mhjensen
246 lines
12 KiB
HTML
246 lines
12 KiB
HTML
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<title>Summary of course</title>
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'sections': [('What? Me worry? No final exam in this course!',
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2,
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('What did I learn in school this year?', 2, None, '___sec1'),
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("What's the future like?", 2, None, '___sec16'),
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('Bayesian Machine Learning', 2, None, '___sec17'),
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('Reinforcement Learning', 2, None, '___sec18'),
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('Distributed machine learning', 2, None, '___sec22'),
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('The Challenges Facing Machine Learning', 2, None, '___sec24'),
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('Explainable machine learning', 2, None, '___sec25'),
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('Quantum machine learning', 2, None, '___sec26'),
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('Quantum machine learning algorithms based on linear algebra',
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('Quantum reinforcement learning', 2, None, '___sec28'),
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('The last words?', 2, None, '___sec31'),
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('Best wishes to you all and thanks so much for your heroic '
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<a class="navbar-brand" href="summary-bs.html">Summary of course</a>
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<ul class="nav navbar-nav navbar-right">
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<li class="dropdown">
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
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<ul class="dropdown-menu">
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<!-- navigation toc: --> <li><a href="._summary-bs001.html#___sec0" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">What did I learn in school this year?</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs003.html#___sec2" style="font-size: 80%;">Topics we have covered this year</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Machine learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Perspective on Machine Learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Machine Learning Research</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Preparing Your Data</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Resampling</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Additional courses of interest</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">What's the future like?</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Bayesian Machine Learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs019.html#___sec18" style="font-size: 80%;">Reinforcement Learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs020.html#___sec19" style="font-size: 80%;">Transfer learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs021.html#___sec20" style="font-size: 80%;">Adversarial learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs022.html#___sec21" style="font-size: 80%;">Dual learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs023.html#___sec22" style="font-size: 80%;">Distributed machine learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs024.html#___sec23" style="font-size: 80%;">Meta learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs025.html#___sec24" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs026.html#___sec25" style="font-size: 80%;">Explainable machine learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs027.html#___sec26" style="font-size: 80%;">Quantum machine learning</a></li>
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<!-- navigation toc: --> <li><a href="#___sec27" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs029.html#___sec28" style="font-size: 80%;">Quantum reinforcement learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs030.html#___sec29" style="font-size: 80%;">Quantum deep learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs031.html#___sec30" style="font-size: 80%;">Social machine learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs032.html#___sec31" style="font-size: 80%;">The last words?</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs033.html#___sec32" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
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</ul>
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</li>
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<div class="container">
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<p> </p><p> </p><p> </p> <!-- add vertical space -->
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<a name="part0028"></a>
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<!-- !split -->
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<h2 id="___sec27" class="anchor">Quantum machine learning algorithms based on linear algebra </h2>
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<p>
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Many quantum machine learning algorithms are based on variants of
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quantum algorithms for solving linear equations, which can efficiently
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solve N-variable linear equations with complexity of O(log2 N) under
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certain conditions. The quantum matrix inversion algorithm can
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accelerate many machine learning methods, such as least square linear
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regression, least square version of support vector machine, Gaussian
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process, and more. The training of these algorithms can be simplified
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to solve linear equations. The key bottleneck of this type of quantum
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machine learning algorithms is data input—that is, how to initialize
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the quantum system with the entire data set. Although efficient
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data-input algorithms exist for certain situations, how to efficiently
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input data into a quantum system is as yet unknown for most cases.
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<p>
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<p>
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<li><a href="._summary-bs027.html">«</a></li>
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<li><a href="._summary-bs000.html">1</a></li>
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<li><a href="">...</a></li>
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<li><a href="._summary-bs020.html">21</a></li>
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<li><a href="._summary-bs021.html">22</a></li>
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<li><a href="._summary-bs022.html">23</a></li>
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<li><a href="._summary-bs023.html">24</a></li>
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<li><a href="._summary-bs027.html">28</a></li>
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<li><a href="._summary-bs029.html">30</a></li>
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<li><a href="._summary-bs031.html">32</a></li>
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<li><a href="._summary-bs032.html">33</a></li>
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<li><a href="._summary-bs033.html">34</a></li>
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<li><a href="._summary-bs029.html">»</a></li>
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