The Key to Quantum Computing AI Applications: A Flexible Programming Language

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The advancement of quantum computing promises to reshape artificial intelligence (AI) as it is known and implemented today. This development is significantly expanding the enterprise and commercial reach of AI, possibly even closer to Artificial intelligence. And there is another promise of the convergence of quantum computing, AI, and programming languages ​​into a single computing environment.

The potential effects of this combination of abilities are nothing short of dramatic. Deep learning applications will run much faster. The problems they solve will reach a level of complexity that defies traditional approaches to advanced machine learning. Statistical and symbolic AI will run in tandem, while verticals from energy production to finance will reap the benefits.

However, this would not have happened without the support of flexible AI programming languages. Such programming languages ​​are indispensable for writing AI algorithms powered by quantum computing to create advanced applications with the ability to transform the use cases in which they are deployed.

By taking advantage of these programming language With the ability to support models for object-oriented, reflection, procedural and functional programming as well as meta-programming, organizations can harness the combination of these capabilities to achieve high levels of productivity. horizontal that cannot be obtained.

Programming language

As the foundation for writing efficient quantum AI applications, adaptive programming languages ​​designed specifically for this task are extremely useful for developers. These high-level languages ​​make it easy to shorten the time it takes to code while increasing throughput in doing so. The best are related to functional programming, which is often contrasted with and considered superior to imperative programming.

The ability of these AI languages ​​to change dynamically while the program is running is superior to that of batch method-based languages, where the program must be compiled and executed before export. Plus, these quantum AI programming languages ​​allow both data and code to be written as expressions. Since the functions in these frameworks are written like lists, they are easily treated like data, so concrete programs can actually manipulate other programs through metaprogramming – that is the key to their fundamental versatility. This advantage also translates into a performance benefit, where such languages ​​work much faster in applications – such as those for genome-related bioinformatics – supported by larger sizes. different dimensions of AI.

AI effect

When enabled by flexible programming languages to develop AI, quantum computing allows organizations to perform AI calculations much faster and at a larger scale than they can. These programming languages ​​also underpin both statistical and symbolic AI approaches enhanced by quantum computing. For example, optimization problems are traditionally solved in a knowledge graph implementation that supports intelligent inferences between constraints.

For applications of advanced machine learning (ML), for example, writing AI algorithms underpinned by quantum computing will reduce the amount of time it takes to bring new drugs to market. There are even universally applicable data science applications to train better ML models with less computational overhead. In all of these use cases, the key to delivering AI solutions enhanced by quantum computing is the array of programming languages ​​that allow developers to write algorithms that clearly benefit from quantum computing. speed and scalability of quantum computing methods.

Quantum Computation

Although there are several other ways, the two basic ways in which quantum computing provides the above benefits are through quantum computing and quantum annealing. Each of these functions involves specialized hardware for quantum computers that are more efficient than traditional computers at solving problems at the scale and speed at which AI becomes super-fast. Quantum computers encode information as 0, 1, or both – simultaneously – in quantum bits (qubits)whereas traditional computers can only encode them as 0s or 1s. The ability to superimpose these states is one of the ways quantum machines process huge amounts of data at once.

Another method is through quantum annealing, which reflects nature in that it solves even difficult NP problems by reaching the lowest energy states of the computer. Traditional computers take an exponential amount of time to solve certain problems, such as concerns about optimization problems related to vehicles, fuel consumption, goals delivery and other issues. Quantum annealing methods accelerate the time it takes to reach answers to such problems, delivering a level of actionable efficiency that’s critical for logistics or routing equipment in the industry. tourism and transportation.

Programming language gatekeeper

The benefits of applying quantum computing to accelerate and strengthen the overall utility of AI for society and business are clear. However, much less attention has been paid to the programming languages ​​used to design these quantum AI applications. These frameworks are the gatekeepers of the future of quantum AI. Wise organizations are using them to capitalize on this growing growth.

Jans Aasman, Ph.D., is a cognitive science expert and CEO of Franz Inc.


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