The Key to AI Applications in Quantum Computing: Flexible Programming Languages
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The advancement of quantum computing promises to reshape artificial intelligence (AI) as it is known and deployed today. This development greatly expands the enterprise and commercial reach of AI, perhaps even approaching artificial general intelligence. And there is another promise of convergence of quantum computing, AI and programming languages in a single computing environment.
The potential effects of this coalescence of abilities are nothing short of awesome. Deep learning applications will run much faster. The problems they solve will reach a complexity that defies traditional approaches to advanced machine learning. Statistical and symbolic AI will work in tandem, while verticals from energy production to finance will reap the benefits.
However, none of this will happen without enabling flexible AI programming languages. Such programming languages are indispensable for writing quantum computing-enhanced AI algorithms to create advanced applications capable of transforming the use cases for which they are deployed.
By availing themselves of these adaptive programming languages capable of supporting paradigms for object orientation, thinking, procedural and functional programming, and metaprogramming, organizations can harness this conjunction of capabilities to achieve a degree of horizontal productivity that is impossible. other.
As a foundation for writing effective quantum AI applications, adaptive programming languages suitable for this task are extremely useful for developers. These high-level languages make it easy to shorten the time needed to write code while increasing throughput. The best ones involve functional programming, which is often opposed to imperative programming and seen as superior to it.
The dynamic ability of these AI languages to change during program execution is superior to languages that rely on a batch method, in which the program must be compiled and executed before outputs. Moreover, these quantum AI programming languages allow both data and code to be written as expressions. Since functions in these frameworks are written as lists, they are easily treated as data, so specific programs can actually manipulate other programs via metaprogramming – which is essential for their underlying flexibility. . This advantage also translates into performance advantages where these languages run much faster in applications – such as those in bioinformatics involving genomics – aided by various dimensions of AI.
The AI effect
When enabled by flexible programming languages for AI development, quantum computing enables organizations to perform AI computations much faster and at greater scale than they otherwise could. . These programming languages also underpin statistical and symbolic AI approaches enhanced by quantum computing. Optimization problems, for example, are traditionally solved in knowledge graph parameters supporting intelligent inferences between constraints.
For advanced machine learning (ML) applications, writing quantum computing-enhanced AI algorithms reduces the time it takes to bring new pharmaceuticals to market, for example. There are even data science applications that are universally applicable for training better ML models with less computation time. In all of these use cases, the key to designing quantum computing-enhanced AI solutions is the range of programming languages that allow developers to write algorithms that unequivocally benefit from speed and reliability. scalability of quantum computing methods.
Although there are several others, the two main ways quantum computing provides the above benefits are quantum computations 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 supercharged. Quantum computers encode information as 0s, 1s, or both—simultaneously—in quantum bits (qubits), whereas traditional computers can only encode information as 0s or 1s. is one of the ways quantum machines simultaneously process massive amounts of data.
Another is via quantum annealing, which mirrors nature in that it solves even NP-hard problems by reaching the computer’s lowest energy state. Traditional computers take exponential time to solve certain problems, such as vehicle optimization issues, fuel consumption, delivery targets, etc. Quantum annealing methods accelerate the time needed to get answers to these problems, providing an actionable degree of efficiency that is essential for logistics or routing equipment in the travel and transportation industries.
The Guardian of the Programming Language
The benefits of applying quantum computing to accelerate and enhance the overall utility of AI to society and business are clear. However, much less attention is paid to the programming languages used to design these quantum AI applications. These frameworks are the guardians of the future of quantum AI. Clever organizations are using them to capitalize on this growing development.
Jans Aasman, Ph.D., is a cognitive scientist and CEO of Franz Inc.
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