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46 changes: 23 additions & 23 deletions docs/Documentation/Applications/quantum_computing.md
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Expand Up @@ -37,7 +37,7 @@ module help qiskit/aer-gpu
## Qiskit Aer GPU Simulation

`qiskit-aer-gpu` replaces the CPU `AerSimulator` with a GPU-accelerated backend.
See [`qae_example_backends.ipynb`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/qae_example_backends.ipynb)
See [`qae_example_backends.ipynb`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/qae_example_backends.ipynb)
for a worked example comparing CPU and GPU backends for a QAE circuit.

The simplest way to enable GPU simulation is:
Expand Down Expand Up @@ -77,8 +77,8 @@ run on CPUs, GPUs, or QPU hardware.

### Single GPU

See [`binary_optimizer.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/binary_optimizer.py)
and [`qae.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/qae.py)
See [`binary_optimizer.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/binary_optimizer.py)
and [`qae.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/qae.py)
for real-world examples using the single-GPU target.

```python
Expand All @@ -100,7 +100,7 @@ print(result) # { 00:~500 11:~500 }
### Noisy Simulation

CUDA-Q supports trajectory-based noisy simulation using a depolarizing noise model.
See [`binary_optimizer.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/binary_optimizer.py)
See [`binary_optimizer.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/binary_optimizer.py)
for a production example with per-gate noise channels applied to a QAE circuit.

```python
Expand All @@ -124,7 +124,7 @@ result = cudaq.sample(bell, shots_count=1000, noise_model=noise)
The `mqpu` option assigns shots to multiple GPUs in parallel. Each GPU holds its own
copy of the full statevector and executes an independent subset of shots — useful
for shot-noisy sweeps where the circuit fits in a single GPU's memory.
See [`par_benchmark_2gpu.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/par_benchmark_2gpu.py)
See [`par_benchmark_2gpu.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/par_benchmark_2gpu.py)
for a DQA benchmark using this strategy on Kestrel H100s.

```python
Expand Down Expand Up @@ -166,7 +166,7 @@ srun python3 my_mqpu_script.py

For noisy circuits that fit in one GPU (≤28 qubits), **`mpi4py` shot-splitting
typically outperforms `mqpu`** due to lower inter-process overhead.
See the [parallelization report](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/parallelisation_report.md)
See the [parallelization report](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/parallelisation_report.md)
for benchmarks on Kestrel H100s.

### Multi-GPU Statevector (mgpu)
Expand All @@ -183,11 +183,11 @@ GPUs using cuStateVec, enabling simulation of circuits that exceed single-GPU me
On Kestrel, multi-GPU requires GPU-aware MPI via the Cray GTL library. Use the
wrapper script below to set the required environment variables **before** Python
starts — they cannot be set with `os.environ` inside the script.
See [`bench_mgpu_dqa.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/bench_mgpu_dqa.py)
and [`par_benchmark_mgpu.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/par_benchmark_mgpu.py)
See [`bench_mgpu_dqa.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/bench_mgpu_dqa.py)
and [`par_benchmark_mgpu.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/par_benchmark_mgpu.py)
for full DQA benchmarking examples.

**[`run_mgpu.sh`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/run_mgpu.sh):**
**[`run_mgpu.sh`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/run_mgpu.sh):**
```bash
#!/bin/bash
export MPICH_GPU_SUPPORT_ENABLED=1
Expand All @@ -198,7 +198,7 @@ export CUDAQ_MGPU_COMM_PLUGIN_TYPE=MPICH
exec python3 "$@"
```

**[Script using mgpu](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/test_mgpu.py):**
**[Script using mgpu](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/test_mgpu.py):**
```python
import cudaq
cudaq.set_target('nvidia', option='mgpu,fp32')
Expand Down Expand Up @@ -287,7 +287,7 @@ srun bash run_mgpu.sh my_cudaq_mgpu_script.py

For noisy trajectory simulation with many shots, splitting shots across MPI ranks
(one rank per GPU) gives near-linear speedup. See
[`par_benchmark_mpi.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/par_benchmark_mpi.py)
[`par_benchmark_mpi.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/par_benchmark_mpi.py)
for a full working example.

```bash
Expand Down Expand Up @@ -334,7 +334,7 @@ See the [Jupyter documentation](../Development/Jupyter/index.md) for instruction
forwarding the port to your local browser.

A worked example notebook for QAE-based amplitude estimation is available at
[`qae_example.ipynb`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/qae_example.ipynb).
[`qae_example.ipynb`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/qae_example.ipynb).

---

Expand Down Expand Up @@ -383,14 +383,14 @@ python3 your_script.py

**NLR example code (Kestrel-tested):**

- [`run_mgpu.sh`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/run_mgpu.sh) — Cray GTL wrapper for mgpu
- [`test_mgpu.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/test_mgpu.py) — GHZ circuit on nvidia mgpu target
- [`bench_mgpu_dqa.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/bench_mgpu_dqa.py) — DQA benchmark: noiseless + noisy mgpu timing
- [`par_benchmark_mgpu.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/par_benchmark_mgpu.py) — scaling benchmark across multiple GPUs with mgpu
- [`par_benchmark_mpi.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/par_benchmark_mpi.py) — DQA benchmark: mpi4py shot-splitting across ranks
- [`par_benchmark_2gpu.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/par_benchmark_2gpu.py) — DQA benchmark: mqpu 2-GPU shot-parallel
- [`parallelisation_report.md`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/parallelisation_report.md) — Full benchmark comparison (mqpu / mpi4py / mgpu) on Kestrel H100s
- [`qae_example_backends.ipynb`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/qae_example_backends.ipynb) — Qiskit Aer GPU vs CPU backend comparison notebook
- [`binary_optimizer.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/binary_optimizer.py) — CUDA-Q QAE-based stochastic optimiser
- [`qae.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/qae.py) — Quantum Amplitude Estimation circuits
- [`resource_estimator.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/fix/cuda-q-script/qiskit_impl/resource_estimator.py) — Gate/qubit resource estimation utilities
- [`run_mgpu.sh`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/run_mgpu.sh) — Cray GTL wrapper for mgpu
- [`test_mgpu.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/test_mgpu.py) — GHZ circuit on nvidia mgpu target
- [`bench_mgpu_dqa.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/bench_mgpu_dqa.py) — DQA benchmark: noiseless + noisy mgpu timing
- [`par_benchmark_mgpu.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/par_benchmark_mgpu.py) — scaling benchmark across multiple GPUs with mgpu
- [`par_benchmark_mpi.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/par_benchmark_mpi.py) — DQA benchmark: mpi4py shot-splitting across ranks
- [`par_benchmark_2gpu.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/par_benchmark_2gpu.py) — DQA benchmark: mqpu 2-GPU shot-parallel
- [`parallelisation_report.md`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/parallelisation_report.md) — Full benchmark comparison (mqpu / mpi4py / mgpu) on Kestrel H100s
- [`qae_example_backends.ipynb`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/qae_example_backends.ipynb) — Qiskit Aer GPU vs CPU backend comparison notebook
- [`binary_optimizer.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/binary_optimizer.py) — CUDA-Q QAE-based stochastic optimiser
- [`qae.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/qae.py) — Quantum Amplitude Estimation circuits
- [`resource_estimator.py`](https://github.com/NatLabRockies/quantum_stochastic_programming/blob/main/qiskit_impl/resource_estimator.py) — Gate/qubit resource estimation utilities