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We address four main areas in which graduate quantum mechanics education can be improved: course content, textbook, teaching methods, and assessment tools. We report on a three year longitudinal study at the Colorado School of Mines using…
The meteoric rise of artificial intelligence in recent years has seen machine learning methods become ubiquitous in modern science, technology, and industry. Concurrently, the emergence of programmable quantum computers, coupled with the…
In this era of incessant advancements in quantum computing, bridging the gap between quantum algorithms' hardware requisites and available devices has become crucial. A prime focus in this context is the Software and System Level support…
Quantum Kernels are projected to provide early-stage usefulness for quantum machine learning. However, highly sophisticated classical models are hard to surpass without losing interpretability, particularly when vast datasets can be…
At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and…
In this paper we study an error correcting protocol that specifically derives its error correcting properties from elementary units of coherence. The entire protocol from beginning to end is performed using non-coherence increasing…
The cybersecurity landscape is constantly evolving, driven by increased digitalization and new cybersecurity threats. Cybersecurity programs often fail to equip graduates with skills demanded by the workforce, particularly concerning recent…
As quantum computing (QC) continues to evolve in hardware and software, measuring progress in this complex and diverse field remains a challenge. To track progress, uncover bottlenecks, and evaluate community efforts, benchmarks play a…
Quantum technologies, including quantum computing, cryptography, and sensing, among others, are set to revolutionize sectors ranging from materials science to drug discovery. Despite their significant potential, the implications for public…
Quantum computing is an emerging field that promises to revolutionize various domains, such as simulation optimization, data processing, and more, by leveraging the principles of quantum mechanics. This paper outlines innovative pedagogical…
Efficient inference is critical for deploying deep learning models on edge AI devices. Low-bit quantization (e.g., 3- and 4-bit) with fixed-point arithmetic improves efficiency, while low-power memory technologies like analog nonvolatile…
Just as classical information technology rests on a foundation built of interconnected information-processing systems, quantum information technology (QIT) must do the same. A critical component of such systems is the interconnect, a device…
There are a number of emerging quantum technologies that have the potential to be disruptive in application areas such as computation, communication and sensing. In such a rapidly emerging field, there is a need for: transparency and…
Quantum computing threatens to undermine classical cryptography by breaking widely deployed encryption and signature schemes. This paper examines enterprise readiness for quantum-safe cybersecurity through three perspectives: (i) the…
Due to the superiority and noteworthy progress of Quantum Computing (QC) in a lot of applications such as cryptography, chemistry, Big data, machine learning, optimization, Internet of Things (IoT), Blockchain, communication, and many more.…
Quantum deep learning (QDL) explores the use of both quantum and quantum-inspired resources to determine when deep learning's core capabilities, such as expressivity, generalization, and scalability, can be enhanced based on specific…
Quantum communications have progressed significantly, moving from a theoretical concept to small-scale experiments to recent metropolitan-scale demonstrations. As the technology matures, it is expected to revolutionize quantum computing in…
With the increasing rise of publicly available high level quantum computing languages, the field of Quantum Computing has reached an important milestone of separation of software from hardware. Consequently, the study of Quantum Algorithms…
Quantum kernel methods are a promising branch of quantum machine learning, yet their effectiveness on diverse, high-dimensional, real-world data remains unverified. Current research has largely been limited to low-dimensional or synthetic…
Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT…