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The advance of quantum computing promises to take a new shape artificial intelligence (AI) as it is known and deployed today. This development dramatically expands, and perhaps even closer, the enterprise and commercial reach of AI artificial general intelligence† And there is another promise of convergence of quantum computing, AI and programming languages into a single computing environment.
The potential effects of this amalgamation of capabilities are nothing short of formidable. Deep learning applications will run much faster. The problems they solve will reach a complexity beyond traditional approaches to advanced machine learning. Statistical and symbolic AI will coexist, while verticals from energy production to finance will reap the rewards.
However, none of this will happen without enabling flexible AI programming languages. Such programming languages are indispensable for writing AI algorithms powered by quantum computing to create advanced applications with the power to transform the use cases for which they are deployed.
By using this adaptive programming languages With the power to support object-orientation, reflection, procedural and functional programming, and meta-programming paradigms, organizations can leverage this combination of capabilities to achieve a degree of horizontal productivity that would otherwise not be possible.
As a 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 reduce the time it takes to write code while increasing throughput. The best involve functional programming, which often contrasts with, and is considered superior to, imperative programming.
The dynamic ability of these AI languages to change as the program runs is superior to languages that rely on a batch method, where the program must be compiled and run before running. In addition, these Quantum AI programming languages make it possible to write data as well as code as well as expressions. Because functions in these frameworks are written as lists, they are easily processed as data, allowing specific programs to manipulate other programs via metaprogramming – essential to their underlying flexibility. This advantage also translates into performance benefits with such languages running much faster in applications – such as those for bioinformatics with genomics – aided by different dimensions of AI.
The AI effect
When enabled by flexible programming languages for developing AIQuantum computing allows organizations to perform AI computations much faster and on a larger scale than they could otherwise. These programming languages also support both statistical and symbolic AI approaches enhanced by quantum computing. For example, optimization problems are traditionally solved in knowledge graph settings that support intelligent inferences between constraints.
For advanced machine learning (ML) applications, writing AI algorithms enhanced by quantum computing reduces the amount of time it takes to bring new drugs 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 coming up with AI solutions enhanced by quantum computing is the suite of programming languages that allow developers to write algorithms that unambiguously take advantage of the speed and scalability of quantum computing methods.
While there are several others, the two main ways in which quantum computing provides the benefits mentioned above are through quantum computation and quantum annealing. Each of these features includes specialized quantum computing hardware that is more effective than traditional computers at addressing problems at the scale and speed at which AI is overloaded. Quantum computers encode information as zeros, ones, or both – at the same time – in quantum bits (qubits), while traditional computers can only encode them as zeros or ones. The ability to superimpose these states is one of the ways quantum machines process massive amounts of data at once.
Another is via quantum annealing, which is reflective of nature as 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 taking care of optimization problems related to vehicles, fuel consumption, delivery targets and others. Quantum annealing methods shorten the time it takes to find answers to such problems, providing a measure of usable efficiency that is critical to logistics or routing equipment in the travel and transportation industry.
The gatekeeper of the programming language
The benefits of applying quantum computing to accelerate and support the overall benefit 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 gatekeepers to the future of quantum AI. Smart organizations use them to respond to this growing development.
Jans Aasman, Ph.D., is a cognitive science expert and CEO of Franz Inc.
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