On the theory of quantum and towards practical computation: A review

Craig S. Lent

Sergio Boixo

Outperforming classical supercomputers in a well-defined computational task is an important milestone in the long-term quest for practical quantum computing. I will argue that this milestone has been reached for the task of random quantum circuit sampling. I will review recent advances in complexity theory and classical algorithms related to this experiment. BosonSampling is another important proposal for early beyond-classical demonstrations. I will review recent BosonSampling experiments and algorithms.

Pushpalata Patil, Ravindra D Patil, Sandeep Kulkarni

Research Problem: The traditional methods of drug discovery are time-consuming, costly, and at times ineffective. There is a need for more novel approaches that can enhance the processes of discovering new drugs and developing them into usable products. Objective of the Paper: This paper aims to conduct a review of the literature and find how the application of quantum computing can change the way drugs are discovered and processed in practice. Process of Review: The review process involved a comprehensive examination of the available literature and recent advances in the application of quantum computing to drug discovery. The review encompassed major studies, theories, and case studies related to the subject matter, aiming to identify the current state and potential future directions of the domain. Insights of the Review: 1. Primary Building Blocks: With quantum computing, superposition, entanglement, and other principles can be utilised to enable complex computations to be performed at higher speeds using less time than traditional computers. 2. Quantum modelling: Quantum computers are expected to accurately model the interactions of various molecules, which could result in the identification of viable drug candidates. 3. Drug Discovery Processes: Quantum algorithms are capable of faster processing than traditional methods when it comes to solving optimisation problems, such as protein structure prediction and molecular docking, in drug design. 4. Limitations and Challenges: As great as the promise of quantum computing is, it has its limitations and challenges, including hardware limitations, error rates, and the need for specialised algorithms. 5. Future Developments: The ongoing development of quantum hardware and algorithms is expected to enhance the practical applications of quantum computing in drug discovery, enabling the quicker and more cost-effective creation of new drugs.

Alain Aspect, Anthony J. Leggett, John P. Preskill +2 more

M. R. Hadi, G. Mohammed, Ammar H. Shnawa +3 more

Beyond traditional computing, quantum computing is based on quantum phenomena like entanglement and superposition. Even though quantum computing is still developing, it has had a tremendous impact on the world of computing by opening up a brand-new dimension. To fully appreciate its potential and advantages, however, there are a number of obstacles to overcome, much like with any other idea or subject. This review explains quantum computing models and identifies some advantages and disadvantages. This systematic review's key contribution is its summary of the state-of-the-art quantum computing models used in numerous fields nowadays. Based on the literature study, it offers new classifications of quantum models and connects the findings to the four main groups of quantum computing models. Evaluation demonstrates that despite their foundation in quantum operations and circuits, the majority of the studied models are either mathematical or algorithmic. In theory, quantum computers are capable of solving some tasks tenfold faster than their classical equivalents. Although the advent of practical quantum computation has not yet occurred, it will have an impact on almost all fields of science. In this analysis, we investigate the potential impact of contemporary quantum algorithms on computational science and informatics.

Xiao-Ming Zhang, Arthur G. Rattew, Bujiao Wu +4 more

Encoding classical data into quantum systems is a foundational step in the execution of nearly all quantum algorithms, and a critical bottleneck in realizing practical quantum advantage. This review provides a comprehensive account of the concepts, algorithms, and practical considerations associated with quantum data encoding. We trace the development from its early conceptual foundations to recent advances, considering commonly used access models, such as quantum state preparation, unitary synthesis, QRAM and block encoding. We survey the circuit size, depth, space-time tradeoffs, as well as non-Clifford resources required for fault-tolerant implementation. We also discuss the roles of different access models in quantum algorithms. Special attention is given to structured data, such as sparse data, Boolean functions and data represented by tensor networks. This review bridges theory and applications, serving both as a pedagogical guide for newcomers and as a reference for active researchers. We also highlight the pivotal role of quantum data encoding in quantum computing and provide insights into future directions that will enable quantum advantage.