Advanced Computation Meets Generative AI

It’s no secret: quantum computing has been poised to be “the next big thing” for years. But recent developments in the quantum ecosystem, including major investments by companies such as IBM, Google, Microsoft and others, are the best indicators that now is the time to begin preparing for potentially viable quantum applications—and to identify where and when to most effectively use them.

“Classical” computers operate in a binary fashion, processing information as either zeros or ones. But quantum computers? They leverage quantum mechanics so data can exist simultaneously in multiple states—zeros, ones or both. The result is a supercomputer that can explore countless possibilities at once and produce results in minutes or seconds instead of hours, weeks or years.

Projected use-case areas span multiple industries and market sectors. In finance, for example, quantum computing could be utilized for portfolio optimization, risk management and algorithmic trading. In healthcare, it could enable advancements in drug discovery, personalized medicine and disease modeling. In logistics, it could optimize supply chain management, route optimization and scheduling.

At the same time, there’s a gap between what quantum can do at the current moment versus anticipated capabilities in the next three to five years.

“In each real-world application, the first step in solving the problem is translating it to a computational problem. After that’s done, you need to choose—or develop—an algorithm for solving that problem. And because these applications are things people do every day, there are clearly a number of ‘classical’ computing algorithms that are known—and still being explored—for solving the associated problems,” says John McNally, Wolfram Academic Innovation Solutions Developer. “The catch is that the scale of certain real applications can become too great for even the best classical computing resources to successfully carry out any known classical algorithm to reach a full solution.”

At this point, quantum algorithms and the hardware needed to run them become interesting. And there are important questions to address, in particular how to scale up quantum systems following practical fault-tolerant approaches.

And this is where the Wolfram Quantum Framework comes in.

Wolfram Quantum Framework

On its own, the Wolfram Quantum Framework is not a quantum computer. Instead, it is a set of tools to model quantum circuits and design algorithms. Then, after you’ve analyzed a quantum system using classical means, the Framework also gives you tools to automatically translate your models into representations that can be run on quantum hardware through service connections with Amazon Braket and other providers:

• Classiq
• Infleqtion
• Q-CTRL
• QuEra
• Strangeworks

The Wolfram Quantum Framework’s key advantage, however, is its seamless integration with Wolfram Language. This includes optimized numerics and symbolics that offer a streamlined approach to quantum computation.

“Aside from working numerically, you can specify noise channels, gates or even elements of a Hamiltonian as symbolic parameters,” McNally says. “This allows you to get exact formulas out of your analysis rather than numerical simulations only. Plus, it seamlessly integrates with other quantum platforms, even across different programming languages.”

Wolfram’s symbolic computation offers distinct benefits over numerically based programming languages by enabling the calculation of “exact” solutions to complex problems. For academia, this means a deeper understanding of quantum principles and the ability to explore new frontiers in research. But for businesses, prospective bottom-line returns include potential cost savings through more efficient algorithms, improved accuracy in simulations and predictions, and faster innovation cycles.

The Wolfram Quantum Framework continues to evolve with new features to expand the Framework’s support for different computational models, such as tensor networks and stabilizer formalism, as well as to accommodate a wider range of quantum tasks. Additionally, plans are underway to introduce enhancements to improve usability, scalability and compatibility with emerging quantum hardware technologies.

The Future of Quantum

Navigating quantum is more than thinking about quantum algorithms alone: it also means understanding the world of classical algorithms to identify when practical problems make contact with the rapidly developing world of quantum hardware. This uniquely positions Wolfram Research to address challenging quantum problems and develop quantum utilities, particularly considering its longstanding development of classical algorithms and well-developed footprint in academia.

In a larger sense, the development of quantum capabilities is going to result in a great divide: the haves and the have-nots, or more importantly, the dids and the did-nots. While quantum hardware is not yet where it’s going to be for use on an industrial scale, the time is now to begin planning for its use when it is. And while this may seem a bit nebulous, vision is the art of seeing what is invisible to others, and those who understand how quantum will benefit them stand to reap the most significant rewards.

Contact the Wolfram Consulting Group to learn how the Wolfram Quantum Framework can provide insights and tools for innovation.

In an economic environment where costs are rising, businesses are searching for new ways to improve margins, ideally by increasing productivity while lowering costs at the same time. Generative AI is offering a quickly growing toolbox for enhancing efficiency and reducing operational expenses with relatively low targeted investments. For example, AI tools can be used to process large amounts of documents, images or video content as well as to automatically generate new content at high quality.

It is not difficult for organizations to develop a multitude of ideas of how to put generative AI to work—indeed, the potential seems almost unlimited. But developing a comprehensive AI strategy for a business is a big challenge at a time when foundational technologies appear to evolve on a weekly basis.

The generative AI ecosystem is moving at a breathtaking speed, with new players arriving daily and established players at risk of disappearing. Big, commercial large language models (LLMs) are leading the scoreboards, but smaller and open-source models, including those with commercially viable licenses, are catching up quickly. The cost structure of operating LLMs is currently dominated by a scarcity of specialized hardware for AI clusters, with delivery times of a year or more for large customers. Selecting the right set of tools from an avalanche of unproven and quickly changing open-source projects is another considerable challenge.

It seems hard to pick the right combination of tools, AI models and technology suppliers for long-term tech investments, especially for organizations (including large, established consulting firms and IT service providers) that lack the expertise to implement generative AI. So what is a safe approach to creating an AI strategy if you do not want to miss out on this exciting technology, while hedging your bets and minimize your risk?

Wolfram Consulting Group can help companies to navigate this quickly transforming landscape by beginning with carefully selected and sharply focused use cases, avoiding the pitfalls of premature and costly investments. By rapidly developing prototypes for the most promising application areas, clients can gain experience and build the expertise and confidence to develop a longer-term generative AI strategy in preparation for more profound and transformative changes.

Why has progress in education been generally slow—at least as measured by standardized international student assessments—compared to the rapid advances in technology over the past two decades? Why have learning management systems (LMSs), ubiquitous access to mobile devices and the availability of vast knowledge resources on the web contributed little to close the education gap between students from both higher- and lower-income backgrounds?

While there are many factors to consider, the single most effective way to boost the rate of learning progress is one-on-one tutoring, which, of course, has not been affordable for students who would benefit from it most. Large language model technology (LLM), with its ability to converse like a human, promises to close this gap, providing a personalized (yet fully automated) AI tutoring experience for every child, in all common human languages.

Although standalone LLMs can be easily prompted to chat like a middle-school tutor, additional technology components are needed to achieve the full potential of this vision: an integration with Wolfram tools for AI to get the math and facts right (LLMs are weak in computation and tend to invent knowledge), long-term memory between sessions, generation of practice examples, automated assessment methods and data-driven personalization engines. LLMs will need to be tuned for this purpose and optimized for cost-efficient cloud operations. An essential part of this platform will be the ability to host custom, localized curricula, as well as assessment standards. Last but not least, data privacy and security need to be part of a sound plan.

To stay ahead of their competition, EdTech companies worldwide need to rethink their offerings and business models in order to develop a future-proof strategy for a world in which everyone has access to personal artifical intelligence. Navigating the rapidly evolving AI landscape and making business-critical investment decisions is a considerable challenge for anyone in education technology. While Wolfram’s R&D teams are already building the technical foundations of future AI education systems, Wolfram Consulting Group can help you now with developing an overall strategy, creating attractive product concepts and rapidly building prototypes of your future products and services.