Quantum computing promises to improve our ability to perform some critical computational tasks in the future. Machine learning is changing the way we use computers in our present everyday life and in science. It is natural to seek connections between these two emerging approaches to computing, in the hope of reaping multiple benefits. The search for connecting links has just started, but we are already seeing a lot of potential in this wild, unexplored territory. We present here two new research articles: "Precise measurement of quantum observables with neural-network estimators," published in Physical Review Research, and "Fermionic neural-network states for ab-initio electronic structure," published in Nature Communications.

Could Quantum Analysis Of Black Holes And AI Reveal If Our Universe Is Just A Hologram? Researchers are attempting to use quantum computing and machine learning in order to gain a better understanding of holographic duality. The study is the first systematic survey for quantum computing and deep-learning as it pertains to matrix quantum mechanics, and lays the groundwork for addressing more complicated problems in the future. Quantum computing is not suited for every task that a computer can tackle. However, it is superior when tackling specific problems, such as encryption.

Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research. The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learning, where concepts of classical information technology meet the power of physics. Subsequent chapters follow a systematic deep dive into various quantum machine learning algorithms, quantum optimization, applications of advanced QML algorithms (quantum k-means, quantum k-medians, quantum neural networks, etc.), qubit state preparation for specific QML algorithms, inference, polynomial Hamiltonian simulation, and more, finishing with advanced and up-to-date research areas such as quantum walks, QML via Tensor Networks, and QBoost. Hands-on exercises from open source libraries regularly used today in industry and research are included, such as Qiskit, Rigetti's Forest, D-Wave's dOcean, Google's Cirq and brand new TensorFlow Quantum, and Xanadu's PennyLane, accompanied by guided implementation instructions.

Antonio J. Martinez, We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, Hamiltonian learning, and sampling thermal states.

Following D-Wave's announcement of Leap 2, a new version of its quantum cloud service for building and deploying quantum computing applications, VentureBeat had the opportunity to sit down with Murray Thom, D-Wave's VP of software and cloud services. We naturally talked about Leap 2, including the improvements the company hopes it will bring for businesses and developers. But we also discussed the business applications D-Wave has already seen to date. Quantum computing leverages qubits to perform computations that would be much more difficult, or simply not feasible, for a classical computer. Based in Burnaby, Canada, D-Wave was the first company to sell commercial quantum computers, which are built to use quantum annealing.

Preparing for a future driven by quantum computing took on new urgency recently when Google announced it had created a quantum computer capable of performing a computation in just a few minutes that would take classical computers thousands of years. The company hailed the breakthrough as the first realization of quantum supremacy -- the moment when quantum computers can solve problems that classical computers cannot. While some took issue with how Google structured its processing test and questioned the legitimacy of its claim, the announcement is at least a sign of progress in quantum and an indicator of what might be coming. The exponential increase in processing power that is theoretically possible with quantum computing has implications for drug discovery, cybersecurity and general AI to name a few areas. Quantum computing and machine learning will enable models that reflect complex conditions far better than today's models are capable of doing, Langione said.

Quantum computers could be the next big frontier in computing, with companies like Intel, IBM and Google racing to develop the technology. They have the potential to be far more powerful than traditional computers: where a normal bit of information is stored as either a 1 or a 0, a qubit takes advantage of the quirks of quantum physics to store data as 1, 0 or both at the same time. The exponential growth with each added qubit means that a system with 5 qubits is about the equivalent of 32 regular bits.

It is fall in Heidelberg and the leaves on the trees are already turning. This is the fifth year of the Heidelberg Laureate Forum (http://www.heidelberg-laureate-forum.org/) and it continues to be a highlight of the year for me and for about 250 others who participate. This year, computer science was heavily represented. There were fewer mathematicians, but they made up for smaller numbers by their extraordinary qualifications. A new cohort of laureates was added this year: recipients of the ACM Prize for Computing.a