Quantum Programming Examples

[Compiled]

quantum_circuit bell_state() -> qubit[2] {
  qubit[2] q = [|0⟩, |0⟩]
  
  # Apply Hadamard to first qubit
  q[0] = H(q[0])
  
  # Apply CNOT to create entanglement
  q[1] = CNOT(q[0], q[1])
  
  return q
}

fn main() {
  let bell = bell_state()
  let result = measure_all(bell)
  print("Bell state measurement:", result)
}

[Compiled]

quantum_circuit teleport(qubit message) -> qubit {
  # Create Bell pair
  qubit[2] bell = bell_state()
  
  # Alice's operations
  qubit alice_qubit = bell[0]
  qubit bob_qubit = bell[1]
  
  # Entangle message with Alice's qubit
  alice_qubit = CNOT(message, alice_qubit)
  message = H(message)
  
  # Measure Alice's qubits
  let m1 = measure(message)
  let m2 = measure(alice_qubit)
  
  # Bob's corrections
  if m2 {
    bob_qubit = X(bob_qubit)
  }
  if m1 {
    bob_qubit = Z(bob_qubit)
  }
  
  return bob_qubit
}

[Compiled]

quantum_circuit grover_search(
  qubit[n] database,
  fn oracle: qubit[n] -> qubit[n]
) -> qubit[n] {
  # Initialize superposition
  for i in 0..n {
    database[i] = H(database[i])
  }
  
  # Grover iterations
  let iterations = floor(π/4 * sqrt(2^n))
  for _ in 0..iterations {
    database = oracle(database)
    database = grover_diffuser(database)
  }
  
  return database
}

# Oracle function (marks the solution)
fn mark_solution(qubit[n] q) -> qubit[n] {
  # Flip the phase of the target state
  # Implementation depends on the problem
  return q
}

[Compiled]

quantum_circuit qft(qubit[n] q) -> qubit[n] {
  for i in 0..n {
    q[i] = H(q[i])
    for j in (i+1)..n {
      let phase = 2 * π / (2^(j - i + 1))
      q[j] = controlled_phase(phase, q[i], q[j])
    }
  }
  return reverse(q)
}

[Compiled]

fn variational_quantum_eigensolver(
  hamiltonian: matrix,
  ansatz: quantum_circuit
) -> f64 {
  let optimizer = Adam(learning_rate: 0.01)
  let params = initialize_parameters()
  
  for iteration in 0..100 {
    # Prepare quantum state
    qubit[n] q = ansatz(params)
    
    # Measure expectation value
    let energy: f64 = expectation(hamiltonian, q)
    
    # Update parameters classically
    let gradient = calculate_gradient(energy, params)
    params = optimizer.update(params, gradient)
    
    print("Iteration", iteration, "Energy:", energy)
  }
  
  return energy
}

[Compiled]

# Encoding
quantum_circuit encode_bit_flip(qubit logical) -> qubit[3] {
  qubit[3] physical = [|0⟩, |0⟩, |0⟩]
  physical[0] = logical
  physical[1] = CNOT(logical, physical[1])
  physical[2] = CNOT(logical, physical[2])
  return physical
}

# Error correction
quantum_circuit correct_bit_flip(qubit[3] encoded) -> qubit[3] {
  # Measure stabilizers
  let syndrome1 = measure(CNOT(encoded[0], encoded[1]))
  let syndrome2 = measure(CNOT(encoded[0], encoded[2]))
  
  # Apply correction based on syndrome
  if syndrome1 && !syndrome2 {
    encoded[1] = X(encoded[1])  # Flip second qubit
  } else if !syndrome1 && syndrome2 {
    encoded[2] = X(encoded[2])  # Flip third qubit
  } else if syndrome1 && syndrome2 {
    encoded[0] = X(encoded[0])  # Flip first qubit
  }
  
  return encoded
}

[Compiled]

fn run_on_hardware() {
  # Connect to IBM Quantum
  quantum_backend ibm = connect("ibm_quantum", api_key: "...")
  
  # Prepare circuit
  qubit[5] q = prepare_circuit()
  
  # Execute with multiple shots
  let result = execute(q, ibm, shots: 1024)
  
  # Analyze results
  for (state, count) in result.counts() {
    print("State:", state, "Count:", count)
  }
}