If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
"History is called the mother of all subjects", said Marc Bloch. So, let's talk about how the famous Sudoku even came into existence. The story dates back to the late 19th Century and it originated from France. Le Siecle, a French daily published a 9x9 puzzle that required arithmetic calculations to solve rather than logic and had double-digit numbers instead of 1-to-9 with similar game properties like Sudoku where the digits across rows, columns, and diagonals if added, will result in the same number. In 1979 a retired architect and puzzler named Howard Garns is believed to be the creator behind the modern Sudoku which was first published by Dell Magazines in the name of Number Place.
Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies. To optimize operating conditions of large scale semiconductor quantum devices, a large parameter space has to be explored. Here, the authors report a machine learning algorithm to navigate the entire parameter space of gate-defined quantum dot devices, showing about 180 times faster than a pure random search.
Like last year's 6-Series, the new-and-improved model offers sensational, quantum-dot-powered brightness and color, the best smart platform in the game, and a host of hardware features that go far beyond the standard-issue set you're likely to find in budget TVs. Considering how well the 6-Series apes the abilities of TVs that cost twice as much, it's a top value pick this year. There are some concessions to mention, however: The TV's design isn't the swankiest, its built-in speakers aren't that great, and it won't quite set you up for a fully-loaded, next-generation gaming experience. But honestly, these are nitpicks--the TCL 6-Series is a sensational value proposition for all but the most dedicated A/V fanatics. You're getting some of the best performance available in this particular price bracket as well as a host of thoughtful additions that'll keep you covered for years to come.
In this work, we attempt to solve the integer-weight knapsack problem using the D-Wave 2000Q adiabatic quantum computer. The knapsack problem is a well-known NP-complete problem in computer science, with applications in economics, business, finance, etc. We attempt to solve a number of small knapsack problems whose optimal solutions are known; we find that adiabatic quantum optimization fails to produce solutions corresponding to optimal filling of the knapsack in all problem instances. We compare results obtained on the quantum hardware to the classical simulated annealing algorithm and two solvers employing a hybrid branch-and-bound algorithm. The simulated annealing algorithm also fails to produce the optimal filling of the knapsack, though solutions obtained by simulated and quantum annealing are no more similar to each other than to the correct solution. We discuss potential causes for this observed failure of adiabatic quantum optimization.
Impact Biomedical, a wholly-owned subsidiary of SGX-listed Singapore eDevelopment, has announced the initiation of Quantum, a research program designed as a solution to the'patent cliff', the impending pharmaceutical threat. A patent cliff looms when patents for blockbuster drugs expire without being replaced with new drugs, and pharmaceutical companies experience an abrupt decrease in revenue, reducing overall pharmaceutical innovation globally, including crucial research into new methods to prevent and treat illnesses. Impact, through their strategic partner Global Research and Discovery Group Sciences (GRDG), has created a solution called Quantum, a new frontier in pharmaceutical development. Quantum is a new class of medicinal chemistry that uses advanced methods to boost efficacy and persistence of natural compounds and existing drugs while maintaining the safety profile of the original molecules. Instead of modifying functional groups, as is typically done presently in drug discovery, this new technique alters the behavior of molecules at the sub-molecular level.
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.
We propose a standardized methodology for developing and evaluating use cases for quantum computers and quantum inspired methods. This methodology consists of a standardized set of questions which should be asked to determine how and indeed if, near term quantum computing can play a role in a given application. Developing such a set of questions is important because it allows different use cases to be evaluated in a fair and objective way, rather than considering each case on an ad hoc basis which could lead to an evaluation which focuses on positives of a use case, while ignoring weaknesses. To demonstrate our methodology we apply it to a concrete use case, ambulance dispatch, and find that there are some ways in which near term quantum computing could be deployed sensibly, but also demonstrate some cases ways in which its use would not be advised. The purpose of this paper is to initiate a dialogue within the community of quantum computing scientists and potential end users on what questions should be asked when developing real world use cases.
We propose a new assumption in outlier detection: Normal data instances are commonly located in the area that there is hardly any fluctuation on data density, while outliers are often appeared in the area that there is violent fluctuation on data density. And based on this hypothesis, we apply a novel density-based approach to unsupervised outlier detection. This approach, called Quantum Clustering (QC), deals with unlabeled data processing and constructs a potential function to find the centroids of clusters and the outliers. The experiments show that the potential function could clearly find the hidden outliers in data points effectively. Besides, by using QC, we could find more subtle outliers by adjusting the parameter $\sigma$. Moreover, our approach is also evaluated on two datasets (Air Quality Detection and Darwin Correspondence Project) from two different research areas, and the results show the wide applicability of our method.
Quantum computation is an emerging technology that promises a wide range of possible use cases. This promise is primarily based on algorithms that are unlikely to be viable over the coming decade. For near-term applications, quantum software needs to be carefully tailored to the hardware available. In this paper, we begin to explore whether near-term quantum computers could provide tools that are useful in the creation and implementation of computer games. The procedural generation of geopolitical maps and their associated history is considered as a motivating example. This is performed by encoding a rudimentary decision making process for the nations within a quantum procedure that is well-suited to near-term devices. Given the novelty of quantum computing within the field of procedural generation, we also provide an introduction to the basic concepts involved.