mindquantum.algorithm.mapping.SABRE

class mindquantum.algorithm.mapping.SABRE(circuit: Circuit, topology: QubitsTopology)[source]

SABRE (SWAP-based BidiREctional heuristic search) algorithm for qubit mapping optimization.

Due to physical constraints in real quantum hardware, not all qubits can directly interact with each other. SABRE algorithm enables arbitrary quantum circuits to run on specific quantum hardware topologies by inserting SWAP gates and remapping qubits. It employs a bidirectional heuristic search method that minimizes a cost function considering both current and future gate operations to find optimal mapping solutions.

Reference:: Gushu Li, Yufei Ding, Yuan Xie: "Tackling the Qubit Mapping Problem for NISQ-Era Quantum Devices", ASPLOS 2019. https://arxiv.org/abs/1809.02573

Parameters

circuit (Circuit) – The quantum circuit to be mapped. Currently only supports circuits composed of single-qubit gates and two-qubit gates (including controlled gates).
topology (QubitsTopology) – The hardware qubit topology. Currently only supports connected coupling graphs.

Examples

>>> from mindquantum.core.circuit import Circuit
>>> from mindquantum.core.gates import RX, X
>>> from mindquantum.device import GridQubits
>>> from mindquantum.algorithm.mapping import SABRE
>>> # Create a simple quantum circuit
>>> circ = Circuit()
>>> circ += RX('a').on(0)
>>> circ += RX('b').on(1)
>>> circ += RX('c').on(2)
>>> circ += X.on(1, 0)
>>> circ += X.on(2, 1)
>>> circ += X.on(0, 2)
>>> # Create a 2x2 grid topology
>>> topo = GridQubits(2, 2)
>>> # Use SABRE for mapping
>>> solver = SABRE(circ, topo)
>>> new_circ, init_map, final_map = solver.solve()

solve(iter_num: int = 5, w: float = 0.5, delta1: float = 0.3, delta2: float = 0.2)[source]

Solve the qubit mapping problem using the SABRE algorithm.

The method employs bidirectional heuristic search to find optimal qubit mapping solutions. Key steps include: 1. Generate random initial mapping 2. Execute forward-backward-forward traversal to optimize initial mapping 3. Perform final forward traversal with optimized mapping to generate physical circuit with SWAP gates

Parameters

iter_num (int, optional) – Number of forward-backward-forward traversal iterations. Each iteration starts with a different initial mapping. Default: 5.
w (float, optional) – Weight parameter in cost function H = H_current + w * H_future. Larger w (>0.5) favors future operations, potentially reducing circuit depth; Smaller w (<0.5) favors current operations, potentially reducing total gate count. Default: 0.5.
delta1 (float, optional) – Decay parameter for single-qubit gates. Affects how algorithm updates decay values after single-qubit operations. Default: 0.3.
delta2 (float, optional) – Decay parameter for two-qubit gates (CNOT, SWAP). Controls how algorithm distributes SWAP operations in space and time. Since one SWAP equals three CNOTs, SWAP operations add 3*delta2 to decay values. Default: 0.2.

Returns

Quantum circuit compatible with hardware: topology after adding SWAP gates

initial_mapping (List[int]): Mapping from logical to physical qubits at the start of execution
final_mapping (List[int]): Mapping from logical to physical qubits at the end of execution

Return type

mapped_circuit (Circuit)

Examples

>>> # Use default parameters
>>> new_circ, init_map, final_map = solver.solve()
>>> # Or customize parameters
>>> new_circ, init_map, final_map = solver.solve(iter_num=10, w=0.7)