The Daily Qubit

📜 The publication anniversary of Newton's Principia 🧹 Plus a bit of quantum housekeeping -- frameworks for integrating QC successfully into industry and benchmarking

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

📜 It's the publication anniversary of Newton's Principia, as good a day as ever to read about some physics (quantum for us) 🧹 Plus a bit of quantum housekeeping — frameworks for integrating QC successfully into industry and benchmarking — dark matter, zero-sum energy, and some literally cool discoveries.

🗓️ UPCOMING

📰 NEWS QUICK BYTES

📜 The day classical physics made its debut — anniversary of the publication of Newton's Principia: July 5, 1687 was the day of publication for Sir Isaac Newton's Principia, forever changing our then understanding of gravity and motion, as well as laying the foundation for physics. Isaac Newton reminds us that excuses are just that — his discoveries in the fundamental principles of gravitation and fluxional calculus (you know, simple things) were made during a plague-induced hiatus. His collaboration with Edmund Halley in 1684 spurred the development of the Principia, which elaborates on the laws of motion, gravitational attraction, and the behavior of bodies in space and resisting mediums. Despite initial resistance, as all revolutionary scientific endeavors must endure, Newton's work was eventually recognized globally and has continued to profoundly influence science and mathematics for centuries.

☀️ The digital technologies actively being explored for a net-zero energy future: While talk of the urgent need for renewable energy is often doom and gloom, a look towards current research highlights the digital technologies that are actively being pursued in the energy sector. Quantum computing, alongside AI, has the potential to tackle complex optimization problems in energy systems in order to increase overall efficiency and reducing emissions. Specific applications include predictive maintenance, smart grid management, and renewable energy integration. However, progress does not happen in vaccuum — there is undeniably a need for continued research and collaboration to fully realize the potential of these technologies in achieving a sustainable, net-zero energy future.

🧊 Quantum computing is cool, but this device is even cooler: EPFL researchers have developed a device that efficiently manages heat at millikelvin temperatures. The device combines graphene and indium selenide and takes advantage of the Nernst effect to convert heat to voltage at extremely low temperatures. This addresses the challenge of heat dissipation in quantum systems, which is vital for maintaining qubit stability and efficiency. Operating efficiently at temperatures close to -273 Celsius, this device is not only a godsend for cooling systems related to quantum computing, but also a significant mileston for nanotechnology.

⛄️ Supercold quantum tech has joined the hunt for the ever-elusive dark matter: To detect dark matter, which theoretically constitutes most of the universe yet remains evasive to our continual probing, scientists at UK universities are creating two ultra-sensitive, supercold quantum detectors. These detectors must operate at nearly absolute zero to avoid noise interference. The project Quantum Enhanced Superfluid Technologies for Dark Matter and Cosmology will use superfluid helium-3 to detect weakly interacting particles, while the project Quantum Sensors for the Hidden Sector quantum amplifier will be used to detect the tiny signals from axions, hypothesized to be extremely light and abundant. If successful, this quantum technology would allow us to directly observe dark matter in a laboratory setting.

📈 Quantum computing as a tool for conducting market research: Quantum computing may prove effective in addressing complex problems in market research as it has the potential to solve optimization, machine learning, and simulation problems far beyond the capabilities of classical computers, providing significant advancements in data analysis and predictive analytics. A presented survey provides a bibliometric analysis of 209 publications, identifying key trends and future expectations for quantum computing in business applications. Experts foresee substantial growth in quantum computing's role in market research although the timeline for its full implementation remains uncertain.

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☕️ FRESHLY BREWED RESEARCH

From Problem to Solution: A general Pipeline to Solve Optimisation Problems on Quantum Hardware: A comprehensive development pipeline is proposed that integrates technical and project management perspectives into industry adoption of quantum computing for optimization. By organizing the process into five stages—use-case identification, solution draft, pre-processing, quantum hardware execution, and post-processing—the pipeline is intended to improve the success rate of quantum software projects. This structured approach not only clarifies the contributions and risks of quantum technology but also provides a tool for more effective communication and coordination among stakeholders. Breakdown here.

Quantum Curriculum Learning: Integrating curriculum learning with quantum machine learning, Q-CurL introduces simpler tasks first and increases complexity gradually, improving both training convergence and generalization. Task-based Q-CurL pre-trains on auxiliary tasks with a curriculum order based on data density ratios, while data-based Q-CurL dynamically adjusts data weights, focusing on easier samples early on and more difficult ones later. The implementation of Q-CurL may make QML more practical and efficient by reducing quantum resources needed and improving accuracy. Breakdown here.

Quantum Serverless Paradigm and Application Development using the QFaaS Framework: The Quantum Function-as-a-Service framework offers a serverless paradigm for quantum computing, so that developmers may focus on writing code without managing underlying infrastructure. By supporting multiple quantum software development kits and providing an adaptive backend selection policy, QFaaS serves portability and optimizes quantum function execution. This approach simplifies the deployment of quantum applications, making quantum computing more accessible and practical for real-world applications while addressing the complexities of current quantum hardware and software integration.

From the Physics Lab to the Computer Lab: Towards Flexible and Comprehensive DevOps for Quantum Computing: This paper discusses the development of a comprehensive DevOps environment for quantum computing as it transitions from laboratory experiments to production use. Key components include the establishment of hybrid HPCQC systems, automation of critical operations, a robust software stack for seamless quantum-classical integration, and extensive instrumentation and data analytics to ensure optimal performance. These efforts, demonstrated through projects like NordIQuEst and MQV, are intended to support scalable and efficient quantum computing environments.

HamPerf: A Hamiltonian-Oriented Approach to Quantum Benchmarking: HamPerf is a benchmarking framework for quantum computers focused on Hamiltonian-based performance evaluation. HamPerf uses the HamLib dataset, which includes various Hamiltonians from physics, chemistry, and combinatorial optimization, to assess quantum hardware and algorithm efficiency across different computational models. This approach is devised to bridge the gap between low-level hardware metrics and high-level computational utility, as well as provide a comprehensive toolset for evaluating quantum devices' practical performance and potential for quantum advantage.

UNTIL TOMORROW.

BREAKDOWN

From Problem to Solution: A general Pipeline to Solve Optimisation Problems on Quantum Hardware

🔍️ SIGNIFICANCE: 

  • The principles of quantum mechanics, which are the lifeblood of quantum computing, are notably abstract, lacking easy-to-compare parallels in our everyday reality. Since one of the most important factors for technology adoption is the ability to clearly and effectively communicate what the technology in question can contribute, this leads to difficulty in demonstrating both the contribution and risk of the technology to stakeholders. This research addresses a significant gap in the development of quantum software solutions by proposing a pipeline as a comprehensive guide for developing quantum applications.

  • Unlike previous methods that focus on technical aspects or specific stages of the software lifecycle, this pipeline integrates both technical and project management perspectives with the noble goal of increasing the likelihood of successful quantum software projects.

  • The article also emphasizes the need for risks and constraints to be thoroughly understood, and provides solutions tailored to specific problems rather than attempting to offer a general solution, which would be less effective.

🧪 METHODOLOGY: 

  • An extensive review of existing literature, interviews with experts, and the authors' expertise were used to identify core patterns in quantum software development.

  • The resulting pipeline is organized into five stages: use-case identification, solution draft, pre-processing, quantum hardware execution, and post-processing. Each stage also incorporates activities designed to address specific tasks within the development process.

  • The pipeline also includes two review points to assess the project's value and adaptability to ensure that resources are invested for a demonstrable reason. Other notable inclusions in this pipeline include the emphasis on iterative feedback loops, the inclusion of project management elements, and the adaptability to various quantum algorithms and hardware configurations.

📊 OUTCOMES & OUTLOOK: 

  • As seen in the previous adoption of conventional computation, a structured, well-defined development process can significantly improve the quality and success rates of quantum software projects. The proposed activities cover critical aspects such as data preparation, algorithm design, hardware selection, error mitigation, and post-processing.

  • Since the pipeline may streamline the development of practical quantum applications, reduce project risks, and facilitate better communication among stakeholders, this research is invaluable in its contribution to the broader goal of achieving quantum advantage and integrating quantum computing into mainstream technology workflows.

Source: Tobias Rohe and Simon Grätz and Michael Kölle and Sebastian Zielinski and Jonas Stein and Claudia Linnhoff-Popien From Problem to Solution: A general Pipeline to Solve Optimisation Problems on Quantum Hardware. arXiv quant-ph. (2024). https://doi.org/10.48550/arXiv.2406.19876

BREAKDOWN

Quantum Curriculum Learning

🔍️ SIGNIFICANCE: 

  • Curriculum learning is a technique used for machine learning models that draws inspirations from human learning processes. The training process is structured so that simpler concepts or tasks are introduced to the model first, before gradually increasing the complexity of the tasks. This technique is not only efficient, but also effective in that it allows the model to build a solid foundational knowledge.

  • In quantum computing, the practicality of practical applications most often comes from the ability to demonstrate significant speedup over classical machines. QML, however, is an exciting branch of the field for its potential to detect patterns in data or generate new patterns in data in ways that would be too difficult for classical algorithms. Barriers to practical application of QML stems from challenges with barren plateaus (getting trapped in local minima) and the need for extensive computational resources.

  • In order to realize the full potential of QML, we need a two-pronged focus that centers around improved architecture as much as it does the efficiency of the applied learning model. This is where Q-CurL comes into play. By integrating the concept of curriculum learning with QML, Q-CurL can specifically target quantum data and integrate a dynamic learning schedule that introduces simple tasks and builds upon complexity.

🧪 METHODOLOGY: 

  • Two principal methodologies are explored for QML: task-based Q-CurL and data-based Q-CurL.

  • In task-based Q-CurL, a model benefits from pre-training on auxiliary tasks, which are simpler or have richer datasets, before tackling the main task. The curriculum order is determined by the data density ratio between tasks.

  • Data-based Q-CurL, on the other hand, employs a dynamic learning schedule that adjusts data weights, emphasizing the importance of quantum data in optimizing the loss function to reduce generalization error. This approach dynamically predicts the easiness of each sample at each training epoch, focusing on easier samples early in the training process and more difficult ones later.

📊 OUTCOMES & OUTLOOK: 

  • Overall, Q-CurL was shown to improve training convergence and generalization for unitary learning tasks as well as benefit quantum phase recognition tasks. Task-based Q-CurL showed that pre-training on auxiliary tasks with a curriculum order based on data density ratios significantly improves model performance on the main task while data-based Q-CurL demonstrated increased resistance to noise.

  • These results imply that Q-CurL may make QML more practical and efficient by reducing the quantum resources needed and improving the models' capability and accuracy in handling complex quantum data.

Source: Quoc Hoan Tran and Yasuhiro Endo and Hirotaka Oshima. Quantum Curriculum Learning. arXiv quant-ph. (2024). https://doi.org/10.48550/arXiv.2407.02419

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