Quantum Programming (Sommersemester 2021)

Dozent: Prof. Dr. Holger Giese (Systemanalyse und Modellierung) , Christian Medeiros Adriano (Systemanalyse und Modellierung) , Matthias Barkowsky (Systemanalyse und Modellierung) , Dr. Sven Schneider (Systemanalyse und Modellierung)

Allgemeine Information

• Semesterwochenstunden: 4
• ECTS: 6
• Benotet: Ja
• Einschreibefrist: 18.03.2021 - 09.04.2021
• Lehrform: Projekt / Seminar
• Belegungsart: Wahlpflichtmodul
• Lehrsprache: Englisch

Studiengänge, Modulgruppen & Module

Beschreibung

Motivation:

Recently, researchers in China were able to find solutions to the boson-sampling problem in 200 seconds [1]. The same problem would have taken  2.5 billion years to calculate in China’s TaihuLight supercomputer.  This is a quantum advantage of around 10¹⁴. This breakthrough came after a different group of researchers at Google achieved for the first time quantum supremacy, which consists of using a quantum computer to solve a meaningful problem faster than a traditional supercomputer [2].

Meanwhile, support for quantum programming has been growing in terms of platforms, software libraries, and algorithms. Cloud platforms from Amazon [3], IBM [4], and Microsoft [5] have been made available to the general programmer. Multiple software development libraries like Cirq [6], Qsit [7], and Pyquil-Forest [8] allow programmers to use high level languages like Python, Swift or Java to write quantum programs. Various algorithms [9] have been designed and implemented to benefit from quantum computing. Ultimately, processing-intensive domains like optimization and machine learning are also being redesigned to leverage on these new algorithms (see PennyLane [10] and Quantum TensorFlow [11]).

Hence, after many years of fundamental progress in theory and hardware, we are now in a position to learn how to engineer quantum-based systems by (1) integrating existing quantum algorithms, (2) studying their execution on simulators, and (3) even measuring the performance of some these algorithms in real quantum computers.

Goals:

In this course the students will learn how to model and integrate applications that rely on existing quantum algorithms from the various emerging fields of quantum graphs [12,13,14], quantum optimization [15], quantum reinforcement learning [16],  quantum neural networks [17], quantum recommender systems [18], and quantum machine learning [19] in general.

Literatur

[1] Ball, P.. , (2020). Physicists in China challenge Google's' quantum advantage', Nature.

[2] Arute, F., et al., (2019), Quantum supremacy using a programmable superconducting processor, Nature 574.7779: 505-510.

[3] Microsoft, https://azure.microsoft.com/en-us/services/quantum/

[4] Amazon, https://aws.amazon.com/braket/

[5] IBM, https://quantum-computing.ibm.com/

[6] Cirq at Google Quantum AI, https://quantumai.google/cirq 

[7] Qiskit at IBM, https://github.com/Qiskit/qiskit 

[8] Pyquil at Rigetti, https://github.com/rigetti/pyquil 

[9] Quantum Algorithm Zoo, https://quantumalgorithmzoo.org/

[10] PennyLane, https://pennylane.readthedocs.io/en/stable/

[11] Quantum TensorFlow, https://www.tensorflow.org/quantum

[12] Zhou, W. (2021), Review on Quantum Walk Algorithm. In Journal of Physics: Conference Series (Vol. 1748, No. 3, p. 032022). IOP Publishing.

[13] Venegas-Andraca, S. E. (2012), Quantum walks: a comprehensive review. Quantum Information Processing, 11(5), 1015-1106.

[14] Paparo, G. D., et al., (2013), Quantum google in a complex network. Scientific reports, 3(1), 1-16.

[15] Wiebe, et al. (2014), Quantum Algorithms for Nearest-Neighbor Methods for Supervised and Unsupervised Learning. Quantum Information & Computation. 15 (3)

[16]  Crawford, et al., (2018), Reinforcement Learning Using Quantum Boltzmann Machines.

[17] Beer, K,, et al., (2020), Training deep quantum neural networks, Nature communications 11.1: 1-6.

[18] Kerenidis, I., Prakash, V., (2016), Quantum Recommender Systems, Arxiv.

[19]  Wittek, P., (2018), Quantum Machine Learning, Elsevier.

Lern- und Lehrformen

The course is a project seminar, which has an introductory phase comprising initial short lectures. After that, the students will work in groups on jointly identified experiments applying specific solutions to given problems and finally prepare a presentation and write a report about their findings concerning the experiments.

There will be an introductory phase to present basic concepts for the theme including the necessary foundations.

Leistungserfassung

We will grade the group's experiments (60%), reports (30%), and presentations (10%). Participation in the project seminar during meetings and other groups' presentations in the form of questions and feedback will also be required.

Termine

After the introductory phase with few initial short lectures, we will identify the group topics and then there will be regular individual feedback rounds of the groups with their supervisors. In addition, there will also be regular meetings during the semester for the whole project seminar to discuss the progress of all groups and open questions in general.

The introductory meeting will be held online on Monday 19.04 at 15:15.

Regular Meetings: Monday 15:15, Wednesday 11:00

Please email christian.adriano(at)hpi.de to obtain the Zoom credentials. If you are interested in the project seminar but cannot attend the introduction meeting, please contact us. We will find an individual solution for you.

 

Announcement regarding the coronavirus regulations: 

Because of the Coronavirus situation and corresponding restriction outbreak, we will organize all meetings as online meetings by default. This especially applies to the first lecture. If all participants agree to and the current restrictions as well as seminar room availability allow it, further meetings may also take place at HPI.

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