Finally, there are **machine learning** tasks, such as playing Go, that involve combinations of supervised and unsupervised **learning**, together with training sets that may be generated by the **machine** itself. Linear-algebra based **quantum machine learning** A wide variety of data analysis and **machine learning** protocols operate by

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The resource requirements of **quantum machine learning** algorithms are likely to be similarly difficult to quantify in practice. The analysis of their practical feasibility is a central subject of this review. As will be seen throughout the review, there are **quantum** algorithms for **machine learning** that exhibit **quantum** speedups 4–7. For example, the

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Data science; automation; **machine learning**; neuron networks; **quantum** gra-dient descent; hybrid classical-**quantum** algorithms. Abstract This is an overview of the emerging topic of **quantum machine learning** (QML). We rst explain **machine learning** and some primary types of **machine** networks. We then introduce QML, which has not been fully understood yet,

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input is a **quantum** state and the computing is classical, the **machine learning** routine is used to translate **quantum** information into classical information. If both the input and processing are **quantum**, this will be real **quantum machine learning**, however, only a few results in this direction are published yet. In most **quantum machine learning** re-

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Keywords – **Quantum Machine Learning**, Perceptron, Nearest Neighbours, Hamming Distance, Inner Product via Swap test Introduction Motivation **Machine Learning** is one of the fastest developing ﬁelds in computer science in today’s time. Problems in **machine learning** frequently require ma-nipulation of large number of high dimensional vec-tors.

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**Quantum machine learning** 14 September 2017 An international team of scientists presents a thorough review on **quantum machine learning**, its …

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[Submitted on 28 Nov 2016 ( v1 ), last revised 10 May 2018 (this version, v2)]** Quantum Machine Learning** Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, Seth Lloyd Fuelled by increasing computer power and algorithmic advances,** machine learning** techniques have become powerful tools for finding patterns in data.

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**quantum physics** M** achine learning** is a field of computer science that seeks to build computers capable of discovering meaningful information and making predictions about data. It is the core of artificial intelligence (AI) and has powered many aspects of modern technologies, from face recognition and natural language processing to

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**Quantum Artificial Intelligence** and** Machine Learning** Algorithms Machine Learning Supervised Learning 1. Support Vector Machines (FTQC) 2. Cluster assignment (FTQC) 3. Recommendation systems (FTQC) 4. Quantum deep learning (FTQC) 5. Quantum Boltzmann Machine training (QA) Unsupervised Learning 1. Clustering (cluster finding) 1a. Quantum PCA

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**Quantum** autoencoders have been proposed in recent years, in analogy with autoencoders of standard** Machine Learning,** as a way to efficiently employ the resources in a** quantum computation.** Two proposals were made initially [33], [34], in which a compression of the** quantum** resources needed for a specific** quantum computation** was suggested.

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**Quantum** Computation for** Machine Learning,** AI, and Optimization Zlatko K. Minev, Ph.D. IBM** Quantum** @ TJ Watson 2 Experimental Superconducting** Quantum Computing** Hardware Chemistry** Machine Learning** 3 Cloud-based** quantum computing** What does today’s hardware look like? Commercial cloud service Si fabrication junctions qubit readout

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How can **quantum** computing help **machine learning**? I Core idea: inputs to **learning** problem are often high-dimensional vectors of numbers (texts, images, ). These can be viewed as amplitudes in a **quantum** state. Required number of qubits is only logarithmic in dimension! Vector v 2Rd)log 2(d)-qubit state jvi= 1 kvk P d i=1 v ijii

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The algorithm at the center of the “**quantum machine learning**” mini-revolution is called HHL [9], after my colleagues Aram Harrow, Avinatan Hassidim, and Seth Lloyd, who invented it in 2008. Many of the subsequent **quantum learning** algorithms extend HHL or use it as a subroutine, so it’s important to understand HHL ﬁrst.

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**Quantum Machine Learning** bridges the gap between abstract developments in** quantum computing** and the applied research on** machine learning.** Paring down the complexity of the disciplines involved, it

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**Quantum machine learning** (QML) is a rapidly emerging field of immense industrial and scientific interest. It beautifully merges the ideas and applications of **machine learning** with the enigmatic principles of **quantum** physics. The meaning of **learning** can …

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**quantum** mechanical calculations. Anandkumar, whose research focuses on **machine learning**, helped design and optimize the graph neural network implementation. "Orbnet …

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**Quantum machine** learning17—hybridization of classical **machine learning** techniques with **quantum** computation—is emerging as a powerful approach allowing **quantum** speed-ups and improving classical **machine learning** algorithms18–22. Recently, Wiebe et al.23 have shown that **quantum** computing is capable of reducing the time required to train a

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The exact answer to your question depends on what you consider to be a good career. Quantum machine learning would be an interesting field to work in so if that’s important to you, check! It’s a new career path. It’s not even that, it’s a new idea for a career path.

While machine learning algorithms are used to compute immense quantities of data, quantum machine learning utilizes qubits and quantum operations or specialized quantum systems to improve computational speed and data storage done by algorithms in a program.

**Machine Learning Gets a Quantum Speedup**

- A Natural Marriage. Much of AI, and machine learning in particular, comes down to automating, and improving on, tedious tasks.
- Kernel Trickery. Temme speaks from experience. ...
- Training a Quantum Learner. ...
- Quantum Leaps Ahead. ...

You can tackle basic, nonrelativistic QM at an introductory level with just the following prerequisites: Linear algebra (mainly vector algebra and matrix algebra, and especially including eigenvectors and eigenvalues, which are absolutely central to QM)