We initiate a study to overview and compare quantum software development kits (QSDKs) in terms of their usability for introductory level quantum education. In this work, we focus on Qiskit, ProjectQ, ...Cirq, and Forest. For comparison, we define six tasks based on QWorld's introductory tutorial called Bronze. We implement each task on these QSDKs. Besides, we check how easy it is to install them. According to our results, not every QSDK comes as a stand-alone Python package. This may create certain installation and execution problems. Visualization of quantum circuits may be poor or fail in some case. For the rest of tasks, all QSDKs are sufficient to work with.
In recent years, there has been a growing interest in leveraging the unique properties of quantum computing to develop novel machine learning algorithms and architectures. This research paper ...presents an investigation of quantum convolutional neural networks (QCNNs), which leverage the unique properties of quantum computing to potentially improve the accuracy and efficiency of image classification tasks. Specifically, the paper explores three different QCNN architectures, including a pure quantum-based QCNN, a hybrid QCNN with a single quantum convolution layer, and a hybrid convolutional architecture with multiple quantum filters. We tested the models on MNIST dataset and the results of the study demonstrate that hybrid architectures that combine quantum and classical processing are more effective than pure quantum-based architectures in image classification tasks. In particular, the third model, the Hybrid Convolution with Multiple Quantum Filters, achieved the highest test set accuracy of 92.7%. The use of multiple quantum filters in conjunction with a classical neural network resulted in enhanced accuracy and efficiency in image classification tasks, highlighting the potential of hybrid architectures for future applications in machine learning tasks.
As a classic quantum computing implementation, the Deustch-Jozsa (DJ) algorithm is taught in many courses pertaining to quantum information science and technology (QIST). We exploit the DJ framework ...as an educational testbed, illustrating fundamental qubit concepts while identifying associated algorithmic challenges. In this work, we present a self-contained exploration which may be beneficial in educating the future quantum workforce. Quantum Key Distribution (QKD), an improvement over the classical Public Key Infrastructure (PKI), allows two parties, Alice and Bob, to share a secret key by using the quantum physical properties. For QKD the DJ-packets, consisting of the input qubits and the target qubit for the DJ algorithm, carry the secret information between Alice and Bob. Previous research from Nagata and Nakamura discovered in 2015 that the DJ algorithm for QKD allows an attacker to successfully intercept and remain undetected. Improving upon the past research we increased the entropy of DJ-packets through: (i) size hopping (H), where the number of qubits in consecutive DJ-packets keeps on changing and (ii) reordering (R) the qubits within the DJ-packets. These concepts together illustrate the multiple scales where entropy may increase in a DJ algorithm to make for a more robust QKD framework, and therefore significantly decrease Eve's chance of success. The proof of concept of the new schemes is tested on Google's Cirq quantum simulator, and detailed python simulations show that attacker's interception success rate can be drastically reduced.
Modeling High Dimensional Signal Data (HDSD) is challenging due to statistical complexities and an imbalance of noise to signal data, which creates ground truth problems. Likewise, enabling ...recognition and detection analytics on the information-bearing portion of the noise-obscured HDSD is computationally cost-prohibitive due to the unprecedented size, speed, and scale of HDSD. Consequently, human experts often out-perform Deep Learning (DL) algorithms at HDSD recognition. This is problematic due to the spectrum of applications reliant on accurate HDSD detection. These include multi-sensor and biologic signal detection, virus outbreak surveillance, genetic association identification, image, audio, and sonar processing, source separation for speech identification, astrophysical source detection, financial time series, and satellite payload processing. To solve this problem and facilitate favorable DL outcomes, synthetic data possessing enough positive samples is needed to balance the HDSD dataset. Research reveals an entangled Quantum Generative Adversarial Network (QGAN) utilizing parameterized quantum circuits for the QGAN Generator (QGEN) and Discriminator (QDIS) exhibit an exponential advantage over a classical-GAN when generating highly accurate and cost-effective synthetic data. Using this method, an unsupervised TensorFlow-Quantum QGAN is prepared from Cirq Noisy Intermediate-Scale Quantum (NISQ) circuits. The QGEN and QDIS create new statistically consistent synthetic HDSD from real data; thereby solving the ground truth problem. Next, TensorFlow-GPU facilitates classical-DL to train the new synthetic HDSD dataset. Once validated, the TensorFlow model is exported to the production Vertica analytics tier for fast and accurate ML inference, where the HDSD is instantly identified and classified at ingest.
Provider: - Institution: - Data provided by Europeana Collections- Appartient à l’ensemble documentaire : PhoSpec1- All metadata published by Europeana are available free of restriction under the ...Creative Commons CC0 1.0 Universal Public Domain Dedication. However, Europeana requests that you actively acknowledge and give attribution to all metadata sources including Europeana
Provider: - Institution: - Data provided by Europeana Collections- All metadata published by Europeana are available free of restriction under the Creative Commons CC0 1.0 Universal Public Domain ...Dedication. However, Europeana requests that you actively acknowledge and give attribution to all metadata sources including Europeana
Provider: - Institution: KIK-IRPA, Brussels (Belgium) - Data provided by Europeana Collections- All metadata published by Europeana are available free of restriction under the Creative Commons CC0 ...1.0 Universal Public Domain Dedication. However, Europeana requests that you actively acknowledge and give attribution to all metadata sources including Europeana
Provider: - Institution: KIK-IRPA, Brussels (Belgium) - Data provided by Europeana Collections- All metadata published by Europeana are available free of restriction under the Creative Commons CC0 ...1.0 Universal Public Domain Dedication. However, Europeana requests that you actively acknowledge and give attribution to all metadata sources including Europeana
Provider: - Institution: - Data provided by Europeana Collections- All metadata published by Europeana are available free of restriction under the Creative Commons CC0 1.0 Universal Public Domain ...Dedication. However, Europeana requests that you actively acknowledge and give attribution to all metadata sources including Europeana
