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Multi-Control-X

The multi-control-X gate applies X gate to one target qubit bit only if the logical AND of all control qubits is satisfied. The multi-control-X function incorporates numerous implementations for the multi-control-X gate, each with a different depth and number of auxiliary qubits. These implementations generically outperform the Gray-code, V-chain and recursive implementations of Ref. [1], as well as the relative-phase Toffoli implementation of Ref. [2]. Given a sufficient number of auxiliary qubits, some implementations allow for logarithmic depth and linear CX-count. The synthesis process selects the appropriate implementation depending on the defined constraints.

Syntax

Function: Mcx

Parameters:

  • num_ctrl_qubits: Optional[int]
  • ctrl_state: Optional[str]
  • arguments: Optional[List[RegisterUserInput]]

The function requires passing either num_ctrl_qubits or arguments. When the function is called with arguments, the last qubit of the last argument is taken to be the target (the qubit on which the X gate is applied). All other qubits are the control qubits. The following two calls are equivalent.

{
  "function": "Mcx",
  "function_params": {
    "num_ctrl_qubits": 3,
    "ctrl_state": "011"
  }
}

{
  "function": "Mcx",
  "function_params": {
    "arguments": [{ "size": 4, "name": "reg" }],
    "ctrl_state": "011"
  }
}

Example

The following example shows how to "mark" a single state in a superposition:

  1. Load a uniform superposition over 3 qubits using StatePreparation.
  2. To mark a single state, e.g., '011', within this superposition, apply the multi-control-X gate on a target qubit where the 3-qubit register serves as the control register.
  3. Specify the state '011' in the ctrl_state field of the multi-control-X gate. Thus, an X gate is applied to the target qubit only for the control state '011'.

As a result, the target qubit is in state '1' only if the control register is in state '011'.

{
  "functions": [
    {
      "name": "main",
      "body": [
          {
            "function": "StatePreparation",
            "function_params": {
              "probabilities": [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125],
              "error_metric": { "KL": {"upper_bound": 0.3}}
            },
            "outputs": "sp_out"
          },
          {
              "function": "Mcx",
              "function_params": {"num_ctrl_qubits": 3, "ctrl_state": "011"},
           "inputs": {
              "CTRL_IN": "sp_out"
            }
          }
      ]
    }
  ]
}

from classiq import Model, QReg, synthesize, show
from classiq.builtin_functions import Mcx, StatePreparation

model = Model()

probabilities = (0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125)
sp_params = StatePreparation(probabilities=probabilities)
x = model.StatePreparation(sp_params)["OUT"]

mcx_params = Mcx(num_ctrl_qubits=len(x), ctrl_state="011")
model.Mcx(mcx_params, in_wires={"CTRL_IN": x})

quantum_program = synthesize(model.get_model())
show(quantum_program)

Different constraints for the circuit depth and number of qubits result in different implementations selected for the Mcx function, as shown.

Mcx example

References

[1] A. Barenco et al, Elementary gates for quantum computation, Phys. Rev. A 52 (1995). https://journals.aps.org/pra/abstract/10.1103/PhysRevA.52.3457

[2] D. Maslov, Advantages of using relative-phase Toffoli gates with an application to multiple control Toffoli optimization, Phys. Rev. A 93 (2016). https://journals.aps.org/pra/abstract/10.1103/PhysRevA.93.022311