Add stim.Circuit.with_feedback_ensuring_flows

[Strilanc]

This can be done by Gaussian elimination over the flow generators. Alternatively, simulate the circuit with measurements forced to the reference sample to validate the flows up to sign/feedback. Then, for each measurement, force it to the opposite value (if possible) and see which flows have their sign change. Then solve Paulis that invert in the right way.

[Strilanc]

This is my best attempt so far at this method. It doesn't work on non-unitary operations, and it doesn't work if the expected flows don't exactly match up with the computed flow generators.

def mbqc_to_unitary_by_solving_feedback(
        mbqc_circuit: stim.Circuit,
        *,
        desired_flow_generators: list[stim.Flow] | None = None,
        num_relevant_qubits: int,
) -> stim.Circuit:
    """Converts an MBQC circuit to a unitary circuit by adding Pauli feedback.

    Args:
        mbqc_circuit: The circuit to add feedback to.
        desired_flow_generators: Defaults to None (clear all measurement
            dependence and negative signs). When set to a list, it specifies
            reference signs and measurement dependencies to keep.
        num_relevant_qubits: The number of non-ancillary qubits.

    Returns:
        The circuit with added Pauli feedback.
    """
    num_qubits = mbqc_circuit.num_qubits
    num_measurements = mbqc_circuit.num_measurements
    num_added_dof = num_relevant_qubits * 2

    # Add feedback from extra qubits, so `flow_generators` includes X/Z feedback terms.
    augmented_circuit = mbqc_circuit.copy()
    for q in range(num_relevant_qubits):
        augmented_circuit.append('M', [num_qubits + q*2])
        augmented_circuit.append('CX', [stim.target_rec(-1), q])
        augmented_circuit.append('M', [num_qubits + q*2 + 1])
        augmented_circuit.append('CZ', [stim.target_rec(-1), q])

    # Diagonalize the flow generators.
    # - Remove terms mentioning ancillary qubits.
    flow_table: list[tuple[stim.PauliString, stim.PauliString, list[int]]] = [
        (f.input_copy(), f.output_copy(), f.measurements_copy())
        for f in augmented_circuit.flow_generators()
    ]
    num_solved_flows = 0
    pivot_funcs = [
        lambda f, i: len(f[0]) > i and 1 <= f[0][i] <= 2,
        lambda f, i: len(f[0]) > i and 2 <= f[0][i] <= 3,
        lambda f, i: len(f[1]) > i and 1 <= f[1][i] <= 2,
        lambda f, i: len(f[1]) > i and 2 <= f[1][i] <= 3,
    ]
    def elim_step(q: int, func: Callable):
        nonlocal num_solved_flows
        for pivot in range(num_solved_flows, len(flow_table)):
            if func(flow_table[pivot], q):
                break
        else:
            return
        for row in range(len(flow_table)):
            if pivot != row and func(flow_table[row], q):
                i1, o1, m1 = flow_table[row]
                i2, o2, m2 = flow_table[pivot]
                flow_table[row] = (i1 * i2, o1 * o2, xor_sorted(m1 + m2))
        if pivot != num_solved_flows:
            flow_table[num_solved_flows], flow_table[pivot] = flow_table[pivot], flow_table[num_solved_flows]
        num_solved_flows += 1

    for q in range(num_relevant_qubits, augmented_circuit.num_qubits):
        for func in pivot_funcs:
            elim_step(q, func)
    flow_table = flow_table[num_solved_flows:]
    num_solved_flows = 0
    for q in range(num_relevant_qubits):
        for func in pivot_funcs[:2]:
            elim_step(q, func)
    for q in range(num_relevant_qubits):
        for func in pivot_funcs[2:]:
            elim_step(q, func)


    if desired_flow_generators is not None:
        # TODO: make this work even if the desired generators are in a different basis.
        for k in range(len(desired_flow_generators)):
            i1 = desired_flow_generators[k].input_copy()
            i2 = flow_table[k][0]
            assert (i1 * i2).weight == 0
            o1 = desired_flow_generators[k].output_copy()
            o2 = flow_table[k][1]
            assert (o1 * o2).weight == 0
            flow_table[k] = (i1 * i2.sign, o1 * o2.sign, xor_sorted(flow_table[k][2] + desired_flow_generators[k].measurements_copy()))

    # Construct a feedback table describing how each measurement affects each flow.
    feedback_table: list[np.ndarray] = []
    for g in flow_table:
        i2 = g[0].pauli_indices()
        o2 = g[1].pauli_indices()
        if i2 and i2[0] >= num_relevant_qubits:
            continue
        if o2 and o2[0] >= num_relevant_qubits:
            continue
        row2 = np.zeros(num_measurements + num_added_dof + 1, dtype=np.bool_)
        for k in g[2]:
            row2[k] ^= 1
        row2[-1] ^= g[0].sign * g[1].sign == -1
        feedback_table.append(row2)

    # Diagonalize the feedback table.
    num_solved = 0
    for k in range(num_measurements, num_measurements + num_added_dof):
        for pivot in range(num_solved, len(feedback_table)):
            if feedback_table[pivot][k]:
                break
        else:
            continue
        for row in range(len(feedback_table)):
            if pivot != row and feedback_table[row][k]:
                feedback_table[row] ^= feedback_table[pivot]
        if pivot != num_solved:
            feedback_table[num_solved] ^= feedback_table[pivot]
            feedback_table[pivot] ^= feedback_table[num_solved]
            feedback_table[num_solved] ^= feedback_table[pivot]
        num_solved += 1

    result = mbqc_circuit.copy()

    # Convert from table to dicts.
    cx = collections.defaultdict(set)
    cz = collections.defaultdict(set)
    for q in range(num_added_dof):
        assert np.array_equal(np.flatnonzero(feedback_table[q][-num_added_dof-1:-1]), [q])
    for q in range(num_relevant_qubits):
        if feedback_table[2*q + 0][-1]:
            cx[None].add(q)
        for m in np.flatnonzero(feedback_table[2*q + 0][:-num_added_dof-1]):
            cx[m - num_measurements].add(q)
    for q in range(num_relevant_qubits):
        if feedback_table[2*q + 1][-1]:
            cz[None].add(q)
        for m in np.flatnonzero(feedback_table[2*q + 1][:-num_added_dof]):
            cz[m - num_measurements].add(q)

    # Output deterministic Paulis.
    for q in sorted(cx[None] - cz[None]):
        result.append('X', [q])
    for q in sorted(cx[None] & cz[None]):
        result.append('Y', [q])
    for q in sorted(cz[None] - cx[None]):
        result.append('Z', [q])
    cx.pop(None, None)
    cz.pop(None, None)

    # Output feedback Paulis.
    for k in cx.keys():
        for q in sorted(cx[k] - cz[k]):
            result.append('CX', [stim.target_rec(k), q])
    for k in cx.keys():
        for q in sorted(cx[k] & cz[k]):
            result.append('CY', [stim.target_rec(k), q])
    for k in cz.keys():
        for q in sorted(cz[k] - cx[k]):
            result.append('CZ', [stim.target_rec(k), q])

    return result