BlueQubit Offers Managed Quantum Simulators Powered by NVIDIA Technology
Simulating quantum circuits is a key part of developing and testing quantum algorithms—but doing so efficiently calls for a great deal of computational power. At BlueQubit, we use cutting-edge NVIDIA GRID technology to provide seamless, managed quantum simulation services. The NVIDIA quantum development kit helps power our simulations on high-performance NVIDIA GPU clusters. This way, researchers, developers, and enthusiasts can explore quantum technology without the overhead cost of setting up complex computational environments.
What Is NVIDIA GRID Technology?
NVIDIA GRID technology delivers high-performance computing power through cloud-based access, eliminating the need for organizations to build and maintain their own specialized infrastructure. For businesses interested in quantum computing applications, this means you can experiment with advanced simulations without significant upfront hardware investments. Teams can focus on developing potential solutions rather than managing complex technical environments, reducing both cost and time-to-market for quantum-inspired innovations.
Quantum Simulators as a Managed Service
The quantum computing ecosystem is rich, with over 30 open-source quantum simulation libraries, each offering unique features and capabilities. Our team at BlueQubit has undertaken the colossal task of benchmarking these libraries to identify the most efficient tools for large-scale simulations.
Our findings reveal that GPU-based simulators leveraging NVIDIA cuQuantum, a toolkit designed to accelerate computing workflows on GPUs, stand out for their performance in large-scale experiments.
cuQuantum offers plugins for popular quantum computing frameworks such as Qiskit, Pennylane, and QSim, making it a versatile choice for a wide range of applications. BlueQubit integrated this library into its service stack to provide a managed quantum simulation service. Users can simply submit their quantum circuits—created in Qiskit, Cirq, or Pennylane—to BlueQubit servers, and we handle the rest. Our system eliminates the need for users to deal with containerization, virtual machines, GPU driver setup, and other technical complexities. Instead, they can focus on simulating quantum circuits on large GPU clusters. This approach allows up to 36-qubit universal state-vector simulations without any preparatory work needed from the users.
Quantum ML and Differentiable Quantum Programming on GPUs
Quantum machine learning (QML) represents a potential breakthrough for businesses working with complex data analysis challenges. By integrating quantum approaches with GPU acceleration, organizations may eventually tackle optimization problems that current systems cannot solve efficiently. For example, financial institutions might analyze market patterns more comprehensively, or logistics companies could optimize routing across thousands of variables. While these capabilities are still developing, our research demonstrates progress toward practical business applications that could deliver competitive advantages in data-intensive industries.
Our team at BlueQubit has developed modifications to the cuQuantum-Pennylane integration, driving the company to the forefront of quantum ML research. We have applied these advancements in our paper, “Hierarchical Learning for Quantum ML: Novel Training Technique for Large-Scale Variational Quantum Circuits,” where we demonstrate the training of 27-qubit, 1000-parameter Variational Quantum Circuits (VQC) models on GPUs.
This breakthrough was made possible by employing a novel technique that modifies adjoint differentiation, allowing the computation of gradients for all parameters in the VQC model with only two circuit passes. It reduces the computational overhead typically associated with training large-scale quantum circuits—a milestone in the field of QML.
Multi-Node GPU Simulation: The Sky’s the Limit
One of the major advancements in quantum simulation is the ability to perform multi-node GPU simulations. BlueQubit has taken advantage of this to exceed the traditional limits of quantum computing simulations. Traditionally, simulating more than 36 qubits was a huge challenge due to the exponential increase in computational resources required. But with the support of the NVIDIA quantum computer simulation technology for multi-node GPU simulation, BlueQubit users can go beyond these limitations with only a few lines of code.
The number of qubits we can simulate is now primarily limited by the number of GPUs we can harness in unison. This breakthrough has made it possible to simulate quantum systems that were previously thought to be out of reach, paving the way for quantum research and development. It is currently offered exclusively in the Team tier of our services and is a valuable tool for advanced research teams looking to push the boundaries of quantum computing.
Quantum-Assisted Training of Boltzmann Machines on GPUs
Our R&D team at BlueQubit is also using fast GPU simulators to perform quantum-assisted training of Boltzmann Machines (BMs) on GPUs as part of our efforts to find quantum advantage. Quantum-assisted training of BMs makes room for faster convergence in certain sampling algorithms compared to their classical counterparts. The speed and performance on GPU simulators are crucial for our iterative process, allowing us to quickly test and refine our quantum algorithms.
Our preliminary findings suggest that quantum-assisted training of BMs, when simulated on GPUs, shows promising results in terms of time to convergence for certain sampling problems. As we continue to work in this direction, we are preparing a paper that will provide more technical details on our methodology, findings, and the implications of our research. This forthcoming publication aims to further clarify the potential of quantum computing, particularly in the fields of machine learning and optimization.
Tensor Network Simulators: The New Approach to Quantum Programming
Another innovative feature that BlueQubit offers is the use of Tensor Network simulators. Tensor Networks provide a new approach to simulating certain types of quantum circuits that are low in entanglement, making it possible to overcome the traditional memory limitations of 36 to 40 qubits. With this technique, we can simulate circuits with more than 100 qubits in some cases. This is a huge leap forward, as it allows for the simulation of much larger and more complex quantum systems than ever before.
The ability to simulate such large systems is key to advancing our understanding of the quantum mechanical model and for developing new quantum algorithms. This feature, like multi-node GPU simulation, is also experimental and currently exclusive to the Team tier. It represents another step toward making quantum computing more accessible and practical for a wider range of applications.
The Road Ahead
Ten years ago, the quantum processing units (QPUs) available could be simulated on a laptop. Today, we have quantum computers with hundreds of qubits, far beyond the capability of any personal computing device. Running large-scale quantum experiments now requires a lot of computing power, often in the form of large CPU and GPU clusters.
As quantum computing evolves, it’s a good idea to have solid benchmarks and baselines for large-scale numerical experiments, particularly in the QMLsphere. This is where BlueQubit excels. The platform uses NVIDIA GPU emulators to help users run large-scale quantum simulations on GPU clusters. Through our managed quantum computer simulator software powered by NVIDIA GPUs, we are opening new avenues for innovation and exploration in the quantum computing domain—and making the quantum future more accessible than ever before.
Frequently Asked Questions
How does BlueQubit use NVIDIA technology for quantum simulation?
BlueQubit integrates the NVIDIA quantum computing SDK to run high-performance simulations on state-of-the-art GPU clusters. This technology speeds up the simulation of quantum circuits, especially when working with frameworks like Qiskit, Cirq, or Pennylane. The result is a faster, more scalable environment for testing algorithms and conducting research.
What is a managed quantum simulator?
A managed quantum simulator is a cloud-based platform that allows users to simulate quantum circuits without setting up local environments or managing infrastructure. At BlueQubit, everything from GPU clusters to software libraries is pre-configured so that users can run simulations with just a few lines of code. This simplifies access to powerful resources and allows for fast experimentation at scale.
Can I run machine learning experiments using BlueQubit simulators?
Yes, BlueQubit supports quantum machine learning through tools like Pennylane and its integration with GPU-based simulation. Developers can run differentiable quantum circuits and train large models efficiently using adjoint differentiation and custom enhancements by BlueQubit. This setup is especially useful for exploring variational quantum algorithms and hybrid quantum-classical models.
