lambeq : Quantum Natural Language Processing

admin — Sun, 17 Apr 2022 17:37:47 +0000

Quantum Guru has been writing on different applications of Quantum Computing and here we once again touch upon a foreseeable Quantum Computing application, Quantum Natural Language Processing (QNLP). It is assumed that Quantum computing applications will be prevalent and applicable in our day-to-day life. Some of the potential applications of quantum computing are considered to be in the following domains:

Artificial intelligence
Logistics
Machine learning
Cryptography
Genomics
Optimization

Artificial Intelligence could reap substantial benefits of quantum computing. More specifically, it’s the Natural Language Processing (NLP) where quantum computing could have significant and even fundamental influence.

QNLP

Natural Language Processing (NLP) allows machines to break down and interpret human language. It’s at the core of tools we use every day – from translation software, chatbots, spam filters, and search engines, to grammar correction software, voice assistants, and social media monitoring tools. The compute speed for quantum computing is exponentially higher than that of classic computing. NLP could greatly exploit the increased computational speed for real world human-like experience. The hardware required for this kind of speed up will have to be quantum capable. Thus QNLP, though the research is still in infancy, aims at the development of NLP models explicitly designed to be executed on quantum hardware.

Categorical Compositional Distributional

Categorical compositional distributional semantics, known as is DisCoCat uses category theory to combine the benefits of two very different approaches to linguistics namely, categorical grammar and distributional semantics. First introduced in 2010, it’s a powerful mathematical model for composing the meaning of sentences in natural languages.

Two important QNLP resources are Quantinuum and Lambeq:

Quantinuum – Honeywell Quantum Solutions and Cambridge Quantum have joined forces as Quantinuum to accelerate the delivery of real-world, quantum solutions. By uniting best-in-class software and enabling tools with the best-performing quantum computers, Quantinuum is delivering on the potential of quantum technology today. Quantinuum team majorly focuses on QNLP
Lambeq – It is the world’s first high level Python library software for QNLP and is capable of converting the sentences into quantum circuits. Its motivation is to accelerate the development of practical and real world QNLP applications, such as chatbot, language translation, TTS, language generation, bioinformatics and text mining. Lambeq facilitates and automates the design and deployment of compositional-distributional (DisCo) NLP experiments, as described by CQ scientists. This deployment involves moving away from syntax/grammar diagrams, which encode a text’s structure, and toward TKET-implemented (classical) tensor networks or quantum circuits, which TKET can optimise for machine learning tasks like text categorisation. Furthermore, Lambeq is designed in a modular manner so that users can swap components in and out of the model and have architectural design flexibility.

Lambeq minimises the entry barrier for practitioners and researchers interested in AI and human-machine interactions, which could be one of quantum technology’s most important applications. TKET presently has a user base of hundreds of thousands of people all over the world. Lambeq has the potential to become an essential toolbox for the quantum computing community looking to engage with QNLP applications, which are one of AI’s most lucrative sectors. QNLP will apply to the study of symbol sequences that originate in genomes and proteomics, according to a critical point that has lately become clear.

Natural language generation

Another critical component for realizing QNLP is Natural language generation (NLG). NLG uses artificial intelligence (AI) programming to produce written or spoken narratives from a data set. Some of the applications of NLG are OCR, Speech recognition, Machine translation and Chatbots. NLG process consists of five steps as follows:

Lexical analysis
Parsing
Semantic analysis
Discourse integration
Pragmatic analysis

Source: https://thenewstack.io/lambeq-a-toolkit-for-quantum-natural-language-processing/

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Getting ready for post Quantum Era with open Source Software

admin — Sat, 04 Dec 2021 17:37:30 +0000

QuantumGuru recent articles have been on applications of quantum computers in addressing real world use cases. For the last three decades, software has been at the forefront of technology development and its application and scalability for general masses. Hence, this article is about some software frameworks that we foresee may influence quantum computer programming.

Software for classical computers is highly matured, but the computing enhancement brought by quantum computing will require significant changes in the current software paradigm. Tech giants like Google, IBM, Microsoft and Amazon are investing billions of dollars to bring commercially viable quantum computers – hardware to start with. However, all of these are software companies and invest considerably in software to enable programming of quantum computers. New age startups like Cambridge quantum, Orange Quantum, Qblox etc. are working creatively to overcome similar barriers. For example. Cambridge quantum, a company that creates agnostic quantum software, has merged with Honeywell Quantum Solutions, a quantum hardware company that uses trapped-ions for quantum computing to create Quantinuum, a blend of Quantum Continuum.

Some open source software in development for the post quantum era are:

1. Quantify

The open-source software platform is for qubit calibration and characterization routines. Qblox provides a completely open-source software stack, called Quantify, to control experiments on Qblox Cluster and SPI hardware. Quantify is a python-based, high-level data acquisition platform focused on providing all the necessary tools for Quantum Computing experiments. It is built on top of QCoDeS, and is the successor of the extensively tested PyQED measurement environment. The simple software framework enables setting-up typical characterization experiments and advanced experimental procedures with ease-of-use.

2. lambeq

lambeq is the world’s first software toolkit for quantum natural language processing (QNLP). It is capable of converting sentences into a quantum circuit. It is designed to accelerate the development of practical, real-world QNLP applications such as automated dialogue, text mining, language translation, text-to-speech, language generation and bioinformatics. lambeq has been released on a fully open-sourced basis for the benefit of the world’s quantum computing community. lambeg ecosystem is rapidly growing and includes quantum computing researchers, developers and users. lambeq works seamlessly with Cambridge Quantum’s TKET, the world’s leading and fastest-growing quantum software development platform that is also open-source. This provides QNLP developers with access to the broadest possible range of quantum computers.

3. pyQuil

PyQuil is a Python library for quantum programming using Quil. Quil is a quantum instruction language developed by Rigetti Computing. PyQuil has the following three main functions:

Easily generates Quil programs from quantum gates and classical operations
Compiles and simulates Quil programs using the Quil Compiler (quilc) and the Quantum Virtual Machine (QVM)
Executes Quil programs on real quantum processors (QPUs) using Quantum Cloud Services (QCS)

4. Pennylane

Pennylane is a cross-platform Python library for differentiable programming of quantum computers. It aims to build rich and flexible hybrid quantum-classical models. It trains a quantum computer similar to that of a neural network. Pennylane connects to quantum hardware using standard neural networks frameworks such as PyTorch, TensorFlow, JAX, Keras or NumPy. It is fully device independent and executes the same quantum circuit on different quantum backends. Plugins are needed in order to access even more devices, including Strawberry Fields, Amazon Braket, IBM Q, Google Cirq, Rigetti Forest, Qulacs, Pasqal, Honeywell, and more