Classical and quantum computing

Maryam Tavassoli Sabzevari, Matteo Esposito, Davide Taibi +1 more

Quantum Computing (QC) is transitioning from theoretical frameworks to an indispensable powerhouse of computational capability, resulting in extensive adoption across both industrial and academic domains. QC presents exceptional advantages, including unparalleled processing speed and the potential to solve complex problems beyond the capabilities of classical computers. Nevertheless, academic researchers and industry practitioners encounter various challenges in harnessing the benefits of this technology. The limited accessibility of QC resources for classical developers, and a general lack of domain knowledge and expertise, represent insurmountable barrier, hence to address these challenges, we introduce a framework- Quantum Computing as a Service for Hybrid Classical-Quantum Software Development (QCSHQD), which leverages service-oriented strategies. Our framework comprises three principal components: an Integrated Development Environment (IDE) for user interaction, an abstraction layer dedicated to orchestrating quantum services, and a service provider responsible for executing services on quantum computer. This study presents a blueprint for QCSHQD, designed to democratize access to QC resources for classical developers who want to seamless harness QC power. The vision of QCSHQD paves the way for groundbreaking innovations by addressing key challenges of hybridization between classical and quantum computers.

Yorick Hardy, Willi-Hans Steeb

Annapurna P Patil, Shashank Prakash, Shreyas S Acharya +2 more

With the rapid onset of the Quantum Computing (QC) paradigm in the field of computer science, the QC approach could enable better outcomes to solve some of the classical problems. It is plausible to speculate that quantum computers could outperform classical computers in machine learning tasks because quantum systems exhibit peculiar patterns that conventional systems are assumed to be incapable of producing. This work introduces the QC approach to Machine Learning solutions in computer science and provides a comparison of the performances of QC-based approaches against the classical ones. In addition, an attempt to discuss the potential of QC in solving classical computer science problems such as Shor’s algorithm, the Travelling Salesman problem(TSP), and the prime factorization problem is made through the empirical study of the same.In conclusion, we analyze the outcomes of our practical investigation and devise potential improvements that can be made to the QC methods employed by the solutions.

Daniel Gottesman, Alexei Kitaev, Alexander Khanievich Shen' +1 more

Wei Tang, Teague Tomesh, Martin Suchara +2 more

Quantum computing (QC) is a new paradigm offering the potential of exponential speedups over classical computing for certain computational problems. Each additional qubit doubles the size of the computational state space available to a QC algorithm. This exponential scaling underlies QC’s power, but today’s Noisy Intermediate-Scale Quantum (NISQ) devices face significant engineering challenges in scalability. The set of quantum circuits that can be reliably run on NISQ devices is limited by their noisy operations and low qubit counts. This paper introduces CutQC, a scalable hybrid computing approach that combines classical computers and quantum computers to enable evaluation of quantum circuits that cannot be run on classical or quantum computers alone. CutQC cuts large quantum circuits into smaller subcircuits, allowing them to be executed on smaller quantum devices. Classical postprocessing can then reconstruct the output of the original circuit. This approach offers significant runtime speedup compared with the only viable current alternative—purely classical simulations—and demonstrates evaluation of quantum circuits that are larger than the limit of QC or classical simulation. Furthermore, in real-system runs, CutQC achieves much higher quantum circuit evaluation fidelity using small prototype quantum computers than the state-of-the-art large NISQ devices achieve. Overall, this hybrid approach allows users to leverage classical and quantum computing resources to evaluate quantum programs far beyond the reach of either one alone.

Song Yuan Yan