Update trajectory simulation docs

docs/sphinx/snippets/cpp/using/backends/trajectory_batching.cpp

@@ -0,0 +1,44 @@
+/*******************************************************************************
+ * Copyright (c) 2022 - 2025 NVIDIA Corporation & Affiliates.                  *
+ * All rights reserved.                                                        *
+ *                                                                             *
+ * This source code and the accompanying materials are made available under    *
+ * the terms of the Apache License 2.0 which accompanies this distribution.    *
+ ******************************************************************************/
+
+// [Begin Documentation]
+#include <chrono>
+#include <cudaq.h>
+#include <iostream>
+
+struct xOp {
+  void operator()(int qubit_count) __qpu__ {
+    cudaq::qvector q(qubit_count);
+    x(q);
+    mz(q);
+  }
+};
+
+int main() {
+  // Add a simple bit-flip noise channel to X gate
+  const double error_probability = 0.01;
+
+  cudaq::bit_flip_channel bit_flip(error_probability);
+  // Add noise channels to our noise model.
+  cudaq::noise_model noise_model;
+  // Apply the bitflip channel to any X-gate on any qubits
+  noise_model.add_all_qubit_channel<cudaq::types::x>(bit_flip);
+
+  const int qubit_count = 10;
+  const auto start_time = std::chrono::high_resolution_clock::now();
+  // Due to the impact of noise, our measurements will no longer be uniformly in
+  // the |1...1> state.
+  auto counts =
+      cudaq::sample({.shots = 1000, .noise = noise_model}, xOp{}, qubit_count);
+  const auto end_time = std::chrono::high_resolution_clock::now();
+  counts.dump();
+  const std::chrono::duration<double, std::milli> elapsed_time =
+      end_time - start_time;
+  std::cout << "Simulation elapsed time: " << elapsed_time.count() << "ms\n";
+  return 0;
+}

docs/sphinx/snippets/python/using/backends/trajectory.py

@@ -10,6 +10,9 @@
 import cudaq
 
 # Use the `nvidia` target
+# Other targets capable of trajectory simulation are:
+# - `tensornet`
+# - `tensornet-mps`
 cudaq.set_target("nvidia")
 
 # Let's define a simple kernel that we will add noise to.

docs/sphinx/snippets/python/using/backends/trajectory_batching.py

@@ -0,0 +1,46 @@
+# ============================================================================ #
+# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
+# All rights reserved.                                                         #
+#                                                                              #
+# This source code and the accompanying materials are made available under     #
+# the terms of the Apache License 2.0 which accompanies this distribution.     #
+# ============================================================================ #
+
+#[Begin Docs]
+import time
+import cudaq
+# Use the `nvidia` target
+cudaq.set_target("nvidia")
+
+# Let's define a simple kernel that we will add noise to.
+qubit_count = 10
+
+
+@cudaq.kernel
+def kernel(qubit_count: int):
+    qvector = cudaq.qvector(qubit_count)
+    x(qvector)
+    mz(qvector)
+
+
+# Add a simple bit-flip noise channel to X gate
+error_probability = 0.01
+bit_flip = cudaq.BitFlipChannel(error_probability)
+
+# Add noise channels to our noise model.
+noise_model = cudaq.NoiseModel()
+# Apply the bit-flip channel to any X-gate on any qubits
+noise_model.add_all_qubit_channel("x", bit_flip)
+
+ideal_counts = cudaq.sample(kernel, qubit_count, shots_count=1000)
+
+start = time.time()
+# Due to the impact of noise, our measurements will no longer be uniformly
+# in the |1...1> state.
+noisy_counts = cudaq.sample(kernel,
+                            qubit_count,
+                            noise_model=noise_model,
+                            shots_count=1000)
+end = time.time()
+noisy_counts.dump()
+print(f"Simulation elapsed time: {(end - start) * 1000} ms")

docs/sphinx/snippets/python/using/backends/trajectory_observe.py

@@ -11,6 +11,9 @@
 from cudaq import spin
 
 # Use the `nvidia` target
+# Other targets capable of trajectory simulation are:
+# - `tensornet`
+# - `tensornet-mps`
 cudaq.set_target("nvidia")
 
 

docs/sphinx/using/backends/sims/noisy.rst

@@ -4,15 +4,12 @@ Noisy Simulators
 Trajectory Noisy Simulation
 ++++++++++++++++++++++++++++++++++
 
-The :code:`nvidia` target supports noisy quantum circuit simulations using 
-quantum trajectory method across all configurations: single GPU, multi-node 
-multi-GPU, and with host memory. When simulating many trajectories with small 
-state vectors, the simulation is batched for optimal performance.
+CUDA-Q GPU simulator backends, :code:`nvidia`, :code:`tensornet`, and :code:`tensornet-mps`,
+supports noisy quantum circuit simulations using quantum trajectory method.
 
-When a :code:`noise_model` is provided to CUDA-Q, the :code:`nvidia` target 
+When a :code:`noise_model` is provided to CUDA-Q, the backend target 
 will incorporate quantum noise into the quantum circuit simulation according 
-to the noise model specified.
-
+to the noise model specified, as shown in the below example.
 
 .. tab:: Python
 
@@ -33,14 +30,70 @@ to the noise model specified.
 
     .. code:: bash 
 
+        # nvidia target
         nvq++ --target nvidia program.cpp [...] -o program.x
         ./program.x
         { 00:15 01:92 10:81 11:812 }
+        # tensornet target
+        nvq++ --target tensornet program.cpp [...] -o program.x
+        ./program.x
+        { 00:10 01:108 10:73 11:809 }
+        # tensornet-mps target
+        nvq++ --target tensornet-mps program.cpp [...] -o program.x
+        ./program.x
+        { 00:5 01:86 10:102 11:807 }
+
+
+In the case of bit-string measurement sampling as in the above example, each measurement 'shot' is executed as a trajectory, 
+whereby Kraus operators specified in the noise model are sampled.
+
+
+Unitary Mixture vs. General Noise Channel
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
+Quantum noise channels can be classified into two categories:
 
-In the case of bit-string measurement sampling as in the above example, each measurement 'shot' is executed as a trajectory, whereby Kraus operators specified in the noise model are sampled.
+(1) Unitary mixture
 
-For observable expectation value estimation, the statistical error scales asymptotically as :math:`1/\sqrt{N_{trajectories}}`, where :math:`N_{trajectories}` is the number of trajectories.
+The noise channel can be defined by a set of unitary matrices along with list of probabilities associated with those matrices.
+The depolarizing channel is an example of unitary mixture, whereby `I` (no noise), `X`, `Y`, or `Z` unitaries may occur to the
+quantum state at pre-defined probabilities.
+
+(2) General noise channel
+
+The channel is defined as a set of non-unitary Kraus matrices, satisfying the completely positive and trace preserving (CPTP) condition.
+An example of this type of channels is the amplitude damping noise channel.
+
+In trajectory simulation method, simulating unitary mixture noise channels is more efficient than
+general noise channels since the trajectory sampling of the latter requires probability calculation based
+on the immediate quantum state. 
+
+.. note::
+    CUDA-Q noise channel utility automatically detects whether a list of Kraus matrices can be converted to
+    the unitary mixture representation for more efficient simulation.
+
+.. list-table:: **Noise Channel Support**
+  :widths: 20 30 50
+
+  * - Backend
+    - Unitary Mixture
+    - General Channel
+  * - :code:`nvidia`
+    - YES
+    - YES
+  * - :code:`tensornet`
+    - YES
+    - NO
+  * - :code:`tensornet-mps`
+    - YES
+    - YES (number of qubits > 1)
+
+
+Trajectory Expectation Value Calculation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In trajectory simulation method, the statistical error of observable expectation value estimation scales asymptotically 
+as :math:`1/\sqrt{N_{trajectories}}`, where :math:`N_{trajectories}` is the number of trajectories.
 Hence, depending on the required level of accuracy, the number of trajectories can be specified accordingly.
 
 .. tab:: Python
@@ -63,24 +116,54 @@ Hence, depending on the required level of accuracy, the number of trajectories c
 
     .. code:: bash 
 
+        # nvidia target
         nvq++ --target nvidia program.cpp [...] -o program.x
         ./program.x
         Noisy <Z> with 1024 trajectories = -0.810547
         Noisy <Z> with 8192 trajectories = -0.800049
 
+        # tensornet target
+        nvq++ --target tensornet program.cpp [...] -o program.x
+        ./program.x
+        Noisy <Z> with 1024 trajectories = -0.777344
+        Noisy <Z> with 8192 trajectories = -0.800537
+
+        # tensornet-mps target
+        nvq++ --target tensornet-mps program.cpp [...] -o program.x
+        ./program.x
+        Noisy <Z> with 1024 trajectories = -0.828125
+        Noisy <Z> with 8192 trajectories = -0.801758
+
+In the above example, as we increase the number of trajectories, 
+the result of CUDA-Q `observe` approaches the true value.
+
+.. note::
+    With trajectory noisy simulation, the result of CUDA-Q `observe` is inherently stochastic.  
+    For a small number of qubits, the true expectation value can be simulated by the :ref:`density matrix <density-matrix-cpu-backend>` simulator. 
+
+Batched Trajectory Simulation 
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+On the :code:`nvidia` target, when simulating many trajectories with small 
+state vectors, the simulation is batched for optimal performance.
 
-The following environment variable options are applicable to the :code:`nvidia` target for trajectory noisy simulation. Any environment variables must be set
-prior to setting the target.
+.. note::
+
+    Batched trajectory simulation is only available on the single-GPU execution mode of the :code:`nvidia` target. 
+
+    If batched trajectory simulation is not activated, e.g., due to problem size, number of trajectories, 
+    or the nature of the circuit (dynamic circuits with mid-circuit measurements and conditional branching), 
+    the required number of trajectories will be executed sequentially.  
+
+The following environment variable options are applicable to the :code:`nvidia` target for batched trajectory noisy simulation. 
+Any environment variables must be set prior to setting the target or running "`import cudaq`".
 
 .. list-table:: **Additional environment variable options for trajectory simulation**
   :widths: 20 30 50
 
   * - Option
     - Value
     - Description
-  * - ``CUDAQ_OBSERVE_NUM_TRAJECTORIES``
-    - positive integer
-    - The default number of trajectories for observe simulation if none was provided in the `observe` call. The default value is 1000.
   * - ``CUDAQ_BATCH_SIZE``
     - positive integer or `NONE`
     - The number of state vectors in the batched mode. If `NONE`, the batch size will be calculated based on the available device memory. Default is `NONE`.
@@ -95,11 +178,45 @@ prior to setting the target.
     - The minimum number of trajectories for batching. If the number of trajectories is less than this value, batched trajectory simulation will be disabled. Default value is 4.
 
 .. note::
-
-    Batched trajectory simulation is only available on the single-GPU execution mode of the :code:`nvidia` target. 
-
-    If batched trajectory simulation is not activated, e.g., due to problem size, number of trajectories, or the nature of the circuit (dynamic circuits with mid-circuit measurements and conditional branching), the required number of trajectories will be executed sequentially.  
+    The default batched trajectory simulation parameters have been chosen for optimal performance.
+
+In the below example, we demonstrate the use of these parameters to control trajectory batching.
+
+.. tab:: Python
+
+    .. literalinclude:: ../../../snippets/python/using/backends/trajectory_batching.py
+        :language: python
+        :start-after: [Begin Docs]
+
+    .. code:: bash 
+
+        # Default batching parameter
+        python3 program.py
+        Simulation elapsed time: 45.75657844543457 ms
+
+        # Disable batching by setting batch size to 1
+        CUDAQ_BATCH_SIZE=1 python3 program.py
+        Simulation elapsed time: 716.090202331543 ms
+
+.. tab:: C++
+
+    .. literalinclude:: ../../../snippets/cpp/using/backends/trajectory_batching.cpp
+        :language: cpp
+        :start-after: [Begin Documentation]
+
+    .. code:: bash 
+
+        nvq++ --target nvidia program.cpp [...] -o program.x
+        # Default batching parameter
+        ./program.x
+        Simulation elapsed time: 45.47ms
+        # Disable batching by setting batch size to 1
+        Simulation elapsed time: 558.66ms
+
+.. note::
 
+    The :code:`CUDAQ_LOG_LEVEL` :doc:`environment variable <../../basics/troubleshooting>` can be used to 
+    view detailed logs of batched trajectory simulation, e.g., the batch size. 
 
 
 Density Matrix 

docs/sphinx/using/backends/sims/svsims.rst

@@ -96,8 +96,8 @@ To execute a program on the :code:`nvidia` backend, use the following commands:
 
 
 In the single-GPU mode, the :code:`nvidia` backend provides the following
-environment variable options. Any environment variables must be set prior to
-setting the target. It is worth drawing attention to gate fusion, a powerful tool for improving simulation performance which is discussed in greater detail `here <https://nvidia.github.io/cuda-quantum/latest/examples/python/performance_optimizations.html>`__.
+environment variable options. Any environment variables must be set prior to setting the target or running "`import cudaq`".
+It is worth drawing attention to gate fusion, a powerful tool for improving simulation performance which is discussed in greater detail `here <https://nvidia.github.io/cuda-quantum/latest/examples/python/performance_optimizations.html>`__.
 
 .. list-table:: **Environment variable options supported in single-GPU mode**
   :widths: 20 30 50
@@ -121,6 +121,7 @@ setting the target. It is worth drawing attention to gate fusion, a powerful too
     - positive integer, or `NONE`
     - GPU memory (in GB) allowed for on-device state-vector allocation. As the state-vector size exceeds this limit, host memory will be utilized for migration. `NONE` means unlimited (up to physical memory constraints). This is the default.
 
+
 .. deprecated:: 0.8
     The :code:`nvidia-fp64` targets, which is equivalent setting the `fp64` option on the :code:`nvidia` target, 
     is deprecated and will be removed in a future release.
@@ -212,8 +213,8 @@ See the `Divisive Clustering <https://nvidia.github.io/cuda-quantum/latest/appli
 
 In addition to those environment variable options supported in the single-GPU mode,
 the :code:`nvidia` backend provides the following environment variable options particularly for 
-the multi-node multi-GPU configuration. Any environment variables must be set
-prior to setting the target.
+the multi-node multi-GPU configuration. Any environment variables must be set prior to setting the target or running "`import cudaq`".
+
 
 .. list-table:: **Additional environment variable options for multi-node multi-GPU mode**
   :widths: 20 30 50
@@ -276,3 +277,8 @@ environment variable to another integer value as shown below.
         nvq++ --target nvidia --target-option mgpu,fp64 program.cpp [...] -o program.x
         CUDAQ_MGPU_FUSE=5 mpiexec -np 2 ./program.x
 
+
+.. note:: 
+
+  On multi-node systems without `MNNVL` support, the `nvidia` target in `mgpu` mode may fail to allocate memory. 
+  Users can disable `MNNVL` fabric-based memory sharing by setting the environment variable `UBACKEND_USE_FABRIC_HANDLE=0`.