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Enjoy today’s breakdown of news, research, events & jobs within quantum.

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IN TODAY’S ISSUE:

Tags: AEROSPACE APPLICATION ENERGY APPLICATION MACHINE LEARNING 

• Infleqtion’s quantum technologies have successfully been tested in quantum-based aerospace navigation systems in the UK resulting in the most un-spoofable and un-jammable tech available for commerical flights today

• A variational quantum computing-based framework that reduces energy consumption and carbon emissions in AI data centers

• Machine learning approach to quantum gate set tomography, reduces computational demands through a transformer neural network

• Plus, Alice & Bob tease an upcoming world record, IQM to build a quantum computer & chip fabrication unit in France, NVIDIA spreads CUDA-Q to the globe, and D-Wave renews partnership with USC.

 😵 Mondays, amirite?

BRIEF BYTES

NEWS FOR THOSE IN A HURRY

• Google and OpenAI have AI enthusiasts on the edge of their seats this week so it’s only fair that we get to have our fun, too — have you seen the Alice & Bob teaser post? After the recent news of extended bit-flip error resistance time on cat qubits, it seems like we’re in for an exciting week.

  

• At the Choose France summit, IQM Quantum Computers unveiled plans to construct a first-of-its-kind industrial-grade production site for quantum computers and chips in France. The facility will focus on the production of superconducting quantum processors and the initial phase of the quantum computing project will commence with pilot production at CEA-Leti in Grenoble.

• NVIDIA is continuing global quantum computing efforts with its CUDA-Q platform, which is now being adopted by supercomputing centers in Germany, Japan, and Poland. These centers are integrating quantum processing units into their NVIDIA-powered systems to advance research in areas such as AI, energy, and biology. The integration of CUDA-Q into quantum-classical hybrid architectures provides the optimal environment for advancements in scientific discovery and the acceleration of practical quantum computing applications.

• D-Wave and the University of Southern California have renewed their multiyear quantum computing partnership which means the USC Viterbi School of Engineering will continue to host a D-Wave Advantage quantum computer. This collaboration provides real-time access to annealing quantum computing through D-Wave's Leap quantum cloud service and aligns with USC's ambitious Frontiers of Computing initiative to advance technologies ethically.

TOP HEADLINES IN NEWS & RESEARCH

NEWS

Tags: AEROSPACE APPLICATION

BRIEF BYTE:  The UK is officially at the forefront of aerospace technology innovation after a successful round of testing was completed for advanced quantum-based navigation systems on commercial flights. This quantum technology cannot be jammed or spoofed and is a demonstration of added navigation resilience and accuracy.

WHAT: Infleqtion’s Tiqker optical atomic clock and ultra-cold-atom-based quantum system have successfully completed flight trials as part of an advanced quantum-based positioning, navigation, and timing system. This system was tested aboard QinetiQ's RJ100 Airborne Technology Demonstrator in the UK.

WHY: Backed by nearly £8 million ($100.5 million) in government funding, this project aligns with the UK's National Quantum Strategy. By 2030, its goal is to equip aircraft with quantum navigation systems and set a new standard for navigation technology. Today’s technology is expected to be included in a quantum inertial navigation system to change the way we do PNT — positioning, navigation, and timing.

Learn more about Tiqker below:

RESEARCH

Tags: ENERGY APPLICATION

OVERVIEW OF VARIATIONAL QUANTUM CIRCUIT LEARNING-ENABLED ROBUST OPTIMIZATION FOR AI DATA CENTER ENERGY CONTROL AND DECARBONIZATION

BRIEF BYTE: This study introduces a variational quantum computing-based robust optimization (VQC-RO) framework to manage energy in AI data centers by combining quantum and classical optimization to tackle energy consumption and carbon footprint challenges. The framework successfully reduces energy use by 9.8% and carbon emissions by 12.5% in AI data centers.

WHY: AI data centers have led to increased global electricity consumption and carbon emissions. Due to the growing demand for AI applications, energy management has become critical. The pitfalls of computational complexity and the non-linearity of energy management tasks can be addressed by integrating variational quantum circuits with classical optimization.

HOW: A digital twin of the AI data center was created to accurately simulate and visualize energy usage and optimize operations. The system's operations were modeled as a Markov decision process to capture the dynamics and interactions of different data center components. The variational quantum circuit robust optimization framework combined quantum and classical methods to optimize energy management. The framework's performance was tested through computational experiments across multiple AI data center locations in the U.S. by measuring reductions in power consumption and carbon emissions.

RESULTS: Overall, the VQC-RO framework demonstrated a reduction in energy consumption (up to 12.5%) and carbon emissions (9.8%) compared to traditional methods across locations and conditions. This approach not only increases the sustainability and efficiency of AI data centers but also demonstrates the practical utility of combined classical and quantum methods in creating solutions for real-world problems

PREPRINT

Tags: MACHINE LEARNING

OVERVIEW OF TRANSFORMER MODELS FOR QUANTUM GATE SET TOMOGRAPHY

BRIEF BYTE: This study proposes Ml4Qgst, a new approach to quantum gate set tomography that integrates machine learning with a transformer neural network to reduce computational complexity.

WHY: Quantum gate set tomography is used to characterize the operational capabilities and limitations of quantum processors. Traditional quantum tomography methods are computationally expensive due to the complex, nonlinear nature of quantum systems. Machine learning, particularly deep learning models like the transformer, can manage the computational complexity of QGST by handling the nonlinear, multi-model regression tasks involved.

HOW: The ML4Qgst model uses a transformer neural network to address the challenges of QGST and verify that the resulting quantum processes are completely positive and trace-preserving (CPTP). Techniques such as data grouping and curriculum learning are used to improve the model’s performance and efficiency in learning complex QGST tasks.

RESULTS: The ML4Qgst model performed QGST with high accuracy, matching while sometimes surpassing more traditional methods such as pyGSTi. The model effectively estimated error parameters and produced corresponding process matrices.

EVENTS

• Monday, May 13 | D-Wave conference call on Q1 2024 financial results

• Thursday, May 16 | Report on Quantum Computing in the Global South by the Centre for Quantum and Society

• Monday, May 20 | Stanford Responsible Quantum Technology Conference

• Now - May 31 | Register for Google/X-Prize Quantum Challenge

JOBS POSTED WITHIN LAST 24 HOURS

• CIA Quantum Computing Scientist | Washington, DC $61.9K - $164.1K

UNTIL TOMORROW.

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