Practical application-specific advantage through hybrid quantum computing
Michael Romanovich Perelshtein, Asel Sagingalieva, Karan Pinto, Shete, Vishal, Pakhomchik, Alexey, Artem Melnikov, Florian Neukart, Georg Gesek, Alexey A. Melnikov, V. M. Vinokur
Quantum computing promises to tackle technological and industrial problems insurmountable for classical computers. However, today's quantum computers still have limited demonstrable functionality, and it is expected that scaling up to millions of qubits is required for them to live up to this touted promise. The feasible route in achieving practical quantum advantage goals is to implement a hybrid operational mode that realizes the cohesion of quantum and classical computers. Here we present a hybrid quantum cloud based on a memory-centric and heterogeneous multiprocessing architecture, integrated into a high-performance computing data center grade environment. We demonstrate that utilizing the quantum cloud, our hybrid quantum algorithms including Quantum Encoding (QuEnc), Hybrid Quantum Neural Networks and Tensor Networks enable advantages in optimization, machine learning, and simulation fields. We show the advantage of hybrid algorithms compared to standard classical algorithms in both the computational speed and quality of the solution. The achieved advance in hybrid quantum hardware and software makes quantum computing useful in practice today.
Quantum computing promises to tackle technological and industrial problems insurmountable for classical computers. However, today's quantum computers still have limited demonstrable functionality, and it is expected that scaling up to millions of qubits is required for them to live up to this touted promise. The feasible route in achieving practical quantum advantage goals is to implement a hybrid operational mode that realizes the cohesion of quantum and classical computers. Here we present a hybrid quantum cloud based on a memory-centric and heterogeneous multiprocessing architecture, integrated into a high-performance computing data center grade environment. We demonstrate that utilizing the quantum cloud, our hybrid quantum algorithms including Quantum Encoding (QuEnc), Hybrid Quantum Neural Networks and Tensor Networks enable advantages in optimization, machine learning, and simulation fields. We show the advantage of hybrid algorithms compared to standard classical algorithms in both the computational speed and quality of the solution. The achieved advance in hybrid quantum hardware and software makes quantum computing useful in practice today.
Quantum computers acting as ML accelerators have attracted considerable interest as a promising avenue for addressing the limitations of classical computing confronted with solving complex, high-dimensional problems and handling large data sets. The most exciting aspect of quantum computing lies in its quantum advantage via techniques such as quantum parallelism, superposition, and entanglement which allows not only for rapid data processing, but also a means to resolve complex optimization problems beyond the capacity of classical algorithms. Nevertheless, the usage of quantum computing in machine learning is still a fledgling domain, with challenges concerning quantum noise, hardware restrictions, and algorithmic development. In particular, this research is concerned with the development of and study on hybrid quantum-classical algorithms that target to improve ML models in terms of computational capabilities by making use of the best of both worlds. To address this, researchers are developing hybrid algorithms that leverage the strengths of quantum computing for specific tasks, like optimization and pattern recognition, while still relying on classical computing for more flexible and larger-scale processing, ultimately achieving more efficient machine learning systems that can handle large datasets and complex problem-solving more effectively. It includes potential hybrid approaches like quantum-enhanced optimization algorithms, quantum neural networks, and quantum support vector machines, and examines how such algorithms can be used for practical ML problems including data classification, regression and clustering. Furthermore, the paper proposes that hybrid quantum algorithms offer several advantages over their classical counterparts, such as faster convergence rates, higher accuracy in high-dimensional spaces, and improved handling of non-linear and complex problem landscapes. Key advancements in the area of data processing speed and efficiency in problem-solving with and without quantum in the machine learning workflow are also highlighted in the research through comparative analysis and simulation studies. Lastly, the paper concludes by outlining the obstacles yet to be surmounted and the opportunities that lie ahead for hybrid quantum-classical algorithms, such as further improvement of quantum hardware and error mitigation methods, as well as the design of new algorithmic paradigms that seamlessly integrates the classical and quantum computing spaces.
Quantum computing as emerging technology raises high expectations to accelerate complicated calculations, superseding capabilities of presently available supercomputers and to provide a solution to the ever-increasing challenge of processing big amount of data. Besides theoretical promises, quantum computers have successfully been realized in experimental settings during the past few years, and can now also be accessed commercially. Given this availability of quantum computers, their application potential can be scrutinized. Promised to lead to disruptive changes in addressing and solving problems from the domains of simulation, optimization and machine learning, quantum computers have been researched in context of many application scenarios in industrial and academic setting in the recent years. However, it turns out that currently available quantum computers are still to small in size and limited in quality to fulfill the high hopes set in them at the moment. This review reflects on the current state of the technology coming from the perspective of the application, and indicates the developments required on hardware, software and algorithmic side to eventually make quantum computing beneficial for applications.
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.
The entwining of quantum computing with classical machine learning has proven to be one of the most promising frontiers of artificial intelligence, as it possesses the potential to speed up intricate computations in ways that classical systems will never be able to achieve. This study delves into the theoretical and practical implications of hybrid quantum-classical approaches in machine learning, while also examining their advantages, limitations, and applicability in different domains. Utilizing quantum entanglement and parallelism, hybrid solutions also have the potential to process high-dimensional data, optimize complex functions, and facilitate pattern recognition tasks more efficiently. However, noise, decoherence, and limited qubit connectivity on current hardware pose considerable barriers to practical implementations. The authors discuss hybrid architectures like quantum-enhanced optimizers and variational quantum circuits which include classical pre- and post-processing to alleviate quantum limitations. Moreover, a comparative study is performed to show the effectiveness of hybrid models comparing with classical machine learning state-of-the-art in natural language processing, computer vision and financial modeling. Results indicate that although hybrid quantum–classical approaches provide significant gains in optimization and computational efficiency; these benefits remain limited by hardware limitations and the scalability of quantum algorithms. With these considerations in mind, the paper offers guidance for targeting future research directions in quantum machine learning, including advances in quantum error correction, quantum hardware scalability, and new algorithmic approaches. Additional advancements needed and notably, the study focused on a smaller representation of data Since quantum computing is relatively new and researchers are still exploring its application, it is still early days in terms of machine learning models that are solely, intrinsically quantum.
Hybrid computing models, which integrate both classical and quantum systems, represent a promising frontier in enhancing computational power and efficiency. Classical computing systems, while highly efficient for a broad range of tasks, encounter significant limitations when addressing complex, large-scale problems such as optimization, cryptography, and molecular simulations. Quantum computing, on the other hand, offers exponential speedup for specific algorithms, particularly in areas like factorization, search, and simulation of quantum systems. However, quantum systems are not yet mature enough to replace classical systems entirely, due to issues like error rates, qubit coherence, and the need for cryogenic environments. As a result, hybrid models are emerging as the most viable solution, combining the strengths of both paradigms to tackle computationally intensive tasks more efficiently. This paper provides a comprehensive analysis of the development and application of hybrid computing models. It explores how these models leverage the parallel processing power of quantum systems while utilizing the robustness and data handling capabilities of classical systems. We discuss various architectures proposed for hybrid computing, focusing on their scalability, efficiency, and real-world applications across industries such as healthcare, finance, cryptography, and artificial intelligence. Additionally, we examine the challenges involved in integrating classical and quantum systems, including synchronization, resource management, and algorithm development. The paper concludes with future directions for hybrid computing, emphasizing the potential for these models to revolutionize fields that rely on high-performance computing by offering unprecedented computational capabilities. In summary, hybrid computing models stand at the intersection of classical and quantum technologies, providing a synergistic approach to solving some of the most challenging computational problems faced today.
The technology of Quantum Computing (QC) is continuously evolving, as researchers explore new technologies and the public gains access to quantum computers with an increasing number of qubits. In addition, the research community and industry are increasingly interested in the potential use, application, and contribution of QCs to large-scale problems in the real world as a result of this technological enhancement. QCs operations are based on quantum mechanics, and their special properties are mainly exploited to solve computationally intensive problems in polynomial time, problems that are commonly unsolvable, even by High-Performance Computing systems (HPCs) in a feasible time. However, since QCs cannot perform as general-purpose computing machines, alternative computational approaches aiming to boost further their enormous computing abilities are requested, and their combination as an additional computing resource to HPC systems is considered as one of the most promising ones. In the proposed hybrid HPCs, the Quantum Processing Units (QPUs), similar to GPUs and CPUs, target specific problems that, through Quantum Algorithms, can exploit quantum properties like quantum entanglement and superposition to achieve substantial performance gains from the HPC point of view. This interconnection between classical HPC systems and QCs towards the creation of Hybrid Quantum-Classical computing systems is neither straightforward nor standardized while crucial for unlocking the real potential of QCs and achieving real performance improvements. The interconnection between the classical and quantum systems can be performed in the hardware, software (system), or application layer. In this study, a concise overview of the existing architectures for the interconnection interface between HPCs and QCs is provided, focusing mainly on hardware approaches that enable effective hybrid quantum-classical operation.