Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More
The advance of quantum computing has the promise of reshaping artificial intelligence (AI) as it’s known and deployed today. This development is drastically expanding AI’s enterprise and commercial outreach, perhaps even getting closer to artificial general intelligence. And there is another promise of convergence of quantum computing, AI, and programming languages into a single computational environment.
The potential effects of this coalescence of capabilities are nothing short of formidable. Deep learning applications will run much faster. The problems they solve will reach a complexity defying that of traditional approaches to advanced machine learning. Statistical and symbolic AI will run in tandem, while verticals from energy production to finance reap the benefits.
None of this will occur, however, without the enablement of flexible AI programming languages. Such programming languages are indispensable for writing AI algorithms bolstered by quantum computing to create advanced applications with the power to transform the use cases for which they’re deployed.
By availing themselves of these adaptive programming languages with the power to support paradigms for object orientation, reflection, procedural and functional programming, and meta-programming, organizations can harness this conjunction of capabilities to achieve a degree of horizontal productivity that’s not otherwise possible.
Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.
As the foundation for writing effective quantum AI applications, adaptive programming languages tailored for this task are immensely helpful to developers. These high-level languages make it easy to abbreviate the time required to write code while increasing throughput when doing so. The best ones involve functional programming, which is often contrasted with, and considered superior to, imperative programming.
The dynamic capability of these AI languages to change while the program is running is superior to languages relying on a batch method, in which the program must be compiled and executed prior to outputs. Plus, these quantum AI programming languages enable both data and code to be written as expressions. Since functions in these frameworks are written like lists, they’re readily processed like data, so specific programs can actually manipulate other programs via metaprogramming — which is key for their underlying flexibility. This advantage also translates into performance benefits in which such languages operate much faster in applications — such as those for bioinformatics involving genomics — aided by various dimensions of AI.
The AI effect
When enabled by flexible programming languages for developing AI, quantum computing allows organizations to perform AI calculations much faster, and at a greater scale, than they otherwise could. These programming languages also underpin both statistical and symbolic AI approaches enhanced by quantum computing. Optimization problems, for example, are traditionally solved in knowledge graph settings supporting intelligent inferences between constraints.
For applications of advanced machine learning (ML), writing AI algorithms fortified by quantum computing reduces the amount of time required for bringing new pharmaceuticals to market, for example. There are even data science applications that are universally applicable for training better ML models with less computational overhead. In all of these use cases, the key to devising AI solutions enhanced by quantum computing is the array of programming languages that empower developers to write algorithms that unequivocally benefit from the speed and scalability of quantum computing methods.
Although there are several others, the two capital ways quantum computing supplies the above benefits is via quantum computations and quantum annealing. Each of these functions involves specialized hardware for quantum computers that are more effective than traditional computers for tackling problems at the scale and speed at which AI becomes supercharged. Quantum computers encode information as 0’s, 1’s, or both — simultaneously — in quantum bits (qubits), whereas traditional computers can only encode them as 0’s or 1’s. The ability to superimpose these states is one of the ways in which quantum machines process gigantic quantities of data at once.
Another is via quantum annealing, which is reflective of nature in that it solves even NP-hard problems by reaching the lowest energy state of the computer. Traditional computers take an exponential amount of time to solve certain problems, such as concerns for optimization issues related to vehicles, fuel consumption, delivery objectives, and others. Quantum annealing methods expedite the time required to achieve answers to such problems, providing a degree of actionable efficiency that’s pivotal for logistics or routing equipment in the travel and transportation industries.
The programming language gatekeeper
The boons of applying quantum computing to accelerate and buttress the overall utility of AI for society and the enterprise are apparent. Much less attention, however, is given to the programming languages that are used to design these quantum AI applications. These frameworks are the gatekeepers for the future of quantum AI. Shrewd organizations are utilizing them to capitalize on this growing development.
Jans Aasman, Ph.D., is an expert in cognitive science and CEO of Franz Inc.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.
If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.
You might even consider contributing an article of your own!