Added noisy analytical expectation. (tensorflow#508)

* Added noisy analytical expectation.

* naming fix and import test.

* rename and import fix.

* typo fixes.

* added empty tests.

* Jae feedback.

* Implement better threading mechanism.

WORKSPACE

@@ -67,9 +67,9 @@ http_archive(
 
 http_archive(
     name = "qsim",
-    sha256 = "06c330960edf95d495c3686be9006fa11a5f87c8294d6ef4a2ad0a01660f2e49",
-    strip_prefix = "qsim-0.9.1",
-    urls = ["https://github.com/quantumlib/qsim/archive/v0.9.1.zip"],
+    sha256 = "d39b9c48866ce4d6a095093ae8059444d649e851219497af99e937a74f1e9a45",
+    strip_prefix = "qsim-0.9.2-dev-20210317",
+    urls = ["https://github.com/quantumlib/qsim/archive/v0.9.2-dev+20210317.zip"],
 )
 
 # Added for crosstool in tensorflow.

release/BUILD

@@ -13,6 +13,7 @@ sh_binary(
         "//tensorflow_quantum/core:__init__.py",
         "//tensorflow_quantum/core/ops:__init__.py",
         "//tensorflow_quantum/core/ops/math_ops:__init__.py",
+        "//tensorflow_quantum/core/ops/noise:__init__.py",
         "//tensorflow_quantum/core/proto:__init__.py",
         "//tensorflow_quantum/core/serialize:__init__.py",
 
@@ -39,6 +40,7 @@ sh_binary(
         "//tensorflow_quantum/core/ops:tfq_utility_ops_py",
         "//tensorflow_quantum/core/ops:tfq_simulate_ops_py",
         "//tensorflow_quantum/core/ops/math_ops:inner_product_op_py",
+        "//tensorflow_quantum/core/ops/noise:noisy_expectation_op_py",
         "//tensorflow_quantum/core/serialize:serializer",
         "//tensorflow_quantum/datasets:cluster_state",
         "//tensorflow_quantum/datasets:spin_system",

scripts/import_test.py

@@ -37,6 +37,9 @@ def test_imports():
     # Math ops.
     _ = tfq.math.inner_product
 
+    # Noisy simulation ops.
+    _ = tfq.noise.expectation
+
     # Util functions.
     _ = tfq.convert_to_tensor
     _ = tfq.get_quantum_concurrent_op_mode

tensorflow_quantum/__init__.py

@@ -24,6 +24,9 @@
 # Import math ops.
 from tensorflow_quantum.core import math_ops as math
 
+# Import noise ops.
+from tensorflow_quantum.core import noise
+
 # Re-label python module as layers module.
 import tensorflow_quantum.python.layers as layers
 

tensorflow_quantum/core/__init__.py

@@ -23,3 +23,6 @@
                                          padded_to_ragged2d, resolve_parameters)
 # Import math ops.
 from tensorflow_quantum.core.ops import math_ops
+
+# Import noise ops.
+from tensorflow_quantum.core.ops import noise

tensorflow_quantum/core/ops/__init__.py

@@ -27,3 +27,6 @@
 
 # Import math_ops.
 from tensorflow_quantum.core.ops import math_ops
+
+# Import noise ops.
+from tensorflow_quantum.core.ops import noise

tensorflow_quantum/core/ops/noise/BUILD

@@ -0,0 +1,85 @@
+package(default_visibility = ["//visibility:public"])
+
+licenses(["notice"])
+
+# Export for the PIP package.
+exports_files(["__init__.py"])
+
+config_setting(
+    name = "windows",
+    constraint_values = ["@bazel_tools//platforms:windows"],
+)
+
+cc_binary(
+    name = "_tfq_noise_ops.so",
+    srcs = [
+        "tfq_noisy_expectation.cc",
+    ],
+    copts = select({
+        ":windows": [
+            "/D__CLANG_SUPPORT_DYN_ANNOTATION__",
+            "/D_USE_MATH_DEFINES",
+            "/DEIGEN_MPL2_ONLY",
+            "/DEIGEN_MAX_ALIGN_BYTES=64",
+            "/DEIGEN_HAS_TYPE_TRAITS=0",
+            "/DTF_USE_SNAPPY",
+            "/showIncludes",
+            "/MD",
+            "/O2",
+            "/DNDEBUG",
+            "/w",
+            "-DWIN32_LEAN_AND_MEAN",
+            "-DNOGDI",
+            "/d2ReducedOptimizeHugeFunctions",
+            "/arch:AVX",
+            "/std:c++14",
+            "-DTENSORFLOW_MONOLITHIC_BUILD",
+            "/DPLATFORM_WINDOWS",
+            "/DEIGEN_HAS_C99_MATH",
+            "/DTENSORFLOW_USE_EIGEN_THREADPOOL",
+            "/DEIGEN_AVOID_STL_ARRAY",
+            "/Iexternal/gemmlowp",
+            "/wd4018",
+            "/wd4577",
+            "/DNOGDI",
+            "/UTF_COMPILE_LIBRARY",
+        ],
+        "//conditions:default": [
+            "-pthread",
+            "-std=c++11",
+            "-D_GLIBCXX_USE_CXX11_ABI=0",
+        ],
+    }),
+    features = select({
+        ":windows": ["windows_export_all_symbols"],
+        "//conditions:default": [],
+    }),
+    linkshared = 1,
+    deps = [
+        "//tensorflow_quantum/core/ops:parse_context",
+        "//tensorflow_quantum/core/ops:tfq_simulate_utils",
+        "//tensorflow_quantum/core/src:util_qsim",
+        "//tensorflow_quantum/core/src:circuit_parser_qsim",
+        "@qsim//lib:qsim_lib",
+    ],
+)
+
+py_library(
+    name = "noisy_expectation_op_py",
+    srcs = ["noisy_expectation_op.py"],
+    data = [":_tfq_noise_ops.so"],
+    deps = [
+        "//tensorflow_quantum/core/ops:load_module",
+    ],
+)
+
+py_test(
+    name = "noisy_expectation_op_test",
+    srcs = ["noisy_expectation_op_test.py"],
+    python_version = "PY3",
+    deps = [
+        ":noisy_expectation_op_py",
+        "//tensorflow_quantum/core/ops:batch_util",
+        "//tensorflow_quantum/python:util",
+    ],
+)

tensorflow_quantum/core/ops/noise/__init__.py

@@ -0,0 +1,17 @@
+# Copyright 2020 The TensorFlow Quantum Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Module for tfq.core.ops.noise.*"""
+
+from tensorflow_quantum.core.ops.noise.noisy_expectation_op import expectation

tensorflow_quantum/core/ops/noise/noisy_expectation_op.py

@@ -0,0 +1,65 @@
+# Copyright 2020 The TensorFlow Quantum Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Module for high performance noisy circuit simulation ops."""
+import os
+import tensorflow as tf
+from tensorflow_quantum.core.ops.load_module import load_module
+
+NOISY_OP_MODULE = load_module(os.path.join("noise", "_tfq_noise_ops.so"))
+
+
+def expectation(programs, symbol_names, symbol_values, pauli_sums, num_samples):
+    """Calculate the analytic expectation values using monte-carlo trajectories.
+
+    Simulate the final state of `programs` given `symbol_values` are placed
+    inside of the symbols with the name in `symbol_names` in each circuit.
+    Channels in this simulation will be "tossed" to a certain realization
+    during simulation. This simulation is repeated `num_samples` times and
+    analytic expectation calculations with the given `pauli_sums` are calculated
+    after each run. Once all the runs are finished, these quantities are
+    averaged together. This process can be thought of as analyical expectation
+    calculation done using monte carlo state vector simulation to account
+    for noisy operations in the given circuits.
+
+    Args:
+        programs: `tf.Tensor` of strings with shape [batch_size] containing
+            the string representations of the circuits to be executed.
+        symbol_names: `tf.Tensor` of strings with shape [n_params], which
+            is used to specify the order in which the values in
+            `symbol_values` should be placed inside of the circuits in
+            `programs`.
+        symbol_values: `tf.Tensor` of real numbers with shape
+            [batch_size, n_params] specifying parameter values to resolve
+            into the circuits specificed by programs, following the ordering
+            dictated by `symbol_names`.
+        pauli_sums: `tf.Tensor` of strings with shape [batch_size, n_ops]
+            containing the string representation of the operators that will
+            be used on all of the circuits in the expectation calculations.
+        num_samples: `tf.Tensor` with `num_samples[i][j]` is equal to the
+            number of times `programs[i]` will be simulated to estimate
+            `pauli_sums[i][j]`. Therefore, `num_samples` must have the same
+            shape as `pauli_sums`. Note: internally this quantity can get
+            rounded up to the nearest multiple of the number of available
+            threads to TensorFlow. For best performance ensure that the
+            quantities in `num_samples` are a multiple of the number of
+            available threads.
+    Returns:
+        `tf.Tensor` with shape [batch_size, n_ops] that holds the
+            expectation value for each circuit with each op applied to it
+            (after resolving the corresponding parameters in).
+    """
+    return NOISY_OP_MODULE.tfq_noisy_expectation(
+        programs, symbol_names, tf.cast(symbol_values, tf.float32), pauli_sums,
+        tf.cast(num_samples, dtype=tf.int32))