I n this presentation we will first talk about the different classes of quantum algorithms currently in use. We will then discuss some hybrid (Quantum + Classical) algorithms in use today, notably the variational quantum algorithms (VQAs). The VQAs could be used in a wide range of applications. We will then discuss the limitations of these algorithms and the need for other approaches. Quantum centric supercomputing (QCSC) is an idea where Quantum algorithms in Quantum hardware work in tandem with classical algorithms in high-performance computers. We will discuss the different varieties of Quantum + HPC workloads. Finally, we will discuss a use-case where Quantum + HPC has been used to solve a chemistry problem. We will end the presentation showing IBM’s roadmap in achieving QCSC.
I n this presentation we will first talk about the different classes of quantum algorithms currently in use. We will then discuss some hybrid (Quantum + Classical) algorithms in use today, notably the variational quantum algorithms (VQAs). The VQAs could be used in a wide range of applications. We will then discuss the limitations of these algorithms and the need for other approaches. Quantum centric supercomputing (QCSC) is an idea where Quantum algorithms in Quantum hardware work in tandem with classical algorithms in high-performance computers. We will discuss the different varieties of Quantum + HPC workloads. Finally, we will discuss a use-case where Quantum + HPC has been used to solve a chemistry problem. We will end the presentation showing IBM’s roadmap in achieving QCSC.
High performance computing (HPC) is renowned for its capacity to tackle complex problems. Meanwhile, quantum computing (QC) provides a potential way to accurately and efficiently solve quantum chemistry problems. The emerging field of quantum-centric high performance computing (QCHPC), which merges these two powerful technologies, is anticipated to enhance computational capabilities for solving challenging problems in quantum chemistry. The implementation of QCHPC for quantum chemistry requires interdisciplinary research and collaboration across multiple fields, including quantum chemistry, quantum physics, computer science and so on. This perspective provides an introduction to the quantum algorithms that are suitable for deployment in QCHPC, focusing on conceptual insights rather than technical details. Parallel strategies to implement these algorithms on quantum-centric supercomputers are discussed. We also summarize high performance quantum emulating simulators, which are considered a viable tool to explore QCHPC. We conclude with challenges and outlooks in this field.
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Quantum computing has the potential to achieve breakthroughs in simulation, optimization, machine learning, finance, cybersecurity, medical science and healthcare, logistics, transportation, and many more. Over the last five years, quantum computing has evolved from a field of scientific research to a full-fledged technology industry. Much progress is still needed to solve real-world problems with quantum technology and achieve true blue quantum advantage. Industry, governments and universities are all experimenting with advanced quantum computing technology to become quantum ready and eventually achieve quantum advantage. They combine quantum and classical approaches to form hybrid models, architectures, runtimes, algorithms and techniques to overcome the limitations NISQ (noisy intermediate quantum) computers thereby integrating internet accessible HPC centers and quantum clouds provided by IBM Quantum, D-Wave Systems, Microsoft Azure, Honeywell, Google, Rigetti, Amazon Braket, Xanadu, or IonQ. Such hybrid approaches are rapidly emerging to explore quantum computing killer applications, such as molecule simulations and exponential algorithm speed-up for hard problems. In this talk, we will discuss challenges and opportunities in the realm of hybrid quantum-classical computing when integrating HPC and quantum computing platforms in the quest of achieving quantum advantage through scalable infrastructure.
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.
The rapid evolution of quantum computing has introduced groundbreaking approaches to solving complex optimization problems, traditionally limited by classical methods. This talk explores the principles and applications of quantum optimization, with a focus on Variational Quantum Algorithms (VQAs). These algorithms, including the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA), leverage hybrid quantum-classical systems to address challenges in diverse fields such as combinatorial optimization, quantum chemistry, and material design. Attendees will gain insights into the theoretical foundations of quantum computing, the operational mechanics of quantum circuits, and the integration of quantum programming tools like Qiskit. Practical examples and demonstrations will highlight how VQAs outperform classical methods in specific scenarios, offering a transformative perspective on optimization in the quantum era.
Variational quantum algorithms (VQAs) have established themselves as a central computational paradigm in the Noisy Intermediate-Scale Quantum (NISQ) era. By coupling parameterized quantum circuits (PQCs) with classical optimization, they operate effectively under strict hardware limitations. However, as quantum architectures transition toward early fault-tolerant (EFT) and ultimate fault-tolerant (FT) regimes, the foundational principles and long-term viability of VQAs require systematic reassessment. This review offers an insightful analysis of VQAs and their progression toward the fault-tolerant regime. We deconstruct the core algorithmic framework by examining ansatz design and classical optimization strategies, including cost function formulation, gradient computation, and optimizer selection. Concurrently, we evaluate critical training bottlenecks, notably barren plateaus (BPs), alongside established mitigation strategies. The discussion then explores the EFT phase, detailing how the integration of quantum error mitigation and partial error correction can sustain algorithmic performance. Addressing the FT phase, we analyze the inherent challenges confronting current hybrid VQA models. Furthermore, we synthesize recent VQA applications across diverse domains, including many-body physics, quantum chemistry, machine learning, and mathematical optimization. Ultimately, this review outlines a theoretical roadmap for adapting quantum algorithms to future hardware generations, elucidating how variational principles can be systematically refined to maintain their relevance and efficiency within an error-corrected computational environment.
Variational Quantum Algorithms are a major method of harnessing Noisy Intermediate-Scale Quantum devices to solve classically intractable problems in physics, chemistry, optimization, and machine learning. The current review offers a critical overview of VQAs, with special emphasis on their theoretical backgrounds, algorithmic structures, and application performance within realistic models of noise. Several key variants, such as the Variational Quantum Eigensolver, Quantum Approximate Optimization Algorithm, and Quantum Neural Networks, are compared in terms of noise robustness, ansatz archi- tecture, and hybrid quantum-classical methods of optimization. Benchmarks in fields such as quantum chemistry, finance modeling, and particles and nuclei physics reveal the poten- tial of VQAs in achieving practical quantum advantage. Fundamentally limiting factors 1such as barren plateaus, optimization overhead, and hardware constraints are considered in addition to mitigation strategies, including adaptive construction of ansatz, informed training in the presence of noise, and quantum error correction using QVECTOR. The paper is concluded with an overview of the scalability and future of VQAs in the direction of transitioning toward fault-tolerant quantum computing.