From b62fdc12f89ff722cdd344f3c84e67dd68f21f52 Mon Sep 17 00:00:00 2001 From: PabloAndresCQ Date: Tue, 14 Nov 2023 17:32:43 +0000 Subject: [PATCH 1/2] Updated broken links and added compute capability --- README.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 1c8fec4f..576073d2 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ Both components have both C and Python API. `pytket-cutensornet` is an extension to `pytket` that allows `pytket` circuits and expectation values to be simulated using `cuTensorNet` via an interface to -[cuQuantum Python](https://docs.nvidia.com/cuda/cuquantum/python/index.html). +[cuQuantum Python](https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/index.html>). Currently, only single-GPU calculations are supported, but a multi-GPU execution will be implemented in the due course using `mpi4py` library. @@ -29,9 +29,10 @@ implemented in the due course using `mpi4py` library. ## Getting started `pytket-cutensornet` is available for Python 3.9, 3.10 and 3.11 on Linux. -In order to use it, you need access to a Linux machine with either `Volta`, `Ampere` -or `Hopper` GPU and first install `cuQuantum Python` following their installation -[instructions](https://docs.nvidia.com/cuda/cuquantum/python/README.html#installation). +In order to use it, you need access to a Linux machine with an NVIDIA GPU of +Compute Capability +7.0 (check it [here](https://developer.nvidia.com/cuda-gpus>)) and first +install `cuQuantum Python` following their installation +[instructions](https://docs.nvidia.com/cuda/cuquantum/latest/python/README.html#installation>). This will include the necessary dependencies such as CUDA toolkit. Then, to install `pytket-cutensornet`, run: From 12c814d627044df559f06cbcf52bf7c2e3bbb1bb Mon Sep 17 00:00:00 2001 From: PabloAndresCQ Date: Tue, 14 Nov 2023 17:36:05 +0000 Subject: [PATCH 2/2] Some typos --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 576073d2..8ede2e93 100644 --- a/README.md +++ b/README.md @@ -9,9 +9,9 @@ This repository contains the pytket-cutensornet extension, using Quantinuum's with tket, a quantum computing toolkit and optimisation compiler developed by Quantinuum. -[cuTensorNet](https://docs.nvidia.com/cuda/cuquantum/cutensornet/index.html) is a +[cuTensorNet](https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/index.html) is a high-performance library for tensor network computations, developed by NVIDIA. -It is part of the [cuQuantum](https://docs.nvidia.com/cuda/cuquantum/index.html) SDK - +It is part of the [cuQuantum](https://docs.nvidia.com/cuda/cuquantum/latest/index.html) SDK - a high-performance library aimed at quantum circuit simulations on the NVIDIA GPU chips, consisting of two major components: - `cuStateVec`: a high-performance library for state vector computations. @@ -21,7 +21,7 @@ Both components have both C and Python API. `pytket-cutensornet` is an extension to `pytket` that allows `pytket` circuits and expectation values to be simulated using `cuTensorNet` via an interface to -[cuQuantum Python](https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/index.html>). +[cuQuantum Python](https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/index.html). Currently, only single-GPU calculations are supported, but a multi-GPU execution will be implemented in the due course using `mpi4py` library. @@ -30,9 +30,9 @@ implemented in the due course using `mpi4py` library. `pytket-cutensornet` is available for Python 3.9, 3.10 and 3.11 on Linux. In order to use it, you need access to a Linux machine with an NVIDIA GPU of -Compute Capability +7.0 (check it [here](https://developer.nvidia.com/cuda-gpus>)) and first +Compute Capability +7.0 (check it [here](https://developer.nvidia.com/cuda-gpus)) and first install `cuQuantum Python` following their installation -[instructions](https://docs.nvidia.com/cuda/cuquantum/latest/python/README.html#installation>). +[instructions](https://docs.nvidia.com/cuda/cuquantum/latest/python/README.html#installation). This will include the necessary dependencies such as CUDA toolkit. Then, to install `pytket-cutensornet`, run: