some performance optimizations in decoders

ext/QuantumCliffordLDPCDecodersExt/QuantumCliffordLDPCDecodersExt.jl

@@ -74,16 +74,16 @@ parity_checks(d::BeliefPropDecoder) = d.H
 parity_checks(d::BitFlipDecoder) = d.H
 
 function decode(d::BeliefPropDecoder, syndrome_sample)
-    row_x = syndrome_sample[1:d.cx]
-    row_z = syndrome_sample[d.cx+1:d.cx+d.cz]
+    row_x = @view syndrome_sample[1:d.cx]
+    row_z = @view syndrome_sample[d.cx+1:d.cx+d.cz]
     guess_z, success = LDPCDecoders.decode!(d.bpdecoderx, row_x)
     guess_x, success = LDPCDecoders.decode!(d.bpdecoderz, row_z)
     return vcat(guess_x, guess_z)
 end
 
 function decode(d::BitFlipDecoder, syndrome_sample)
-    row_x = syndrome_sample[1:d.cx]
-    row_z = syndrome_sample[d.cx+1:d.cx+d.cz]
+    row_x = @view syndrome_sample[1:d.cx]
+    row_z = @view syndrome_sample[d.cx+1:d.cx+d.cz]
     guess_z, success = LDPCDecoders.decode!(d.bfdecoderx, row_x)
     guess_x, success = LDPCDecoders.decode!(d.bfdecoderz, row_z)
     return vcat(guess_x, guess_z)

ext/QuantumCliffordPyQDecodersExt/QuantumCliffordPyQDecodersExt.jl

@@ -69,8 +69,8 @@ end
 parity_checks(d::PyBP) = d.H
 
 function decode(d::PyBP, syndrome_sample)
-    row_x = syndrome_sample[1:d.nx] # TODO These copies and indirections might be costly!
-    row_z = syndrome_sample[d.nx+1:end]
+    row_x = @view syndrome_sample[1:d.nx]
+    row_z = @view syndrome_sample[d.nx+1:end]
     guess_z_errors = PythonCall.PyArray(d.pyx.decode(np.array(row_x)))
     guess_x_errors = PythonCall.PyArray(d.pyz.decode(np.array(row_z)))
     vcat(guess_x_errors, guess_z_errors)
@@ -106,18 +106,19 @@ end
 parity_checks(d::PyMatchingDecoder) = d.H
 
 function decode(d::PyMatchingDecoder, syndrome_sample)
-    row_x = syndrome_sample[1:d.nx] # TODO This copy is costly!
-    row_z = syndrome_sample[d.nx+1:end]
+    row_x = @view syndrome_sample[1:d.nx]
+    row_z = @view syndrome_sample[d.nx+1:end]
     guess_z_errors = PythonCall.PyArray(d.pyx.decode(row_x))
     guess_x_errors = PythonCall.PyArray(d.pyz.decode(row_z))
     vcat(guess_x_errors, guess_z_errors)
 end
 
 function batchdecode(d::PyMatchingDecoder, syndrome_samples)
-    row_x = syndrome_samples[:,1:d.nx] # TODO This copy is costly!
-    row_z = syndrome_samples[:,d.nx+1:end]
+    row_x = @view syndrome_samples[:,1:d.nx]
+    row_z = @view syndrome_samples[:,d.nx+1:end]
     guess_z_errors = PythonCall.PyArray(d.pyx.decode_batch(row_x))
     guess_x_errors = PythonCall.PyArray(d.pyz.decode_batch(row_z))
+    n_cols_x = size(guess_x_errors, 2)
     hcat(guess_x_errors, guess_z_errors)
 end
 

src/ecc/decoder_pipeline.jl

@@ -171,12 +171,21 @@ end
 
 function evaluate_guesses(measured_faults, guesses, faults_matrix)
     nsamples = size(guesses, 1)
-    guess_faults = (faults_matrix * guesses') .% 2 # TODO this can be faster and non-allocating by turning it into a loop
-    decoded = 0
-    for i in 1:nsamples # TODO this can be faster and non-allocating by having the loop and the matrix multiplication on the line above work together and not store anything
-        (@view guess_faults[:,i]) == (@view measured_faults[i,:]) && (decoded += 1)
+    fails = 0
+    for i in 1:nsamples
+        for j in 1:size(faults_matrix, 1)
+            sum_mod = 0
+            @inbounds @simd for k in 1:size(faults_matrix, 2)
+                sum_mod += faults_matrix[j, k] * guesses[i, k]
+            end
+            sum_mod %= 2
+            if sum_mod != measured_faults[i, j]
+                fails += 1
+                break
+            end
+        end
     end
-    return (nsamples - decoded) / nsamples
+    return fails / nsamples
 end
 
 function evaluate_decoder(d::AbstractSyndromeDecoder, setup::CommutationCheckECCSetup, nsamples::Int)