flamingpy.noise.CVMacroLayer

class flamingpy.noise.CVMacroLayer(code, *, delta, bs_network=None, **kwargs)[source]

Bases: flamingpy.noise.cv.CVLayer

A class for reducing a macronode CV graph to a canonical graph.

Applies noise to self.egraph (assumed a macronode graph), entangles the macronodes, measures the syndrome, and populates the canonical graph reduced_graph with the reduced states, bit values, and error probabilities.

In addition to CVLayer args, the following:

Parameters

bs_network (np.array, optional) – the sympletic matrix corresponding to the beamsplitter network entangling the macronode. By default, the standard four-splitter.

gkp_inds

the indices of the GKP states.

p_inds

the indices of the p-squeezed states.

gkp_inds

the indices of the GKP states.

Type

array

p_inds

the indices of the p-squeezed states.

Type

array

SCZ([sparse])

Return the symplectic matrix associated with CZ application.

apply_noise([rng])

Reduce the macronode code lattice to the canonical code lattice.

bit_values([inds])

array: bit values associated with the p measurement.

draw(**kwargs)

Draw the CV graph state with matplotlib.

draw_SCZ(**kwargs)

Draw the adjacency matrix of a CV graph state with matplotlib.

hom_outcomes([inds, quad])

array: quad-homodyne measurement outcomes for modes inds.

inner_decoder()

Convert homodyne outcomes to bit values according to translator.

measure_hom([quad, inds, updated_means, ...])

Conduct a homodyne measurement of states in the lattice.

measure_syndrome([rng])

Measure the syndrome for memory-mode error correction of the grpah states.

populate_states([rng])

Populate the graph state with state labels.

SCZ(sparse=True)

Return the symplectic matrix associated with CZ application.

apply_noise(rng=Generator(PCG64) at 0x7F02B7250040)[source]

Reduce the macronode code lattice to the canonical code lattice.

Follow the procedure in arXiv:2104.03241. Take the macronode lattice macro_graph, apply noise, designate micronodes as planets and stars, conduct homodyne measurements, process these measurements, and compute conditional phase error probabilities.

This method modifies the node attributes of self.reduced_graph to include effective bit values and phase error probabilities.

bit_values(inds=None)

array: bit values associated with the p measurement.

draw(**kwargs)

Draw the CV graph state with matplotlib.

Use the default colours: gold for GKP states and blue for p-squeezed states.

See flamingpy.utils.viz.draw_EGraph for more details.

draw_SCZ(**kwargs)

Draw the adjacency matrix of a CV graph state with matplotlib.

See flamingpy.utils.viz.plot_mat_heat_map for more details.

hom_outcomes(inds=None, quad='p')

array: quad-homodyne measurement outcomes for modes inds.

inner_decoder()

Convert homodyne outcomes to bit values according to translator.

This is the inner (CV) decoder, a.k.a. translator, a.k.a binning function. Set converted values to the bit_val attribute for nodes in self.egraph.

This method modifies self.egraph.

measure_hom(quad='p', inds=None, updated_means=None, updated_covs=None, propagate=True, rng=Generator(PCG64) at 0x7F02B72504A0)

Conduct a homodyne measurement of states in the lattice.

Simulate a homodyne measurement of quadrature quad of states at indices inds according to sampling order specified by self._sampling_order. If updated_means or updated_covs is supplied, use those instead of the outputs of self._means_sampler and self._covs_sampler, respectively. The ‘propagate’ option is fed into _means_sampler, if desired.

Parameters

rng (numpy.random.Generator, optional) – a random number generator following NumPy API. It can be seeded for reproducibility. By default, numpy.random.default_rng is used without a fixed seed.

measure_syndrome(rng=Generator(PCG64) at 0x7F02B7250660)

Measure the syndrome for memory-mode error correction of the grpah states.

populate_states(rng=Generator(PCG64) at 0x7F02B7250900)

Populate the graph state with state labels.

Assume the graph states consists of a combination of squeezed states and GKP+ states. A non-zero self.p_swap overrides indices specified in self.states and uses a binomial distribution to identify some indices as p-squeezed states.

This method modifies self.egraph.

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