@@ -129,7 +129,7 @@ choose a specific machine:
129129For GPU simulations, you may follow the instructions in [ this] ( tutorials/gcp_gpu )
130130guide to set up a virtual machine (VM) on Google Cloud Platform (GCP).
131131Alternatively, you can use your own hardware.
132- Note the [ hardware requirements] ( https://docs.nvidia.com/cuda/cuquantum/getting_started.html#custatevec )
132+ Note the [ hardware requirements] ( https://docs.nvidia.com/cuda/cuquantum/latest/ getting_started.html#custatevec )
133133for NVIDIA's cuQuantum when picking a GPU; in particular, it must have
134134CUDA Compute Capability 7.0 or higher.
135135At the time of writing, the following compatible GPUs are available on GCP:
@@ -140,8 +140,8 @@ At the time of writing, the following compatible GPUs are available on GCP:
140140* [ NVIDIA V100] ( https://www.techpowerup.com/gpu-specs/tesla-v100-pcie-16-gb.c2957 ) .
141141 Like the NVIDIA T4, this GPU has 16GB of RAM and
142142 therefore supports up to 30 qubits. It is faster than the T4.
143- Further, it is compatible with multi-GPU simulations. With 8 NVIDIA V100s (128GB ),
144- you can simulate up to 33 qubits.
143+ Further, it is compatible with multi-GPU simulations. With 4 NVIDIA V100s (64GB ),
144+ you can simulate up to 32 qubits.
145145* [ NVIDIA L4] ( https://www.techpowerup.com/gpu-specs/l4.c4091 ) . This GPU has 24GB
146146 of RAM and can therefore simulate up to 31 qubits. With eight of them (192GB), you can simulate
147147 up to 34 qubits.
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