2,1)
solution = candidate(qc, 2)
... | for_loop_circuit | difficult |
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