Quantum NLP (natural language processing) is a relatively new use of quantum computing that is used to express the meaning of phrases as vectors encoded in quantum computers. The field of QNLP focuses on developing NLP models for use with quantum hardware. This article will explore how quantum NLP functions, its applications, and the lambeq toolkit. Before that, however, let’s take a look at conventional NLP and its applications in order to understand why QNLP has come into play.
Natural language processing is a subfield of linguistics, computer science, and artificial intelligence (AI), concerned with the interactions between computers and human language. It involves programming computers to process and analyze large amounts of natural language data.
NLP has become increasingly common in the commercial world for automated summarization, translation, sentiment analysis of media and content, and other sophisticated applications.
As human-computer interfaces become more prevalent, the demands placed on NLP are growing and show the limitations of conventional NLP. This is because most current NLP systems use the “bag of words” approach which uses strings of words defined only by their meanings without considering grammatical structure or composition.
Some of the applications of natural language processing include:
Given that people frequently use sarcasm and irony, interpreting natural language in the context of opinions is extremely challenging for machines. On the other hand, sentiment analysis can spot small variations in emotions and attitudes and assess their positivity or negativity.
Real-time sentiment analysis enables us to track social media mentions, monitor feedback on the most recent marketing initiative or product launch, and get a general idea of how an audience feels about a business.
Automatic question answering (QA) is performed by chatbots and virtual assistants that are programmed to comprehend natural language and produce relevant responses. While conventional QA systems adhere to pre-established rules, chatbots and virtual assistants powered by AI can learn from each contact and determine how to react. What makes them valuable is that they improve over time and learn from experiences.
One of the earliest applications of natural language processing was machine translation (MT). Unfortunately, MT still struggles to understand context, despite technologies like Facebook’s translations being deemed near ‘superhuman’.
If you’ve used Google Translate for a while, you’ll notice that it has advanced significantly since its beginnings. This is largely because of developments in the field of neural networks and greater accessibility to vast amounts of data.
Since automated translation improves communication, enables businesses to reach a wider audience, and makes it quick and affordable to read foreign material, it is particularly helpful in the business world.
Natural language processing can help marketers understand their clients better in order to develop more successful strategies. It aids in market research by analyzing subjects, sentiments, keywords, and intent in unstructured data to reveal trends and commercial prospects. Additionally, data analysis can be used to identify client trouble spots and keep an eye on competitors by seeing what things work for them and what don’t.
Email filtering is one of the most fundamental NLP applications. Spam filters were the first to notice certain terms or phrases that signified a spam message. But filtering has improved hugely over the years. One of the more widespread and recent applications of NLP is Gmail’s email categorization feature. The system evaluates whether emails fall into one of three groups based on their content (Main, Social, or Promotional). It keeps inboxes organized with key emails users need to view and respond to immediately.
Predictive text is an autocomplete feature that relies on NLP. For example, when you enter two or three letters into Google to run a search, a list of potential search terms is displayed. Additionally, if you search for something but make spelling errors, it fixes them and still returns relevant results.
Most people frequently use Google Search’s amazing autocorrect and autocomplete feature, but hardly ever give it a second thought. The feature is an excellent example of how NLP assists millions of individuals worldwide. It also makes locating relevant results simpler.
With the aid of various linguistic, statistical, and machine learning techniques, text analytics transforms unstructured text data into information that can be analyzed. Though organizations may find sentiment analysis intimidating, particularly if they have a sizable customer base, an NLP tool will comb through conversations with consumers, such as comments on social media and reviews. It then examines what is being said before deciding how to respond or improve service for better customer experience.
Brands can use the analysis of these interactions to find out how effectively a marketing campaign is performing or to keep an eye on the most frequent customer concerns. Finding structure or patterns in unstructured text data as well as extracting keywords are other ways that NLP supports text analytics.
Natural language processing has many use cases in the digital world. This list will expand as more organizations and industries adopt it and recognize its benefits. While a human touch is crucial for complicated communication challenges, NLP certainly makes life easier by managing and automating simpler jobs before moving on to complex ones.
In quantum NLP, the meaning of phrases is expressed as vectors that are encoded in quantum computing. To do this, the categorical compositional distributional (DisCoCat) model - which extends the distributional meaning of words by the compositional meaning of sentences - uses vectors that represent the meaning of words and combines them using the syntactic structure of the phrase. An algorithm based on tensor products is used for this. The method performs poorly on conventional computers but does well on quantum circuits.
Quantum NLP is an upcoming giant in technology. Despite the fact that it is still in development, it has already demonstrated a wealth of potential benefits and opportunities as it outperforms state-of-the-art methods.
The field of quantum computing is projected to expand quickly in the next few years after decades of debate and experimentation. Verified Market Research analysts predict that the market will grow from $252.2 million in 2017 to roughly $1.8 billion by 2028, driven partly by increase in computing power, workloads in data centers, and the continuous migration to software-as-a-service (SaaS).
In order to create novel models for language and other cognitive phenomena, designers take advantage of structural correspondences between the matching compositional structures supporting the quantum theory (such as process theories and tensor networks) and formal and natural languages. They may implement their models on quantum computers and use any advantages from the mathematical linkages between quantum theory and language models.
Though quantum NLP is still a relatively new field of study and applications to date have primarily focused on simple tasks, it has already shown a multitude of potential advantages and opportunities.
As per an article in an applied science journal published by MDPI in 2022, according to the widely supported “quantum native” theory of QNLP, a quantum-based strategy may theoretically control the mechanics of natural language more effectively. Quantum language models would consequently be more suitable for understanding and explaining natural language events in a consistent way with actual human cognitive processes. However, there is currently no concrete evidence to support this theory other than in very specific situations, i.e., simple sentences with a constrained vocabulary. This is not typical of how people acquire or develop language.
Theoretically, introducing different sentence types with various constructions using context free grammar and pregroups is expensive because, in some situations, it necessitates the creation of these resources from scratch.
Concerns have also been raised regarding these types of grammars’ ability to simulate a variety of linguistic occurrences. Regarding the “quantum advantage”, some QNLP models running on conventional hardware have outperformed state-of-the-art baselines in several tasks.
Quantum NLP models have been implemented to manage NLP characteristics that have always been crucial to dealing with the traditional probabilistic models, such as interference phenomenon in information retrieval, term dependencies, or ambiguity resolution.
QNLP considers that sentences are networks. A phrase functions more like a network than merely a collection of words, with diverse words interacting in different contexts. Mehrnoosh Sadrzadeh and Steve Clark established these networks a decade ago.
Instead of treating the sentence as a structureless bag accommodating the meanings of individual words, this resulted in a graphical representation of how the meanings of the words are connected to form the meaning of a sentence as a whole.
A constructed network would appear as follows:
In the example above, the sentence’s meaning is formed by the subject, Mayank, and the object, Ankita, which are both sent to the verb “loves”.
In the 1950s, Chomsky and Lambek, among others, pioneered a method for tracking the flow of words in sentences that combined the grammatical systems of all languages into a single mathematical structure. In particular, the compositional mathematical model of meaning is used to structure the network of a sentence’s meaning flow.
As an example of the experimental workflow, consider grammar category A or the mathematical model used to generate grammar diagrams. A grammar diagram (or network) decodes the flow of word meanings in a grammatical sentence. A diagram would be nothing more than the grammatical and syntactic parsing of a sentence based on the provided grammar model if one were to dig further into this idea.
This naturally raised the question of whether it was possible to have quantum computers understand natural language. First proposed in 2016 by Will Zeng and BC, the study established a new pedestal for NLP. However, there were certain difficulties with the idea. The main problem was the lack of suitably powerful quantum computers that could carry out the tasks that the NLP assigned.
It was also assumed that quantum random access memory (QRAM), which is still only theoretically feasible according to this data, could be used to encode word meanings on the quantum computer. Making quantum computers capable of processing everyday language is not just intriguing but also a logical next step for several reasons. A lot of businesses are developing concepts to make this prospect a reality.
lambeq, the first toolkit and library for quantum NLP was launched by Cambridge Quantum in 2021. The software is named after the late linguist and mathematician, Joachim Lambek.
The objective of lambeq is to assist programmers with developing practical QNLP applications for activities like text mining, automated dialogue, language translation, bioinformatics, and text-to-speech.
Bob Coecke, Chief Scientist at Cambridge Quantum, states that lambeq “automates tasks that are fundamental for the large-scale execution of QML [quantum machine learning] pipelines built in terms of compositional models for language.” The toolset can be used by any academic or commercial researcher interested in investigating the potential of quantum computing for NLP.
lambeq can translate phrases into a quantum circuit. For the benefit of the global quantum computing community and the quickly expanding ecosystem of quantum computing researchers, developers, and users, lambeq has been made completely open-source. The quantum software development platform, TKET, from Cambridge Quantum - which is also open-source - smoothly integrates with lambeq. As a result, QNLP developers now have access to a large selection of quantum computers.
The compositional-distributional (DisCo) style of NLP experiments, which Cambridge Quantum researchers have previously described, are made possible and automated using lambeq. This entails switching from syntax/grammar diagrams, which represent the structure of a text, to either tensor networks or quantum circuits built using TKET, ready to be optimised for machine learning applications like text categorization. Users of lambeq can switch components in and out of the model and have freedom in the architecture design, thanks to the modular design of the software.
For researchers and practitioners interested in AI and human-machine interactions, lambeq lowers access barriers. TKET presently has a user base that is in the hundreds of thousands globally. For the quantum computing community looking to interact with QNLP applications, which are among the most significant markets for AI, lambeq has the potential to become the most important toolbox.
In essence, hardware might be changed. For instance, instead of superconducting qubits, one could employ ion traps or optics. The toolkit's implementation will cause this development to proceed quite quickly. The computing paradigm might also be changed. For example, instead of circuits, one could utilize measurement-based quantum computation (MBQC) which has the capacity to transmit quantum states in a unit of time to speed up addition.
We could work on additional jobs besides question answering, such as language production, summarization, etc., rather than being limited to single sentences. Finally, we could simply scale up the size of the meaning spaces and complexity of the jobs as hardware gets more powerful, which is obviously the ultimate goal.
The newly released lambeq Python library from the Cambridge Quantum Computing team enables the direct encoding of DisCoCat instances on quantum devices. As a result, putting it into practice is now fairly simple. Future work can surely address the desired reduction in reliability on large datasets and complicated models with numerous parameters.
Questions still remain about quantum NLP implementation. From a theoretical perspective, these include scaling the context-free grammar (CFG) that underlie the models and the potential for producing strong formal descriptions for other languages. From a software viewpoint, it may take into account the use of more substantial real-world data and put more difficult QNLP tasks.
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