Quantum computing's potential to revolutionize AI depends on growth of a developer ecosystem in which suitable tools, skills, and platforms are in abundance. These milestones are all still at least a few years in the future. What follows is an analysis of the quantum AI industry's maturity at the present time. Quantum AI executes ML (machine learning), DL (deep learning), and other data-driven AI algorithms reasonably well. As an approach, quantum AI has moved well beyond the proof-of-concept stage.
Google today announced the launch of TensorFlow Quantum, bringing together machine learning and quantum computing initiatives at the company. The framework can construct quantum datasets, prototype hybrid quantum and classic machine learning models, support quantum circuit simulators, and train discriminative and generative quantum models. Last fall, Google said it achieved quantum supremacy with the debut of a newly engineered solution. The release of TensorFlow Quantum follows the launch of Azure Quantum and progress by companies like Honeywell. Creating quantum models is made possible with standard Keras functions and by providing quantum circuit simulators and quantum computing primitives compatible with existing TensorFlow APIs, according to a Google AI blog.
Quantum computers have been quite the rage recently with different tech companies vying for the top spot when it comes to building the most powerful quantum machine. While IBM and Google were in the headlines last year for achieving quantum supremacy, other companies like the Industrial giant Honeywell have been quietly working on its own quantum tech. The company plans to make available its quantum machine to clients via the internet in the next three months. However, Honeywell's approach is a little different than the traditional quantum computers which use superconducting qubits to operate. Honeywell's quantum computer uses a different technology, called ion traps, which hold ions in place with electromagnetic fields.
Over the past decade, Artificial Intelligence (AI) has provided enormous new possibilities and opportunities, but also new demands and requirements for software systems. In particular, Machine Learning (ML) has proven useful in almost every vertical application domain. Although other sub-disciplines of AI, such as intelligent agents and Multi-Agent Systems (MAS) did not become promoted to the same extent, they still possess the potential to be integrated into the mainstream technology stacks and ecosystems, for example, due to the ongoing prevalence of the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS). However, in the decade ahead, an unprecedented paradigm shift from classical computing towards Quantum Computing (QC) is expected, with perhaps a quantum-classical hybrid model. We expect the Model-Driven Engineering (MDE) paradigm to be an enabler and a facilitator, when it comes to the quantum and the quantum-classical hybrid applications as it has already proven beneficial in the highly complex domains of IoT, smart CPS and AI with inherently heterogeneous hardware and software platforms, and APIs. This includes not only automated code generation, but also automated model checking and verification, as well as model analysis in the early design phases, and model-to-model transformations both at the design-time and at the runtime. In this paper, the vision is focused on MDE for Quantum AI, and a holistic approach integrating all of the above.
Google has revealed it is bringing together its machine learning and quantum computing initiatives with the launch of TensorFlow Quantum. The machine learning framework has the ability to construct quantum datasets, prototype hybrid quantum and classic machine learning models, support quantum circuit simulators and train both discriminative and generative quantum models. According to a Google AI blog, TensorFlow Quantum is able to create quantum models with standard Keras functions and by providing quantum circuit simulators and quantum computing primitives compatible with existing TensorFlow APIs. The release of TensorFlow Quantum comes after Microsoft's launch of Azure Quantum and the recent news that Honeywell is developing a quantum computer with a quantum volume of at least 64 which will be available in the next three months. In an abstract for a paper, authored by members of Alphabet's X unit, The Institute for Quantum Computing at the University of Waterloo, NASA's Quantum AI Lab, Volkswagen and Google Research, submitted to the preprint repository arXiv, the authors explain what they believe TensorFlow Quantum can achieve, saying: "We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage."