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Superconducting Qubits
Published in David A. Cardwell, David C. Larbalestier, Aleksander I. Braginski, Handbook of Superconductivity, 2022
Britton Plourde, Frank K. Wilhelm-Mauch
A central milestone within NISQ is to run a circuit that cannot be reasonably simulated on the largest existing classical supercomputer. The goal post is set by the available memory, given that an N-qubit quantum state is represented by 2N complex numbers and is currently set around 50 qubits. Next to the qubit number, the quantum algorithm must perform a task that is complex enough so that classical computing offers no shortcut avoiding this type of memory-intensive optimization. Furthermore, the error rate, specifically that due to decoherence, needs to be low enough to be sufficient for the quantum task. Several measures have been brought forward for how this result can be certified (Boixo et al., 2018).
Overview of pmBLOX
Published in J. Chris White, Robert M. Sholtes, The Dynamic Progress Method, 2016
J. Chris White, Robert M. Sholtes
Once you have downloaded pmBLOX, double-click on the installer to copy the program to your computer. (Note: pmBLOX requires a Microsoft Windows operating system to run.) After the installation process completes, you can run pmBLOX by selecting the program from your Start menu or desktop shortcut or by double-clicking on the application file (located in your C:\Program Files\pmBLOX directory). Once the program loads, you will see an empty project view (see Figure 7.1).
Solving Sets of Algebraic Equations
Published in David E. Clough, Steven C. Chapra, Introduction to Engineering and Scientific Computing with Python, 2023
David E. Clough, Steven C. Chapra
where the 2 × 2 determinants are called minors. There is a heuristic (in common parlance, “a rule of thumb”) that many have learned that provides a shortcut for computing the determinants of these smaller matrices. This is shown in Figure 8.5. Note the patterns in the figure. Solid arrows indicate positive contributions and dashed arrows negative.
Extracting Building Footprints from High-resolution Aerial Imagery Using Refined Cross AttentionNet
Published in IETE Technical Review, 2021
We use the pre-trained layers of ResNet-50 [25] along with efficient channel attention (ECA) modules [26] as our encoder. The pre-trained layers assist the proposed model to enhance its generalization ability by learning the simple visual features to complex spectral features. The ResNet has the residual blocks with identity mapping and shortcut connections that handle the vanishing gradient efficiently by propagating the information smoothly. The ECA-Net [26] avoids dimensionality reduction by replacing the fully connected layers with 1-D convolution. As a result, it improves the performance as the parameters and FLOPs are minimized. The ECA-Net captures the local cross-channel relationships by considering every channel along with its k neighbors. The 1-D convolution of kernel size k is performed, where k represents the overall coverage of local channel relations by how many neighbors. So, this indicates how many neighboring pixels involve during the attention process for one channel. For selecting the value of k automatically, an adaptive selection of kernel size is performed by using a non-linear mapping function (k = ψ. (C)). This function shows that the coverage of local relationships is proportional to channel dimension C. Figure 1 shows the structure of both blocks used in the encoder part.
Gene Extraction of Leizhou Kiln Porcelain Patterns Based on Safety Internet of Things and Its Application in Modern Design
Published in IETE Journal of Research, 2021
At the architecture level of the Internet of Things, the type and amount of information transmitted from the perception layer to the application layer are gradually increasing, and the amount of data that must be processed and analyzed is doubling [10]. How to effectively mine, classify and apply a large amount of information is a difficult problem for the Internet of Things [11]. Data analysis and processing functions are the keys to the effective application of the Internet of Things, and the emergence of cloud computing has realized these possibilities [12]. Cloud computing can process tens of millions or billions of information in a few seconds, providing a shortcut for processing a large amount of information collected from the Internet of Things system [13].
Application of Internet of Things and Block-chain Technology in Improving Supply Chain Financial Risk Management System
Published in IETE Journal of Research, 2022
At the architecture level of the Internet of Things, the type and amount of information transmitted from the perception layer to the application layer are gradually increasing, and the amount of data that must be processed and analyzed is doubling. How to effectively mine, classify and apply a large amount of information is a difficult problem for the Internet of Things [25]. Data analysis and processing functions are the key to effective applications of the Internet of Things, and the emergence of cloud computing has realized these possibilities. Cloud computing can process tens of millions or billions of information in a few seconds, which provides a shortcut for processing a large amount of information collected from IoT systems [26].