QSC Research Pillars
QSC is a collaborative effort between academia and industry focusing on distributed and scalable quantum computing. Our consortium brings together researchers and practitioners from various disciplines to investigate six interconnected research themes:
Developing methodologies for distributed quantum computing in applied chemistry to model complex molecular systems
Creating sophisticated frameworks for simulating quantum dynamics across networked quantum processors
Investigating energy efficiency challenges in quantum computing hardware
Designing specialized compilation techniques optimized for distributed quantum architectures
Advancing distributed quantum machine learning algorithms capable of handling complex datasets
Establishing comprehensive benchmarking protocols to evaluate distributed quantum computing performance
Through these coordinated interdisciplinary research efforts, QSC aims to bridge the gap between theoretical quantum computing concepts and practical applications.
The intersection of quantum mechanics and computational science offers significant potential for addressing global challenges that classical physics cannot fully resolve. Quantum mechanical problems—ranging from climate change mitigation to understanding molecular interactions in fertilizer production, water purification, and disease treatment—demand computational approaches that can navigate the intricate quantum landscape. However, solving these complex quantum mechanical equations on classical computers rapidly becomes computationally intractable, necessitating advanced quantum computing strategies. Researchers are developing sophisticated distributed quantum computing (DQC) approaches that focus on translating system Hamiltonian information into precise quantum gate operations, with particular emphasis on two critical encoding techniques: Trotterization and decomposition to a linear combination of unitaries (LCU). These methodologies aim to generate robust Hamiltonian encodings for challenging molecular systems, including complex molecules relevant to density matrix re-normalization group studies and homogeneous catalysts based on transition metals, ultimately bridging the gap between computational simulation and real-world molecular understanding.
Quantum simulations leverage quantum computers to model intricate quantum systems that defy classical computational methods, particularly those characterized by complex, nuanced interactions. This emerging field spans critical domains including quantum materials and chemical reaction dynamics, with profound implications for electronics, photonics, energy technologies, environmental science, and biomedical research. Researchers are strategically focusing their efforts on two primary computational domains: solid materials investigations utilizing unitary dynamics, and chemical system studies that demand the development of sophisticated nonunitary simulation methodologies. By precisely mimicking quantum system behaviors that are computationally intractable through traditional approaches, quantum simulations offer deeper insights into molecular and material interactions at the most fundamental quantum mechanical levels.
Distributed quantum machine learning (dQML) is an emerging computational paradigm that harnesses distributed computing to scale and accelerate data-driven applications through advanced parametrized quantum circuits (PQCs). This approach aims to develop quantum machine learning models that go beyond traditional computational limitations, particularly in domains requiring nuanced data analysis and predictive capabilities. We aim to develop a software framework designed to create quantum machine learning models with exceptional performance characteristics, including rapid and accurate chemical property analysis, enhanced expressibility, and minimal training data requirements. By employing distributed techniques that enable data parallelism and intelligent qubit partitioning, these approaches promise novel applications in applied chemistry, drug discovery, and quantum dynamics, effectively reducing computational complexity while contributing to quantum-enhanced predictive modeling.
Distributed quantum computing is pushing beyond the limitations of traditional single-unit quantum processors by exploring how multiple quantum devices can work together—a challenge that introduces significant complexity in communication and coordination. We aim to tackle these challenges by developing innovative strategies to improve performance, create smarter error correction methods, and find ways to make quantum algorithms that were once strictly sequential run more efficiently in parallel. A particularly exciting aspect of this work is the development of new techniques that, for the first time, will allow computer compilers to thoroughly validate and verify distributed quantum algorithms. By addressing fundamental issues like how quantum computers can effectively communicate and minimize errors, this research is laying the groundwork for more powerful, reliable, and scalable quantum computing systems that can tackle increasingly complex computational problems.
Identifying practical applications where quantum computers deliver tangible advantages over classical systems represents a critical milestone in the quantum computing transition. While theoretical discussions often emphasize formal asymptotic speedups, the pragmatic question facing organizations is more nuanced: Does the quantum advantage justify the investment? This assessment extends beyond purely exponential algorithmic improvements to include a comprehensive cost-benefit analysis. Key considerations in this evaluation include how algorithm runtime scaling compares between quantum and classical approaches, potential improvements in solution quality, and the financial implications of quantum computing time. Understanding these factors helps organizations make informed decisions about when and where to leverage quantum computing capabilities for maximum business value.
As quantum computing technology advances, addressing its resource consumption, optimization, and performance has become an urgent industry priority, with energy considerations standing at the forefront of sustainable development. Our interdisciplinary approach combines expertise from quantum thermodynamics, information science, physics, engineering, and energy studies to tackle fundamental questions about quantum computing's energetic footprint. We're investigating the comprehensive resource costs of quantum systems, including the significant energy demands of cooling infrastructure, while developing strategies to minimize these requirements. By establishing clear metrics for assessing energy efficiency in quantum algorithm execution, our work bridges fundamental research with practical applications. This energy-focused theme intentionally intersects with all other research areas, recognizing that sustainable quantum computing advancement requires integrating energy considerations across the entire development ecosystem.
This research area spans across applications and topics.