Frequently Asked Questions

TQ42 is a software as a service (SaaS) platform where you can work with your teams to build, tune, and run optimization, machine learning, and simulation solutions for your toughest business problems. Not sure where to start? Check out our use cases to learn more about how we’re solving real-world problems.

Terra Quantum’s breakthrough algorithms tackle complex problems in the areas of Machine Learning, Optimization, and Simulation. They operate as building blocks that complement each other, coming together in hybrid solutions tailored to each specific problem.

In some cases, clients may find out-of-the-box solutions work well for their use cases and can make immediate use of application modules (algorithms applied to specific use cases) and/or the algorithms themselves via a TQ42 software license. Other times, Terra Quantum’s professional services may be needed to help you craft the perfect solution. Either way, we help you achieve a common goal: solving complex problems more efficiently.

The TQ42 Python Software Development Kit (SDK) puts the power in your hands to accelerate delivery of custom, high-impact solutions. The SDK comes complete with developer documentation on SDK commands and each of our algorithms, and supports Python and command line interface (CLI).

In collaboration with its joint venture QMware, Terra Quantum offers a full-stack hybrid quantum cloud solution. This provides high-performance compute underpinned by a unique in-memory centric multiprocessing system that harnesses the best of the quantum and classical worlds across both software (SDK, Libraries, APIs) and hardware (CPUs, GPUs, QPUs). Through TQ42, customers can execute code on different types of compute resources, both classical and quantum, as well as quantum simulators.

Hybrid quantum computing refers to the blended nature of using classical and/or quantum-inspired algorithms to run on classical and/or quantum infrastructure. It doesn’t rely on a fully classical, nor a fully quantum compute stack. Instead, it uses the best of both worlds to achieve the objective as quickly and accurately as possible, whether that be optimizing a route, predicting options prices, simulating the most effective designs, etc.

One advantage is it allows researchers and engineers to harness the potential computational advantages of quantum algorithms and compute resources while still relying on classical computers to manage the overall workflow, error correction, and other tasks where quantum computers are not yet mature. In many cases, quantum-inspired algorithms, such as those requiring deep neural networks, are well-suited for execution on classical compute infrastructure, such as CPUs and GPUs. Quantum simulators are also useful in running code that is primed for execution when fault-tolerant quantum computers become the norm.

TQ42 is a proprietary platform (not open-source), and is fully built and maintained by Terra Quantum. Our expert team of 200+ quantum scientists – mostly PhDs based in Europe and North America – relentlessly tackle the intractable, solve the unsolvable, and invent approaches to power the industrial applications of tomorrow, today. One of our goals is to make it easy for non-technical users to apply hybrid quantum solutions to their complex challenges today. This means while you may be an expert in building circuits, you don’t have to be to make the most of TQ42.

TQ42 is not generally available yet. We are actively accepting select clients to join our Beta program and provide valuable feedback as we continue to build the platform. If you are interested in learning more, please join our waiting list.

TQ42 will accommodate a variety of business sizes and budgets. Please stay tuned at tq42.com for more on pricing, coming soon!

TQ42 is built for business and technical audiences in a variety of industries who have a common goal of solving complex business problems. Our user interface makes it easy for business users to run experiments on a variety of problem types, while our Python SDK makes it accessible for developers to build and run experiments using our algorithm APIs. Machine learning experts can also train and tune models, making them available to others on their teams to use in real-world applications.

Whether you’re an expert at building solutions for your company or you’re just getting started in learning how quantum technology might apply to your business, TQ42 is for you.

To prepare the data for an experiment run, developer guides and tutorials help you learn how to format the data according to your use case. You will source the data independently and transform it into vector format in your own environment prior to running the experiment on the TQ42 backend.

The code you provide when submitting an experiment run will provide a reference to the objective function that will inherently contain all the data to be run in the experiment. More information on how to prepare the data and objective function can be found in our developer documentation.

You will use Python or CLI commands to define and submit the code for an experiment (including an endpoint with your vectorized data) via our RESTful services to TQ42.

For more, see the End User License Agreement and our Platform Privacy Policy. Additionally, a customer agreement and data processing addendum is provided for all clients during onboarding.

Our platform offers flexibility and extensibility, with the option to connect to your existing cloud services platforms like Azure, AWS, and more to keep your data and intellectual property secure.

For more, see the End User License Agreement and our Platform Privacy Policy. Additionally, a customer agreement and data processing addendum is provided for all clients during onboarding.

We do not yet support streaming data, but high-performance computing is a priority for us as we work with enterprise clients with sophisticated tech stacks and time-sensitive, high-volume data.

No. While the algorithm you are working with may be quantum-inspired and may run on hybrid quantum compute resources, you can work with the data you have available today. However, your data format will differ depending on the use case you are solving, and may need to be converted into a vector format and/or provided in an endpoint URL as an objective function. More information is available in our developer documentation.

Using the algorithm APIs, you can run experiments with real or sample data to test the process and see if the algorithm might work for you and your dataset. Furthermore, our application modules come equipped with industry-specific sample data and sample code to help you get started.

Our goal is to help you determine quickly if the pre-configured parameters of the algorithm APIs used to solve the problem will work for you, or if you need to tune them to your specific needs.

Whether accessing TQ42 via the graphical user interface (GUI) or the Python SDK, each user must authenticate using OIDC / OAuth2 standard protocols to access the platform. Two-factor authentication is also available for users who prefer – or are required to use – an extra layer of security.

Your organization is responsible for administering projects, running experiments, and managing the model lifecycle for a variety of solutions in the machine learning, optimization, and simulation spaces. With the submission of the experiment run, infrastructure will be provisioned and deployed on demand to run the requested experiment, providing isolation and security on your account. The data remains in your possession and is not visible to Terra Quantum.

TQ42 is currently available as a cloud-hosted quantum-as-a-service (QaaS) product; however, we are continuously evaluating customer needs and may offer an on-premise solution in the future.

If this is a critical requirement, please see Divis intelligent solutions. Part of Terra Quantum since 2023, Divis offers ready-to-use software products for data analysis, auto-ML and optimizing production. The software is available as a stand-alone GUI, Python package, and/or an Excel Add-In.

TQ42 allows your company’s Organization Owner and other authorized members to invite and manage users, providing fine-grained authorization by user. This always includes the type of permissions for the user and level of application – that might be one project, multiple projects, or all the projects within the account. Data access and visibility is managed at the project level by authorized users.

Yes, trained machine learning models can be shared with others working on the same project. Data used to train, test, or apply to those models is controlled by users with a specific dataset permission to ensure only authorized users have access to potentially sensitive information, even within a project.

Project collaborators with appropriate permissions can view each other’s experiments, including input parameters and results.

Results are visible to other project collaborators with appropriate permissions. Certain types of runs will produce results that require sensitive handling or obfuscation. In these cases, we will prompt you to provide the location of your cloud storage bucket where we can send the results file. Alternatively, you may fetch the data using our RESTful APIs.

Please contact us at info@terraquantum.swiss to learn more about our platform and security policies. We invite inquiries and open dialogue with all clients to ensure we are meeting your needs.