Abstract--This paper explores the application of machine learning (ML) techniques in predicting the QPU processing time of quantum jobs. By leveraging ML algorithms, this study introduces predictive models that are designed to enhance operational efficiency in quantum computing systems. Using a dataset of about 150,000 jobs that follow the IBM Quantum schema, we employ ML methods based on Gradient-Boosting (LightGBM) to predict the QPU processing times, incorporating data preprocessing methods to improve model accuracy. The results demonstrate the effectiveness of ML in forecasting quantum jobs. This improvement can have implications on improving resource management and scheduling within quantum computing frameworks. This research not only highlights the potential of ML in refining quantum job predictions but also sets a foundation for integrating AI-driven tools in advanced quantum computing operations. The nature of quantum computing becomes increasingly relevant in the core of data-center operations due to a paradigm shift in computational processing, prioritization, and execution.

In recent years, emerging technologies have become prominent. Amongst them, quantum computing has a singular potential to change our world the most. Quantum computing has shown promising evidence to speed up heuristic computations in an incredible manner. Thus, applying quantum computing within complex solutions to address problems in pharmaceuticals and materials discovery, finance, autonomous vehicle applications, artificial intelligence, and other areas will have a significant impact on our lives. In particular, quantum computing has the potential to magnify the effects (both positives and negatives) of many AI applications.

Efficient quantum control is necessary for practical quantum computing implementations with current technologies. Conventional algorithms for determining optimal control parameters are computationally expensive, largely excluding them from use outside of the simulation. Existing hardware solutions structured as lookup tables are imprecise and costly. By designing a machine learning model to approximate the results of traditional tools, a more efficient method can be produced. Such a model can then be synthesized into a hardware accelerator for use in quantum systems. In this study, we demonstrate a machine learning algorithm for predicting optimal pulse parameters. This algorithm is lightweight enough to fit on a low-resource FPGA and perform inference with a latency of 175 ns and pipeline interval of 5 ns with $~>~$0.99 gate fidelity. In the long term, such an accelerator could be used near quantum computing hardware where traditional computers cannot operate, enabling quantum control at a reasonable cost at low latencies without incurring large data bandwidths outside of the cryogenic environment.

DURHAM – Researchers at Duke University and the University of Maryland have used the frequency of measurements on a quantum computer to get a glimpse into the quantum phenomena of phase changes – something analogous to water turning to steam. By measuring the number of operations that can be implemented on a quantum computing system without triggering the collapse of its quantum state, the researchers gained insight into how other systems -- both natural and computational -- meet their tipping points between phases. The results also provide guidance for computer scientists working to implement quantum error correction that will eventually enable quantum computers to achieve their full potential. The results appeared online June 3 in the journal Nature Physics. When heating water to a boil, the movement of molecules evolves as the temperature changes until it hits a critical point when it starts to turn to steam.

Researchers at Duke University and the University of Maryland have used the frequency of measurements on a quantum computer to get a glimpse into the quantum phenomena of phase changes--something analogous to water turning to steam. By measuring the number of operations that can be implemented on a quantum computing system without triggering the collapse of its quantum state, the researchers gained insight into how other systems--both natural and computational--meet their tipping points between phases. The results also provide guidance for computer scientists working to implement quantum error correction that will eventually enable quantum computers to achieve their full potential. The results appeared online June 3 in the journal Nature Physics. When heating water to a boil, the movement of molecules evolves as the temperature changes until it hits a critical point when it starts to turn to steam.

According to the new market research report "Quantum Computing Market with COVID-19 impact by Offering (Systems and Services), Deployment (On Premises and Cloud Based), Application, Technology, End-use Industry and Region - Global Forecast to 2026", published by MarketsandMarkets, the market is expected to grow from USD 472 million in 2021 to USD 1,765 million by 2026, at a CAGR of 30.2%. The early adoption of quantum computing in the banking and finance sector is expected to fuel the growth of the market globally. Other key factors contributing to the growth of the quantum computing market include rising investments by governments of different countries to carry out research and development activities related to quantum computing technology. Several companies are focusing on the adoption of QCaaS post-COVID-19. This, in turn, is expected to contribute to the growth of the quantum computing market.