From Synthetic Qubits to Real Insights: qBraid Democratizes AI4Quantum with the NVIDIA CUDA-Q platform

qBraid is making AI-driven quantum research faster, cheaper, and easier than ever. The NVIDIA CUDA-Q platform for hybrid quantum-classical computing includes new noisy-circuit simulators capable of generating mountains of training data on GPUs in minutes — and qBraid is rolling out some of the market’s lowest-cost, zero-setup GPU instances so that every student or startup can join the “AI4Quantum” wave. To prove it, the two companies are launching an AI4Quantum denoising demo and a hands-on San Francisco workshop + multi-month challenge later this year.

AI4Quantum: Why Now?

Quantum computers unlock new science, but today’s noisy qubits still hide the answers researchers need. Machine-learning models have shown early promise in cleaning up that noise, yet they crave two rare ingredients: (i) realistic, labelled training data and (ii) affordable GPU horsepower to crunch it.

CUDA-Q tackles the first hurdle by fusing NVIDIA cuQuantum libraries with a C++/Python API that can inject hardware-realistic error models to generate realistic data needed for training models. Using NVIDIA accelerated computing, we can power exact simulations of ~40-qubit circuits.

qBraid removes the second hurdle. Its cloud lab already packages CUDA-Q, Qiskit, and Cirq; now we are introducing pay-as-you-go NVIDIA A100, NVIDIA H100, and NVIDIA GH200 instances at some of the best prices on the market.

Demo Spotlight — Denoising Quantum Observables

2.1 What We Built

Quanta‑Bind, qBraid’s in‑house pipeline for simulating metal‑protein binding, pushed us to scale noisy quantum simulations well beyond 25 qubits. By re‑implementing the workflow in CUDA‑Q we cut the wall‑time for 30‑ and 32‑qubit circuits by almost 2× compared with an equivalent Qiskit stack (see Figure 1). The same CUDA‑Q backend now powers the demo below: a supervised model that learns to translate noisy expectation values into their noise‑free counterparts by producing data‑driven corrections. The training corpus of 600 noisy and noiseless observables was generated in minutes on a single GH200 using CUDA‑Q’s trajectory‑based noise simulator.

Figure 1: Simulation of representative quantum circuits generated by Quanta-Bind executed using both CUDA-Q and a competitor (Qiskit).

2.2 How CUDA-Q Helped

• Noise injection – the new cudaq.NoiseModel() class allows us to design realistic noise models to simulate near-term quantum hardware while bypassing the current dollar and time costs.
  
  
• Multi-GPU data ops – we leveraged the mqpu backend to batch circuit evaluations across multiple simulated QPUs. This drastically accelerates the synthetic data generation and allows for much faster research turnarounds.
  
  

2.3 Early Results

Using a custom graph attention-centered model, we achieved a >25% reduction in mean-squared error versus raw measurements on the synthetic data. The code for reproducing this experiment is available here.

Figure 2: Depiction of noisy and mitigated expectation values.

“CUDA-Q’s noisy simulator lets us quickly prototype weeks of hardware data overnight.” — Kenny Heitritter, VP of R&D @ qBraid

Zero-Setup, Low-Cost GPUs on qBraid

Starting this summer, every qBraid Lab user will be able to spin up an NVIDIA H100 80 GB GPU for just $1.50 per hour—no long-term commitment, no hidden surcharges. That rate undercuts many well-known public clouds by more than 50 percent and stands out because everything comes pre-loaded:

• Full quantum-AI software stack. CUDA-Q, Qiskit, Cirq, PennyLane, PyTorch, and TensorFlow are baked into the image, tuned for GPU acceleration, and verified nightly. That means you can generate synthetic noisy circuits with CUDA-Q and train models in PyTorch without ever touching a package manager.
  
  
• One-click access to the world’s largest catalog of quantum hardware. A sidebar inside Lab lists 20+ live QPUs (ion traps, superconducting, neutral-atom and more); jobs route via the qBraid SDK and credits system the moment you’re ready to graduate from simulation to hardware runs

Why it matters for AI4Quantum. With NVIDIA H100 throughput at your fingertips and zero environment friction, you can accelerate both model training and synthetic data generation, and then push the same circuits to real QPUs—without leaving a single browser tab. In short, qBraid’s $2/hr NVIDIA H100 tier turns quantum-AI research from a budgeting headache into a coffee-break experiment.

AI4Quantum Workshop + Challenge — San Francisco | Q4 2025

• One-day deep-dive on CUDA-Q, qBraid Lab workflows, and AI4Quantum applications
  
  
• Three competition tracks (TBD):
  
  
  
  1. Circuit Compression — shrink depth while preparing approximately the same state
     
     
  2. Noise Denoising — beat the baseline model above
     
     
  3. AI-Assisted Error Decoding — classify syndromes from surface-code sims
     
     
• Compute credits – every registrant gets 20 GPU hours; finalists earn GH200 time and swag.
  
  
• Register your interest

5 | Get Started Today

1. Run the demo: Open the “AI4Quantum/ml-denoising” code here on qBraid Lab.
   
   
2. Save your seat: Register your interest to receive updates on workshop updates and challenge rules here.
   
   

About qBraid

qBraid is the definitive one-stop platform for quantum computing.  As a deep-tech SAAS startup based in Chicago, qBraid provides seamless access to quantum software, quantum computers and NVIDIA GPUs through their development and deployment platform. The qBraid coding platform allows for automatic setup of difficult quantum software packages with integrations to quantum hardware provided by AWS, Rigetti, QuEra, IonQ, IQM, Microsoft, and others to provide their users the best experience doing all things quantum. The open-sourced qBraid SDK allows for turn-key access to quantum hardware where users can code in 15+ different quantum frameworks such as Qiskit, Cirq, or Amazon Braket. qBraid is on YouTube, LinkedIn, Twitter/X and GitHub. For more information on qBraid, visit https://www.qbraid.com.

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