QC Simulators FAQ

Who can get the access to our quantum simulators?

To check eligibility, as well as to get an account, refer to Getting an account.

What are the possible future simulators?

With certainty, future updates of the existing simulators will be integrated regularly, and users will be notified of changes. Additional simulators could be added to the available ensemble depending on quality and demand.

What are the possible application fields for quantum computing simulators?

Many uncertainties still exist regarding useful applications of quantum computing, though research in the following areas has already received significant attention.

Quantum Chemistry: Modeling molecular structures, reaction mechanisms, and properties.
Optimization: Solving complex optimization problems in logistics, finance, and supply chains.
Cryptography: Developing and testing new cryptographic methods, especially for post-quantum cryptography.
Machine Learning: Enhancing machine learning models and algorithms through quantum speedups or novel approaches.
Material Science: Studying new materials at the atomic scale and predicting properties for innovative materials design.
Fundamental Physics: Testing hypotheses and models that are computationally demanding using classical methods.

How can I set up my own simulator environment?

Although it is possible to run quantum simulators on personal laptops, it is recommended to use the GWDG infrastructure to simulate higher number of qubits. As an HPC provider, GWDG offers significantly more compute power and storage facilities. It is also possible to consult experts in case of any doubt. Below are possible suggested steps to install your own local simulators:

We suggest creating an anaconda, miniconda or pyenv environment first.
Install the required packages with pip. A full list of the packages available/necessary for each quantum SDK is in the ‘Package dependencies’ column in the table on the quantum simulators overview page.
If you want to access a chosen SDK via jupyter notebooks, a pykernel needs to be created for the environment first.
Inside a notebook or python file, install the necessary packages and start writing circuits.
Limitations to consider: It is possible to potentially simulate up to 28 qubits on a local machine; however, the runtime is typically quite high (potentially multiple days). On a single GWDG compute node, it is possible to simulate typical 32 qubits circuits within 12 hours of runtime using just CPUs, and seconds or a few minutes with GPUs. GWDG also provides the option to run quantum simulators of GPU, provided quantum simulator has GPU support. It is possible to go beyond one compute node on GWDG infrastructure.

Do I need to know quantum mechanics to work with quantum simulators?

It depends on what you are working on and whether one is using Quantum Annealing-based quantum computers or gate-based quantum computers. At GWDG, we are focusing on gate-based quantum computers, which require less background in quantum mechanics. Additionally, due to the surge in the number of quantum simulators, most details have been abstracted and are hidden in the background. End users only need a high-level understanding of quantum mechanics and can focus on the quantum algorithmic aspects of their field. For example, in optimization, it is more important to have an understanding of algorithms like the Variational Quantum Algorithm (VQA) and the Quantum Approximate Optimization Algorithm (QAOA) than quantum mechanics itself.

How do I choose the most applicable simulator for my needs?

Consult the table on the quantum simulators overview page.

Why would I change my classical computing methods to quantum computing?

Currently, there are few quantum or hybrid applications that provide faster compute and/or more useful output than over classical applications, but the set of examples is ever growing and the barrier to entry is ever thinning, making it an increasingly attractive option for those looking to leverage the power of quantum computing.