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Quantum Advantage on Machine Learning Models


In recent years, quantum computers have become one of most attractive machines based on the principles of quantum mechanics. We make use of quantum superposition, quantum entanglement and so on in the calculation process. Expectations have increased enormously since the announcement of Quantum supremacy1, 2 in October 2019 and December 2020 by superconducting and photonic quantum computers. There are three types of quantum computing devices: quantum annealing3, quantum gates and optical Continuous-Variable2, 4. Quantum gate quantum computing devices include the superconducting type5, 6, trapped ion type7, 8, semiconductor quantum type9, 10, diamond NV centre type11 and Rydberg atom type12. The performance of quantum devices has been rapidly improving day by day in recent years because of severe development competition.

Ml Algorithm Used To Study Brain Connectivity


Researchers at the Indian Institute of Science (IISc) have developed a new graphic processing unit-based machine learning algorithm that may hold …

How And When Quantum Computers Will Improve Machine Learning? - AI Summary


Research in Quantum Machine Learning (QML) is a very active domain, and many small and noisy quantum computers are now available. More recently, a mini earthquake amplified by scientific media has cast doubt on the efficiency of Algorithm QML: the so-called "dequantization" papers [13] that introduced classical algorithms inspired from the quantum ones to obtain similar exponential speedups, in the field of QML at least. Quite recently Google performed a quantum circuit with 53 qubits [15], the first that could not be efficiently simulable by a classical computer. They are all based on the same idea of variational quantum circuits (VQC), inspired by classical machine learning. On the theoretical side, researchers hope that quantum superposition and entangling quantum gates would project data in a much bigger space (the Hilbert Space of n qubits has dimension 2 n) where some classically inaccessible correlations or separations can be done.

Sleep Staging Using End-to-End Deep Learning Model


Sleep staging using nocturnal sounds recorded from common mobile devices may allow daily at-home sleep tracking. The objective of this study is to introduce an end-to-end (sound-to-sleep stages) deep learning model for sound-based sleep staging designed to work with audio from microphone chips, which are essential in mobile devices such as modern smartphones. Patients and Methods: Two different audio datasets were used: audio data routinely recorded by a solitary microphone chip during polysomnography (PSG dataset, N 1154) and audio data recorded by a smartphone (smartphone dataset, N 327). The audio was converted into Mel spectrogram to detect latent temporal frequency patterns of breathing and body movement from ambient noise. The proposed neural network model learns to first extract features from each 30-second epoch and then analyze inter-epoch relationships of extracted features to finally classify the epochs into sleep stages. Results: Our model achieved 70% epoch-by-epoch agreement for 4-class (wake, light, deep, REM) sleep stage classification and robust performance across various signal-to-noise conditions. The model performance was not considerably affected by sleep apnea or periodic limb movement. Conclusion: The proposed end-to-end deep learning model shows potential of low-quality sounds recorded from microphone chips to be utilized for sleep staging. Future study using nocturnal sounds recorded from mobile devices at home environment may further confirm the use of mobile device recording as an at-home sleep tracker. Sound-based sleep staging can be a potential candidate for non-contact home sleep trackers. However, existing works were limited to audio measured with a contact manner (ie, tracheal sounds), with a limited distance (ie, 25 cm), or by a professional microphone. For convenience, a more practical way is to utilize easily obtainable audio, such as sounds recorded from commercial mobile devices.

The best gaming laptops under $1,000: Best overall, best battery life, and more


If you're jonesing for a powerful gaming experience but you're really strapped for cash, there are a number of budget options to consider. You can actually get some pretty decent CPU and GPU performance out of a budget gaming laptop. You just may need to dial back your graphics settings to hit that hallowed 60 frames per second mark in the latest cutting-edge games. If you're not sure where to begin, don't sweat it. We've done the hard work for you and curated a list of the best gaming laptops that fall under the $1,000 mark.

Best GPU for Deep Learning in 2022 (so far)


The A100 family (80GB/40GB with PCIe/SMX4 form factors) has a clear lead over the rest of the Ampere Cards. A6000 comes second, followed closely by 3090, A40, and A5000. There is a large gap between them and the lower tier 3080 and A4000, but their prices are more affordable. So, which GPUs to choose if you need an upgrade in early 2022 for Deep Learning? We feel there are two yes/no questions that help you choose between A100, A6000, and 3090.

D-Wave Delivers Prototype of Next-Generation Advantage2 Annealing Quantum Computer


D-Wave Systems Inc, a leader in quantum computing systems, software, and services, and the only company building both quantum annealing and gate-based quantum computers, announced that it is showcasing an experimental prototype of the next-generation Advantage2 annealing quantum computer in the Leap quantum cloud service. The quantum prototype is available for use today. The Advantage2 prototype has 500 qubits, woven together in the new Zephyr topology with 20-way inter-qubit connectivity and enabled by an innovative new qubit design. The Advantage2 prototype represents a version of the upcoming full-scale product with all core functionality available for testing. In early benchmarks, the reduced scale system demonstrates more compact embeddings; an increased energy scale, lowering error rates; and improved solution quality and increased probability of finding optimal solutions.

em Lightyear /em Is the Saddest em Toy Story /em Movie Yet


"Toy Story 3 is the saddest one. A young man--maybe a Pixar employee, maybe a local emcee or children's entertainer--had come out to work the crowd, tossing out trivia questions about the four previous movies in the Toy Story franchise and riffing with middling success on the replies. This kid's unsolicited comment was the hostility-free version of a heckle. It was a sweet, if retrospectively ironic, way to kick off the showing of one of the first Pixar films in years, and one of only a handful in the studio's 27-year history, to feel first and foremost like a piece of well-engineered corporate IP. That kid had it wrong: Though Toy Story 3 may draw forth more tears from audiences--I'll never forget my then-thirtysomething editor sobbing beside me through that final scene--it is Lightyear that, looked at from a broader perspective, is the saddest of the Toy Story movies. If the point of the original was that a child's love can rescue even the most mass-produced consumer product from meaninglessness, Lightyear is a commercially motivated attempt to reverse-engineer the piece of disposable mass culture that inspired that product in the first place. "In 1995," reads an opening title card, "Andy got a toy.

Theory suggests quantum computers should be exponentially faster on some learning tasks than classical machines


A team of researchers affiliated with multiple institutions in the U.S., including Google Quantum AI, and a colleague in Australia, has developed a theory suggesting that quantum computers should be exponentially faster on some learning tasks than classical machines. In their paper published in the journal Science, the group describes their theory and results when tested on Google's Sycamore quantum computer. Vedran Dunjko with Leiden University City has published a Perspective piece in the same journal issue outlining the idea behind combining quantum computing with machine learning to provide a new level of computer-based learning systems. Machine learning is a system by which computers trained with datasets make informed guesses about new data. And quantum computing involves using sub-atomic particles to represent qubits as a means for conducting applications many times faster than is possible with classical computers.