Quantum computing is one of the promising tools to address complex problems which take more time to provide solutions. Quantum computing works on the qubits or quantum bits. It has been found that the number of qubits is directly proportional to the performance of the system. Health and agriculture are the two important sectors which deal with different complex multi-objective and many-objective problems. As the objectives are associated with multiple features and multiple computations, quantum computing works on those multiple features at a time to address specific objectives within an optimal time period. This chapter explains the basics of quantum computing and application of it both in both the health andagriculture sectors.
Agriculture
In the case of plants, instead of identifying the exact gene to change to, say, improve drought resistance, we crossbreed promising varieties and conduct yield trials — thousands upon thousands of them — to see if the new variety expresses an incremental improvement in yield and drought resistance traits.If you just measure a single plant, it may have done better because it had a more favorable location in the field, rather than improved genetics. The process as a whole usually takes about five to seven years.
Quantum computing opens the door to the possibility of skipping the crossbreeding process and directly identifying the genes responsible for important traits. CRISPR, an incredibly powerful genetic editing tool, could then be used to create a new variety with the desired traits that could proceed straight to the trials stage. All this would happen in a fraction of the time needed to bring elite genetics to market right now.
Quantum hardware’s depth of analysis would likely advance our understanding of the genetic code of plants (and humans) far beyond what we know today. At some point, it could even be possible to achieve not just ‘higher’ yield for a plant, but the absolute maximum yield possible for a given set of conditions.
Quantum and sustainability
Through the lens of four types of improvement — New catalysts, New materials, Fluid Dynamics and Logistics — the authors explain how meaningful reductions in the amount of Greenhouse gas created can be achieved.
The computation behind these improvements all relates to complex combinatorial problems for which quantum computers are perfectly designed to tackle.
Source: BCG - ‘A Quantum Advantage in Fighting Climate Change’, Jan 2020
Source: BCG — ‘A Quantum Advantage in Fighting Climate Change’, Jan 2020
The subject of climate change, and sustainability overall, is of course, huge, multifaceted and highly nuanced so quantum computing is clearly not a panacea. However, being able to explain that quantum calculations could relatively soon be playing a role in fighting climate change makes for a helpful response to the confused or skeptical questions.
Clustering Clustering is an unsupervised algorithm to discover groups of similar things, ideas, or people. Unlike supervised algorithms, we're not training clustering algorithms with examples of known labels. Instead, clustering tries to find structures within a training set where no point of the data is the label. K-Means Clustering K-Means is a clustering algorithm with one fundamental property: the number of clusters is defined in advance . In addition to K-Means, there are other types of clustering algorithms like Hierarchical Clustering, Affinity Propagation, or Spectral Clustering. How K-Means Works Suppose our goal is to find a few similar groups in a dataset like: K-Means begins with k randomly placed centroids. Centroids, as their name suggests, are the center points of the clusters . For example, here we're adding four random centroids: Then we assign each existing data point to its nearest centroid: After the assignment, we move the centroids to the average ...
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