3,972 research outputs found
Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problems
Quantum computing holds great potential for advancing the limitations of
machine learning algorithms to handle higher dimensions of data and reduce
overall training parameters in deep learning (DL) models. This study uses a
trainable variational quantum circuit (VQC) on a gate-based quantum computing
model to investigate the potential for quantum benefit in a model-free
reinforcement learning problem. Through a comprehensive investigation and
evaluation of the current model and capabilities of quantum computers, we
designed and trained a novel hybrid quantum neural network based on the latest
Qiskit and PyTorch framework. We compared its performance with a full-classical
CNN with and without an incorporated VQC. Our research provides insights into
the potential of deep quantum learning to solve a maze problem and,
potentially, other reinforcement learning problems. We conclude that
reinforcement learning problems can be practical with reasonable training
epochs. Moreover, a comparative study of full-classical and hybrid quantum
neural networks is discussed to understand these two approaches' performance,
advantages, and disadvantages to deep-Q learning problems, especially on
larger-scale maze problems larger than 4x4
Quantum Embedding with Transformer for High-dimensional Data
Quantum embedding with transformers is a novel and promising architecture for
quantum machine learning to deliver exceptional capability on near-term devices
or simulators. The research incorporated a vision transformer (ViT) to advance
quantum significantly embedding ability and results for a single qubit
classifier with around 3 percent in the median F1 score on the BirdCLEF-2021, a
challenging high-dimensional dataset. The study showcases and analyzes
empirical evidence that our transformer-based architecture is a highly
versatile and practical approach to modern quantum machine learning problems
Preparing random state for quantum financing with quantum walks
In recent years, there has been an emerging trend of combining two
innovations in computer science and physics to achieve better computation
capability. Exploring the potential of quantum computation to achieve highly
efficient performance in various tasks is a vital development in engineering
and a valuable question in sciences, as it has a significant potential to
provide exponential speedups for technologically complex problems that are
specifically advantageous to quantum computers. However, one key issue in
unleashing this potential is constructing an efficient approach to load
classical data into quantum states that can be executed by quantum computers or
quantum simulators on classical hardware. Therefore, the split-step quantum
walks (SSQW) algorithm was proposed to address this limitation. We facilitate
SSQW to design parameterized quantum circuits (PQC) that can generate
probability distributions and optimize the parameters to achieve the desired
distribution using a variational solver. A practical example of implementing
SSQW using Qiskit has been released as open-source software. Showing its
potential as a promising method for generating desired probability amplitude
distributions highlights the potential application of SSQW in option pricing
through quantum simulation.Comment: 11 pages, 7 figure
Attitude Control Calibration and Experiment Testbed to Characterize Attitude Determination and Control System Performance
This paper describes the design, development, and construction of an attitude control testbed to investigate the performance of ADCS. The Testbed consists of three instruments, an air-bearing platform, a Helmholtz cage, and an AM0 spectrum solar simulator. The Testbed in this research features the capability to measure the mass properties of the tested satellite. One of the motivations of this paper is to share the experience while building this highly automated Testbed. Finally, the procedure of the mass properties measurement will be well described in this paper
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