Due to the beyond-classical capability of quantum computing, quantum machine learning is applied independently or embedded in classical models for decision making, especially in the field of finance. Fairness and other ethical issues are often one of the main concerns in decision making. In this work, we define a formal framework for the fairness verification and analysis of quantum machine learning decision models, where we adopt one of the most popular notions of fairness in the literature based on the intuition -- any two similar individuals must be treated similarly and are thus unbiased. We show that quantum noise can improve fairness and develop an algorithm to check whether a (noisy) quantum machine learning model is fair. In particular, this algorithm can find bias kernels of quantum data (encoding individuals) during checking. These bias kernels generate infinitely many bias pairs for investigating the unfairness of the model. Our algorithm is designed based on a highly efficient data structure -- Tensor Networks -- and implemented on Google's TensorFlow Quantum. The utility and effectiveness of our algorithm are confirmed by the experimental results, including income prediction and credit scoring on real-world data, for a class of random (noisy) quantum decision models with 27 qubits ($2^{27}$-dimensional state space) tripling ($2^{18}$ times more than) that of the state-of-the-art algorithms for verifying quantum machine learning models.

With the attainment of Quantum Supremacy, the Quantum calculating at Google has smashed an exciting milestone. In the come round of this illustration, Google is now considering creating and executing new algorithms for running on its Quantum Computer, which has real-world applications. To deliver users the necessary tools they require to program and pretend to be a quantum computer, Google is functioning on Cirq. Here, Cirq is aimed at quantum computing researchers concerned with running and projecting algorithms that influence prevailing quantum computers. Tensorflow Quantum offers operators the tools they want to interweave quantum algorithms and logic intended in Cirq with the authoritative and performant ML tools from Tensorflow.

Quantum computing has grabbed the imagination of computer scientists as one possible future of the discipline after we've reached the limits of digital binary computers. Thanks to its capability to hold many different possible outcomes in the "quantum state," quantum computing could potentially deliver a big computational upgrade for machine learning and AI problems. However, there are still a lot of unanswered questions around quantum computing, and it's unclear if the devices will help with the building wave of investment in enterprise AI. We've done quite well with the line of binary computers that first appeared in the 1950s and have evolved into the basis of today's multi-trillion-dollar IT sector. With just two bits and three Boolean algebraic operators, we created tremendous data-crunching machines that have automated many manual tasks and had a large impact on the world around us.

Although topics like qubit scalability, error correction and the race to quantum supremacy highlight the current state of quantum computing, recently there has been a lot of discussion around use cases and applications of the available NISQ systems. The technology has reached a point of maturity where early adopters are looking into the possibility of squeezing out some quantum advantage, now or in the very near future. One direction that has been getting a lot of attention is quantum-machine learning or QML. This is not very surprising considering the massive strides made in machine learning over just the past few years. From breakthroughs in predicting protein folding, to deep fakes and the famous GTP3, these systems are sophisticated, impressively versatile and expected to, or already have, revolutionize many fields and industries.

TensorFlow Quantum (TFQ) is a quantum machine learning library for rapid prototyping of hybrid quantum-classical ML models. Research in quantum algorithms and applications can leverage Google's quantum computing frameworks, all from within TensorFlow. TensorFlow Quantum focuses on quantum data and building hybrid quantum-classical models. It integrates quantum computing algorithms and logic designed in Cirq, and provides quantum computing primitives compatible with existing TensorFlow APIs, along with high-performance quantum circuit simulators. Start with the overview, then run the notebook tutorials.

Quantum mechanics was once a very controversial theory. Early detractors such as Albert Einstein famously said of quantum mechanics that "God does not play dice" (referring to the probabilistic nature of quantum measurements), to which Niels Bohr replied, "Einstein, stop telling God what to do". However, all agreed that, to quote John Wheeler "If you are not completely confused by quantum mechanics, you do not understand it". As our understanding of quantum mechanics has grown, not only has it led to numerous important physical discoveries but it also resulted in the field of quantum computing. Quantum computing is a different paradigm of computing from classical computing.

Posted by Andrea Skolik, Volkswagen AG and Leiden University In early March, Google released TensorFlow Quantum (TFQ) together with the University of Waterloo and Volkswagen AG. TensorFlow Quantum is a software framework for quantum machine learning (QML) which allows researchers to jointly use functionality from Cirq and TensorFlow.

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.

Quantum isn't the next big thing in advanced computing so much as a futuristic approach that could potentially be the biggest thing of all. Considering the theoretical possibility of quantum fabrics that enable seemingly magical, astronomically parallel, unbreakably encrypted, and faster-than-light subatomic computations, this could be the omega architecture in the evolution of AI (artificial intelligence). No one doubts that the IT industry is making impressive progress in developing and commercializing quantum technologies. But this mania is also shaping up to be the hype that ends all hype. It will take time for quantum technology to prove itself a worthy successor to computing's traditional von Neumann architecture.

Google's quantum supremacy experiment used 53 noisy qubits to demonstrate it could perform a calculation in 200 seconds on a quantum computer that would take 10,000 years on the largest classical computer using existing algorithms. This marks the beginning of the Noisy Intermediate-Scale Quantum (NISQ) computing era. In the coming years, quantum devices with tens-to-hundreds of noisy qubits are expected to become a reality. Quantum computing relies on properties of quantum mechanics to compute problems that would be out of reach for classical computers. A quantum computer uses qubits.