Go has long been considered as a testbed for artificial intelligence. By introducing certain quantum features, such as superposition and collapse of wavefunction, we experimentally demonstrate a quantum version of Go by using correlated photon pairs entangled in polarization degree of freedom. The total dimension of Hilbert space of the generated states grows exponentially as two players take turns to place the stones in time series. As nondeterministic and imperfect information games are more difficult to solve using nowadays technology, we excitedly find that the inherent randomness in quantum physics can bring the game nondeterministic trait, which does not exist in the classical counterpart. Some quantum resources, like coherence or entanglement, can also be encoded to represent the state of quantum stones. Adjusting the quantum resource may vary the average imperfect information (as comparison classical Go is a perfect information game) of a single game. We further verify its non-deterministic feature by showing the unpredictability of the time series data obtained from different classes of quantum state. Finally, by comparing quantum Go with a few typical games that are widely studied in artificial intelligence, we find that quantum Go can cover a wide range of game difficulties rather than a single point. Our results establish a paradigm of inventing new games with quantum-enabled difficulties by harnessing inherent quantum features and resources, and provide a versatile platform for the test of new algorithms to both classical and quantum machine learning.
Go has long been considered as a testbed for artificial intelligence. By introducing certain quantum features, such as superposition and collapse of wavefunction, we experimentally demonstrate a quantum version of Go by using correlated photon pairs entangled in polarization degree of freedom. The total dimension of Hilbert space of the generated states grows exponentially as two players take turns to place the stones in time series. As nondeterministic and imperfect information games are more difficult to solve using nowadays technology, we excitedly find that the inherent randomness in quantum physics can bring the game nondeterministic trait, which does not exist in the classical counterpart. Some quantum resources, like coherence or entanglement, can also be encoded to represent the state of quantum stones. Adjusting the quantum resource may vary the average imperfect information (as comparison classical Go is a perfect information game) of a single game. We further verify its non-deterministic feature by showing the unpredictability of the time series data obtained from different classes of quantum state. Finally, by comparing quantum Go with a few typical games that are widely studied in artificial intelligence, we find that quantum Go can cover a wide range of game difficulties rather than a single point. Our results establish a paradigm of inventing new games with quantum-enabled difficulties by harnessing inherent quantum features and resources, and provide a versatile platform for the test of new algorithms to both classical and quantum machine learning.
Abstract Go has long been considered as a testbed for artificial intelligence. By introducing certain quantum features, such as superposition and collapse of wavefunction, we experimentally demonstrate a quantum version of Go by using correlated photon pairs entangled in polarization degree of freedom. The total dimension of Hilbert space of the generated states grows exponentially as two players take turns to place the stones in time series. As nondeterministic and imperfect information games are more difficult to solve using nowadays technology, we excitedly find that the inherent randomness in quantum physics can bring the game nondeterministic trait, which does not exist in the classical counterpart. Some quantum resources, like coherence or entanglement, can also be encoded to represent the state of quantum stones. Adjusting the quantum resource may vary the average imperfect information (as comparison classical Go is a perfect information game) of a single game. We further verify its non-deterministic feature by showing the unpredictability of the time series data obtained from different classes of quantum state. Finally, by comparing quantum Go with a few typical games that are widely studied in artificial intelligence, we find that quantum Go can cover a wide range of game difficulties rather than a single point. Our results establish a paradigm of inventing new games with quantum-enabled difficulties by harnessing inherent quantum features and resources, and provide a versatile platform for the test of new algorithms to both classical and quantum machine learning.
The simulation of quantum matter with classical hardware plays a central role in the discovery and development of quantum many-body systems, with far-reaching implications in condensed matter physics and quantum technologies. In general, efficient and sophisticated algorithms are required to overcome the severe challenge posed by the exponential scaling of the Hilbert space of quantum systems. In contrast, hardware built with quantum bits of information are inherently capable of efficiently finding solutions of quantum many-body problems. While a universal and scalable quantum computer is still beyond the horizon, recent advances in qubit manufacturing and coherent control of synthetic quantum matter are leading to a new generation of intermediate scale quantum hardware. \n \nThe complexity underlying quantum many-body systems closely resembles the one encountered in many problems in the world of information and technology. In both contexts, the complexity stems from a large number of interacting degrees of freedom. A powerful strategy in the latter scenario is machine learning, a subfield of artificial intelligence where large amounts of data are used to extract relevant features and patterns. In particular, artificial neural networks have been demonstrated to be capable of discovering low-dimensional representations of complex objects from high-dimensional dataset, leading to the profound technological revolution we all witness in our daily life. \n \nIn this Thesis, we envision a new paradigm for scientific discovery in quantum physics. On the one hand, we have the essentially unlimited data generated with the increasing amount of highly controllable quantum hardware. On the other hand, we have a set of powerful algorithms that efficiently capture non-trivial correlations from high-dimensional data. Therefore, we fully embrace this data-driven approach to quantum mechanics, and anticipate new exciting possibilities in the field of quantum many-body physics and quantum information science. We revive a powerful stochastic neural network called a restricted Boltzmann machine, which slowly moved out of fashion after playing a central role in the machine learning revolution of the early 2010s. We introduce a neural-network representation of quantum states based on this generative model. We propose a set of algorithms to reconstruct unknown quantum states from measurement data and numerically demonstrate their potential, with important implications for current experiments. These include the reconstruction of experimentally inaccessible properties, such as entanglement, and diagnostics to determine sources of noise. Furthermore, we introduce a machine learning framework for quantum error correction, where a neural network learns the best decoding strategy directly from data. We expect that the full integration between quantum hardware and artificial intelligence will become the gold standard, and will drive the world into the era of fault-tolerant quantum computing and large-scale quantum simulations.
We introduce a new board game based on the ancient Chinese game of Go (Weiqi, Igo, Baduk). The key difference from the original game is that players no longer alternatively play single stones on the board but instead they take turns placing pairs of entangled go stones. A phenomenon of quantum-like collapse occurs when one of the stones in an entangled pair is directly in contact with at least one other stone. The player to whom the entangled pair belongs must then remove one of the stones in the pair from the board. The aim of the game is still to surround more territory than the opponent and as the number of stones increases, all the entangled pairs of stones eventually reduce to single stones. Quantum Go provides an interesting and tangible illustration of quantum concepts such as superposition, entanglement and collapse.
This rapid growth in data and the increased complexity of machine learning models have made it almost impossible to continue pushing forward with classical computing, thereby creating a pressing need for new computational paradigms. The following paper will examine how quantum machine learning answers the above questions by applying the principles of quantum mechanics to the art of artificial intelligence (AI) and big data analytics. We examine some of the current Quantum Machine Learning (QML) algorithms and their applications based on a framework that employs quantum-enhanced feature spaces, variational quantum circuits, and quantum-inspired classical algorithms. Our methodology combines theoretical analysis with empirical evaluation of these methods over standard benchmark datasets, using both quantum simulators and real quantum hardware. Key findings include a set of advantages carried by QML in specific fields, such as dimensionality reduction, optimization problems, and some clustering tasks. These are the quadratic speedup for support vector machines and the exponential advantage for principal component analysis. Our results also show that quantum error correction isn’t perfect and that loading classical data into a quantum system can be hard. We conclude that, while QML holds much promise, it is, by all means, clearly still a way off from being considered a panacea. It is awaiting further breakthroughs in quantum hardware, error mitigation techniques, and algorithm design in order to fully realize its potential. This work thus presents a roadmap for future research and highlights where QML is best placed to make major impactful differences in artificial intelligence and big data analytics.
The field of Quantum Artificial Intelligence (Quantum AI) has emerged as a promising interdisciplinary research area where the computational power of quantum computing is merged with the learning and decision-making power of AI. The application of quantum principles such as superposition, entanglement and quantum interference to machine learning algorithms has created new possibilities for solving problems that may be difficult to solve by classical machines. In the last few years, advances in Noisy Intermediate-Scale Quantum (NISQ) devices have helped to speed advances in Quantum Machine Learning (QML) algorithms, Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Quantum Convolutional Neural Networks (QCNNs) and hybrid learning algorithms. This review provides a comprehensive overview of the research in the field of Quantum AI from 2020 to 2026, covering the key concepts, novel architectures, algorithmic advancements, and practical applications. A critical assessment of the application of Quantum AI in fields such as healthcare, finance, cybersecurity, scientific computing and optimization problems is provided, and the benefits of quantum-enhanced learning models are discussed. In addition, common difficulties such as quantum hardware limitations, sensitivity to noise, encoding of data, scalability constraints and a lack of speed in training variational quantum circuits are discussed. The review also covers new avenues of research, including trustworthy quantum machine learning, explainable Quantum AI, quantum federated learning, and fault-tolerant quantum computing. The results suggest while practical large-scale deployment is currently limited by existing hardware capabilities, hybrid quantum-classical systems have great potential to rapidly move intelligent data processing and develop next-generation AI systems.
Artificial Intelligence (AI), a discipline with decades of history, is living its golden era due to striking developments that solve problems that were unthinkable just a few years ago, like generative models of text, images and video.The broad range of AI applications has also arrived to Physics, providing solutions to bottleneck situations, e.g., numerical methods that could not solve certain problems or took an extremely long time, optimization of quantum experimentation, or qubit control.Besides, Quantum Computing has become extremely popular for speeding up AI calculations, especially in the case of data-driven AI, i.e., Machine Learning (ML).The term Quantum ML is already known and deals with learning in quantum computers or quantum annealers, quantum versions of classical ML models and different learning approaches for quantum measurement and control.Quantum AI (QAI) tries to take a step forward in order to come up with disruptive concepts, such as, human-quantum-computer interfaces, sentiment analysis in quantum computers or explainability of quantum computing calculations, to name a few.This special session includes five high-quality papers on relevant topics, like quantum reinforcement learning, parallelization of quantum calculations, quantum feature selection and quantum vector quantization, thus capturing the richness and variability of approaches within QAI.