November 2023

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Efficient semi-supervised quantum anomaly detection using one-class support vector machines

Efficient semi-supervised quantum anomaly detection using one-class support vector machines

Abstract:

Quantum computing is an emerging technology that can potentially improve different tasks in machine learning. Combining the representational power of a classically hard quantum kernel and the one-class SVM, a noticeable improvement in average precision can be achieved compared to the classical version. However, the usual method of calculating these kernels comes with a quadratic time complexity in terms of data size. To address this issue, we try two different methods. The first consists of measuring the quantum kernel using randomized measurements, while the second one uses the variable subsampling ensemble method to achieve linear time complexity. Our experiments show that both of these methods reduce the training times by up to 95% and inference times by up to 25%. While the methods lead to lower performance, the average precision is slightly better than the classical RBF kernel.

Author:

Afrae Ahouzi

Advisors:

Claudia Linnhoff-Popien, Michael Kölle, Pascal Debus, Dr. Robert Müller

Student Thesis | Published November 2023 | Copyright © QAR-Lab
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Using Quantum Machine Learning to Predict Asset Prices in Financial Markets

Using Quantum Machine Learning to Predict Asset Prices in Financial Markets

Abstract:

In the financial world, a lot of effort is spent on predicting future asset prices. Gaining even a modest increase in forecasting capability can generate enormous profits. Some statistical models identify patterns, trends, and correlations in past prices, and apply those patterns to forecast future values. A more novel approach is the use of artificial intelligence to learn underlying trends in the data and predict future prices. As quantum computing matures, its potential applications in this task have also become increasingly more interesting. In this thesis, several different models of these various types are implemented: ARIMA, RBM, LSTM, and QDBM (Quantum Deep Boltzmann Machine). These models are trained on historical asset prices and used to predict future asset prices. The model predictions are then also used as the input for a simulated trading algorithm, which investigates the effectiveness of these predictions in the active trading of assets. The predictions are performed for ten different assets listed on the NYSE, NASDAQ, and XETRA, for the five-year period from 2018 to 2022. The assets were chosen from varying industrial sectors and with diverse price histories. Trading based on the model predictions was able to either match or outperform the classic buy-and-hold approach in nine out of the ten assets tested.

Author:

Maximilian Adler

Advisors:

Claudia Linnhoff-Popien, Jonas Stein, Jonas Nüßlein, Nico Kraus (Aqarios GmbH)

Student Thesis | Published November 2023 | Copyright © QAR-Lab
Direct Inquiries to this work to the Advisors


A Reinforcement Learning Environment for directed Quantum Circuit Synthesis

A Reinforcement Learning Environment for directed Quantum Circuit Synthesis

Abstract:

Fueled by recent advances in quantum computing technologies, the design of optimized quantum circuits including reliable quantum state preparation are topics gaining more and more importance. Common approaches often require a high amount of Know-How and manual calculation hampering implementation, especially if the involved circuits increase in qubit number and gate count. Hence, addressing the rise in possible gate-to- qubit combinations by utilizing machine learning techniques represents a promising step in the development of the field. The following study aims to provide a reinforcement learning environment enabling the training of agents on the directed quantum-circuit design for the preparation of quantum states. Thus, the trained agents are enabled to create quantum circuits facilitating the preparation of desired target states, which can be handed over as inputs. In the course of this, all generated quantum-circuits are built utilizing gates from the Clifford+T gate set only. Based on the implemented environment, we conducted experiments to investigate the relation between the depth of the reconstructed quantum circuits and the involved target state parameters. The explored parameter-space included the respective qubit number and circuit-depth used for the target initialization. By providing a division of the parameter-space into several difficulty regions and a collection of well-known states, we facilitated benchmarking of different reinforcement learning algorithms on the quantum-circuit synthesis problem. Specific findings of the study include the generation of PPO-algorithm-based agents, which outperform the random-baseline. Through the application of the trained agents on the benchmarking tests we show their ability to reliably design minimal quantum-circuits for a selection of 2-qubit Bell states.

Author:

Tom Schubert

Advisors:

Claudia Linnhoff-Popien, Michael Kölle, Philipp Altmann

Student Thesis | Published November 2023 | Copyright © QAR-Lab
Direct Inquiries to this work to the Advisors