gemini examples of coding quantum with nvidia platform and partners

 NVIDIA CUDA-Q integrates disparate regional software stacks and hardware platforms by acting as a unified, multi-level compilation bridge. Rather than requiring developers to code separate toolchains for each regional framework (such as Classiq in Israel, Pasqal in France, or IQM in Finland), CUDA-Q uses a modular Multi-Level Intermediate Representation (MLIR) and LLVM compiler infrastructure. [1, 2]

---

⚙️ The Unified Cross-Platform Compilation Pipeline

[ High-Level Code: Python / C++ / Classiq / Qrisp ]
                        │
                        ▼
         ┌─────────────────────────────┐
         │      CUDA-Q MLIR Core       │  ◄── Language-Specific Dialects
         └──────────────┬──────────────┘
                        │
                        ▼
         ┌─────────────────────────────┐
         │  Split Compilation Engine   │
         └──────┬───────────────┬──────┘
                │               │
                ▼               ▼
      [Host / GPU Code]   [Quantum Kernels]
      (NVVM / LLVM IR)    (QIR / Target APIs)
                │               │
                ▼               ▼
   [NVIDIA Hopper/Blackwell]  [Regional QPU Runtime]
   (cuQuantum / NVQLink)      (Pasqal QRMI / IQM / AQT)

1. High-Level Multilingual Frontends [1]

Developers can write code in standard Python or C++, or use high-level algorithmic builders like Israel's Classiq or Europe's Eclipse Qrisp. These tools abstract away tedious individual gate definitions into concise mathematical logic (e.g., native arithmetic overloading). [1, 2, 3, 4]

2. MLIR-Based Intermediate Representation [1]

The user's code is ingested by the nvq++ compiler and converted into custom MLIR dialects. MLIR allows CUDA-Q to understand quantum operations, classical variables, and tensor instructions inside a single unified data structure without forcing an early commitment to a specific hardware vendor. [1, 2, 3]

3. Split Compilation and Code Lowering [1]

The compiler executes a split compilation model: [1, 2]

• Classical Host/GPU Code: Instructions destined for classical compute are lowered into standard LLVM IR or NVVM IR to execute parallel math or state-vector simulations via cuQuantum on NVIDIA GPUs. [1, 2, 3]
• Quantum Kernels: Quantum-specific instructions are translated into Quantum Intermediate Representation (QIR). QIR acts as the universal adapter plug. [1, 2]

4. Backend Target Runtimes (The Regional Hand-Off) [1]

Through standardized runtime interfaces—such as France's Pasqal QRMI (Quantum Resource Management Interface) or low-latency hardware links via NVQLink—the QIR binary is dynamically routed to the regional QPU runtime of choice (whether trapped-ion, neutral-atom, or superconducting). This write-once, run-anywhere architecture means a single compilation script seamlessly targets hardware in Tokyo, Helsinki, Paris, or the US. [1, 2, 3, 4, 5]

If you'd like, I can provide a code snippet showing how a hybrid Python kernel compiles and alternates between a local GPU state-vector simulator and a remote QPU target.

please provide code snippet you suggest

Here is a hybrid Python code snippet using NVIDIA CUDA-Q (cudaq). This example demonstrates how to write a unified quantum kernel that runs a parameter-search circuit, alternating execution between a local GPU state-vector simulator and a remote QPU/simulator target backend without changing your core quantum logic.

python

import cudaq
import numpy as np

# 1. Define the quantum kernel (The "Write-Once" Core Logic)
# The cudaq.kernel decorator prepares this function to be lowered via MLIR/QIR.
@cudaq.kernel
def parameterized_circuit(angle: float):
    # Allocate a quantum register of 2 qubits
    q = cudaq.qvector(2)
    
    # Apply baseline operations
    h(q[0])
    cx(q[0], q[1])
    
    # Apply a parameterized rotation gate driven by classical input
    ry(angle, q[0])
    
    # Measure the qubits
    mz(q)

# 2. Configure the Execution & Target Routing
def run_hybrid_workflow():
    # Pass a specific test parameter angle (e.g., pi / 4)
    target_angle = np.pi / 4

    print("--- Execution Mode A: Local GPU State-Vector Acceleration ---")
    # By default, running kernel(...) targets the local classical simulator 
    # (leveraging cuQuantum acceleration under the hood on NVIDIA GPUs).
    local_result = cudaq.sample(parameterized_circuit, target_angle)
    print("Local GPU Simulation Counts:", local_result)

    print("\n--- Execution Mode B: Switching to a Remote/Regional QPU Target ---")
    # Switch the global target runtime to a specific backend (e.g., a mock or 
    # remote hardware target connected via QIR/REST API or NVQLink).
    # You can change 'default' or 'qpu' to your specific regional provider interface.
    try:
        cudaq.set_target("remote-simulator") # or an active partner QPU runtime target
        remote_result = cudaq.sample(parameterized_circuit, target_angle)
        print("Remote Target Execution Counts:", remote_result)
    except Exception as e:
        print(f"Note: Remote target offline or requires active cluster authentication. ({e})")
        # Revert safely back to local state-vector context
        cudaq.set_target("default")

if __name__ == "__main__":
    run_hybrid_workflow()

Use code with caution.

Would you like to see how to pass this CUDA-Q MLIR output directly into a multi-node Slurm job scheduler script for cluster execution on a regional AI supercomputer?

yes please show cuda-q output into multi node slurm job scheduler script

To run a hybrid NVIDIA CUDA-Q workflow across multiple cluster nodes, you submit the job using a Slurm workload manager script. The Slurm orchestrator allocates the multi-node GPU/QPU-bridged environment, loads the required CUDA-Q and container runtime environments, and executes your Python compilation script across distributed nodes. [1]

---

📝 Multi-Node Slurm Job Script (run_cudaq_cluster.sh)

Save this script as run_cudaq_cluster.sh on your regional supercomputing cluster (such as a cluster utilizing an NVIDIA Taipei-1 node layout or a national lab DGX cluster):

bash

#!/bin/bash
#SBATCH --job-name=cudaq-hybrid-cluster
#SBATCH --output=cudaq_%j.out
#SBATCH --error=cudaq_%j.err
#SBATCH --nodes=2                    # Number of compute nodes requested
#SBATCH --ntasks-per-node=8          # Number of MPI/GPU tasks per node (e.g., 8 H100 GPUs per node)
#SBATCH --gres=gpu:8                 # Request 8 GPUs per node
#SBATCH --cpus-per-task=12           # CPU cores allocated per task
#SBATCH --time=02:00:00              # Wall time limit (2 hours)
#SBATCH --partition=ai-cluster       # Designated high-performance cluster partition

# 1. Initialize Environment & Modules
module purge
module load nvidia/hpc-sdk
module load openmpi/4.1.5
module load cudaq/latest

# Set environment variables for multi-node communication via NCCL / InfiniBand
export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
export NCCL_DEBUG=INFO
export NCCL_IB_DISABLE=0
export CUBLAS_WORKSPACE_CONFIG=:4096:8

# 2. Output Node & Allocation Diagnostics
echo "Job ID: $SLURM_JOB_ID"
echo "Running on nodes: $(scontrol show hostnames $SLURM_JOB_NODELIST)"
echo "Total GPUs allocated: $SLURM_GPUS_ON_NODE"

# 3. Execute Distributed Hybrid CUDA-Q Job
# Using srun to distribute the python job leveraging cuQuantum state-vector 
# parallelism across the allocated multi-node multi-GPU fabric.
srun --ntasks=$SLURM_NTASKS --ntasks-per-node=$SLURM_NTASKS_PER_NODE \
    python3 -u /path/to/your/hybrid_workflow.py \
    --backend=cuquantum-distributed \
    --shots=1000000

Use code with caution.

---

⚙️ Submitting and Managing the Job

To submit this job script to your cluster's scheduler queue, run:

bash

sbatch run_cudaq_cluster.sh

Use code with caution.

To monitor the real-time execution status and inspect queue allocation:

bash

squeue -u $USER

Use code with caution.

Would you like to examine how to configure MPI-based state-vector slicing inside hybrid_workflow.py so that a massive quantum simulation state vector is split and distributed across the memory space of both Slurm nodes?

yes please examine how to configure MPI-based state-vector slicing inside hybrid_workflow.py so that a massive quantum simulation state vector is split and distributed across the memory space of both Slurm nodes

To simulate large quantum circuits (typically above 30 qubits), a single GPU runs out of memory because the quantum state vector grows exponentially (\(2^{N}\) complex numbers). To solve this, NVIDIA CUDA-Q integrates with cuQuantum (cuTensorNet/custatevec) and MPI (Message Passing Interface) to slice and distribute the state vector across the memory spaces of multiple GPUs across multiple Slurm nodes.

Here is how you configure your hybrid_workflow.py script to automatically detect the Slurm/MPI environment and distribute the workload.

---

🐍 The Distributed Python Code (hybrid_workflow.py)

This script uses cudaq.set_target("nvidia-mgpu") (Multi-GPU/Multi-Node Target) to activate distributed state-vector simulation.

python

import cudaq
import sys

# 1. Initialize the MPI-aware distributed target environment
# This tells CUDA-Q to look at the Slurm/MPI context and split the state vector.
try:
    cudaq.set_target("nvidia-mgpu")
except Exception as e:
    print(f"Failed to set multi-GPU target: {e}")
    sys.exit(1)

# Retrieve MPI rank info from the runtime to manage console output redundancy
import mpi4py.MPI as mpi
comm = mpi.COMM_WORLD
rank = comm.Get_rank()
num_ranks = comm.Get_size()

# 2. Define a Massive Quantum Circuit (e.g., 34 Qubits)
# A 34-qubit simulation requires 2^34 * 16 bytes = 256 GB of raw memory.
# This must be sliced and split across multiple H100 memory banks.
@cudaq.kernel
def massive_simulation_kernel(qubit_count: int):
    # Allocate the distributed qubit vector
    q = cudaq.qvector(qubit_count)
    
    # Create an entangled state across the entire multi-node fabric
    h(q[0])
    for i in range(1, qubit_count):
        cx(q[i-1], q[i])
    
    # Measure the entire distributed register
    mz(q)

def execute_distributed_job():
    num_qubits = 34
    num_shots = 10000
    
    if rank == 0:
        print(f"[Rank 0] Initializing distributed simulation on {num_ranks} MPI Ranks...")
        print(f"[Rank 0] Total required memory: ~256 GB. Memory will be sliced across nodes.")

    # 3. Execute the Sample
    # CUDA-Q automatically chunks the state vector across the InfiniBand fabric.
    # Synchronization happens automatically under the hood via NCCL.
    result = cudaq.sample(massive_simulation_kernel, num_qubits, shots=num_shots)

    # Prevent all ranks from flooding the Slurm log output
    if rank == 0:
        print("\n--- Distributed Execution Successful ---")
        print(f"Simulation result retrieved securely by Rank 0 Master Node.")
        print("Sampled Results (showing top counts):")
        print(result)

if __name__ == "__main__":
    execute_distributed_job()

Use code with caution.

---

🔬 How CUDA-Q and Slurm Orchestrate the Slicing

When Slurm executes srun python3 hybrid_workflow.py based on your previous batch configuration, the backend coordinates the simulation through several synchronized steps:

1. Rank Mapping: The nvidia-mgpu target initializes an MPI rank for every GPU allocated by Slurm (in your previous example, 2 nodes \(\times \) 8 GPUs = 16 total ranks).
2. State-Vector Slicing: The \(2^{34}\) complex amplitudes are cut into 16 equal slices. Each GPU holds exactly 16 GB of the total 256 GB state vector (\(256 \text{ GB} / 16 \text{ GPUs} = 16 \text{ GB per GPU}\)).
3. InfiniBand/NCCL Communication: When non-local gates (like a CNOT between a qubit on Node 1 and a qubit on Node 2) are executed, CUDA-Q automatically triggers sub-millisecond data swaps across your cluster's InfiniBand interconnections using the NVIDIA Collective Communications Library (NCCL). [1]

Would you like to examine how to add CUDA-Q error-mitigation algorithms (like Zero-Noise Extrapolation) to this multi-node pipeline before generating final outputs?