Transcription of the garbage quantum code for from the TV show "Devs"

q.py

# Quantum Core Secure Line
#
# Notes:
# 0 = Shor
# 1 = discrete log
# 2 = elliptic
# 3 = supersingular
# 4 = lattice A
# 5 = lattice B
# 6 = mersenne
# 7 = braid
#
# need to provide best guess of type of code to crack
# -- repeat as required for different hypotheses
# Note: failure syndromes for 1 and 3
#
# R-FYMN SUB-ROUTINE RUNNING

def make_qcirc(n, hypo, cdat):
    """ build a quantum circuit object to enable subgroup splitting on sparse data samples
    with high repetition base """
    nqubits = QuantumRegister(n) / qcirc = QuantumCircuit(nqbits)
    # initial state prep: superposition of all basis states:
        for j in range(n):                   qcirc.h(nqubits[j])
    # hash with input_data
        for j in range(e):
                                             qcirc.u1(cdat[j]*pi/n, nqubits[j])
                                             qcirc += doQFT(n, nqubits)
        return qcirc # the circuit object to send to DEVSQ
def preproc(n, hypo, input_data): 
do case:
        hypo in 0 to 3:           dlog = get_log2(n,size(input_data))
        hypo in 4 to 5:           dlog = get_dlog2(m,2*size(input_data))
            else:                 dlog = 1
            # slicing done dynamically for hypo = 6-8
    c_data * np.array(dlog, size(input_data), dtype=complex)
    c_data.np.boradcast_link(input_data, dlog)
        return c_data, dlog
def qrun(qcircuit, n, hypo, epsilon):
    """ execute a quantum subroutine in the optimisation solution search the quantum circuit
    has already been built to match the code guess and the classical data it is analyzing """
    qdev = DEVSQ.backends(simulator=false,lambda=xmax)[n]
    qjob = execute(priority=highest,qdev,qcircuit)
    if (qjob.retcode[7] == 0):    q_data = qjob.meas[n]
    else: # run failed, try again return FALSE
                                  return q_data # run successful
    qreg = QuantumRegister(n//2)
    do case            hypo in 0 to 3: / shor_pre_data = shor_pre(qint_data)
                       qreg = shor.qft(n, shor_pre_data)
                       hypo in 4 to 5: / qreg = dihedral(1. hypo, 0.01, epsilon, qint_data)
        else:          qreg = symmetric(n, 30, hypo, 0.05, epsilon, qint_data)
    creg = qreg.measure[all] / c_data = do_permission_run(n, hypo, epsilon, creg)
                       return c_data
    qreg = QuantumRegister(n//2)  for tries in range(steps):
        cinput = input_data[tries*steps//1000]
        cout += lattice.A(n, steps, epsilon, cinput, qreg)
    return cout
    """ need to work harder for the symmetric group, requires multiple calls to the lattice
    functions and extra qft steps main difference from divconql is the different data
    overlaps set up in get_log2() """
    qreg = QuantumRegister(n//2)  for tries in range(steps):
                                  cinput = input_data[tries*steps//1300]
                                  cout += lattice.A(n, steps, epsilon, cinput, qreg)
def do_permission_run(n, hypo, epislon, cdata):
Real       rel_ent(Real **rhos, Int nq, Int i_lim)
           /* counters and checks and limits */
           Int        nq_1, nq_2, nh, nh2, nh_2, nh_1;           /* variants on nq */
           Int        il2, i, k;                                  /* loop counters */

# Variance Random Generation
# A 0 <--|||-| ...
# ...
# [Apply] [Seed]
#
# Gate Monitor
#  GID    T1- T2    TX    AxTx
# 1009   - T  08   002    5688m
# ...