initial commit

Manifest.toml

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Project.toml

@@ -0,0 +1,11 @@
+name = "PyQDecoders"
+uuid = "17f5de1a-9b79-4409-a58d-4d45812840f7"
+authors = ["Stefan Krastanov <[email protected]>"]
+version = "0.1.0"
+
+[deps]
+Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
+PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
+
+[compat]
+PyCall = "1.96"

README.md

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+A Julia meta-package for accessing the python libraries `pymatching` (for MWPM-like decoders) and `ldpc` (for BP-like decoders).
+
+# How to install
+
+If you plan to use a python environment you are managing yourself, just make sure that julia is started from that environment and run
+
+`] add https://github.com/QuantumSavory/PyQDecoders.jl#master`
+
+You might get error messages telling you that you need to install some python packages in that environment of yours.
+
+If you want julia to automatically take care of creating a hidden python environment that it manages itself, do `ENV["PYTHON"] = ""` before you install `PyQDecoders.jl`
+
+# How to use
+
+## `pymatching`
+
+The python pymatching module is immediately available:
+
+```
+julia> using PyQDecoders
+
+julia> PyQDecoders.pm
+PyObject <module 'pymatching' from ...>
+```
+
+Running the example from `pymatching`'s original Readme:
+
+```
+julia> using PyQDecoders: sps, np, pm
+
+julia> H = sps.csc_matrix(
+           [1 1 0 0 0;
+            0 1 1 0 0;
+            0 0 1 1 0;
+            0 0 0 1 1])
+PyObject <4x5 sparse matrix of type '<class 'numpy.int64'>'
+        with 8 stored elements in Compressed Sparse Column format>
+
+julia> weights = [4, 3, 2, 3, 4]
+5-element Vector{Int64}:
+ 4
+ 3
+ 2
+ 3
+ 4
+
+julia> matching = pm.Matching(H, weights=weights)
+PyObject <pymatching.Matching object with 4 detectors, 1 boundary node, and 5 edges>
+
+julia> prediction = matching.decode([0, 1, 0, 1])
+5-element Vector{UInt8}:
+ 0x00
+ 0x00
+ 0x01
+ 0x01
+ 0x00
+
+julia> prediction, solution_weight = matching.decode([0, 1, 0, 1], return_weight=true);
+
+julia> prediction
+5-element Vector{UInt8}:
+ 0x00
+ 0x00
+ 0x01
+ 0x01
+ 0x00
+
+julia> solution_weight
+5.0
+```
+
+## ldpc
+
+The python ldpc module is immediately available:
+
+```
+julia> using PyQDecoders
+
+julia> PyQDecoders.ldpc
+PyObject <module 'ldpc' from ...>
+```
+
+Running the example from `ldpc`'s original Readme:
+
+
+```
+julia> using PyQDecoders: sps, np, ldpc
+
+julia> H = ldpc.codes.rep_code(3) # parity check matrix for the length-3 repetition code
+2×3 Matrix{Int64}:
+ 1  1  0
+ 0  1  1
+
+julia> n = size(H, 2) # the codeword length
+3
+
+julia> bpd = ldpc.bp_decoder(
+           H, # the parity check matrix
+           error_rate=0.1, # the error rate on each bit
+           max_iter=n, # the maximum iteration depth for BP
+           bp_method="product_sum", # BP method. The other option is `minimum_sum'
+           channel_probs=[nothing] # channel probability probabilities. Will overide error rate.
+       )
+PyObject <ldpc.bp_decoder.bp_decoder object at 0x7fea8283ab80>
+
+julia> error = [0,1,0]
+3-element Vector{Int64}:
+ 0
+ 1
+ 0
+
+julia> syndrome = H*error .% 2
+2-element Vector{Int64}:
+ 1
+ 1
+
+julia> decoding = bpd.decode(syndrome)
+3-element Vector{Int64}:
+ 0
+ 1
+ 0
+```

src/PyQDecoders.jl

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+module PyQDecoders
+import Conda
+using PyCall
+
+const sps = PyNULL()
+const np = PyNULL()
+const pm = PyNULL()
+const ldpc = PyNULL()
+
+function __init__()
+    copy!(sps, pyimport_conda("scipy.sparse", "scipy"))
+    copy!(np, pyimport_conda("numpy", "numpy"))
+    if PyCall.conda
+        Conda.pip_interop(true)
+        Conda.pip("install", "pymatching")
+        Conda.pip("install", "ldpc")
+    end
+    copy!(pm, pyimport_conda("pymatching", "pymatching"))
+    copy!(ldpc, pyimport_conda("ldpc", "ldpc"))
+    pyimport("ldpc.codes")
+end
+
+end # module