Practical Quantum Computing

Minh Thanh Chung, Yaknan John Gambo, Burak Mete +12 more

Quantum computing (QC) promises to be a transformative technology with impact on various application domains, such as optimization, cryptography, and material science. However, the technology has a sharp learning curve, and practical evaluation and characterization of quantum systems remains complex and challenging, particularly for students and newcomers from computer science to the field of quantum computing. To address this educational gap, we introduce Q-BEAST, a practical course designed to provide structured training in the experimental analysis of quantum computing systems. Q-BEAST offers a curriculum that combines foundational concepts in quantum computing with practical methodologies and use cases for benchmarking and performance evaluation on actual quantum systems. Through theoretical instruction and handson experimentation, students gain experience in assessing the advantages and limitations of real quantum technologies. With that, Q-BEAST supports the education of a future generation of quantum computing users and developers. Furthermore, it also explicitly promotes a deeper integration of High Performance Computing (HPC) and QC in research and education.

Prabu Manoharan

The understanding of the Agile development practices and quantum computer are becoming more and more a requirement as the engineering projects of the day are growing extremely complicated and dynamic. Agile methods that are familiar to facilitate an iterative cycle, cooperate and adaptability are inherently appropriate to the experimental and unpredictable world of quantum software development. Quantum computing (QC) that is grounded on the principles of superposition and entanglement is the origin of novel computational paradigms that require new programming and continuous fine-tuning. In addition to satisfying this need for brief development cycles and quick feedback loops, other Agile methods like Scrum, Kanban, and Extreme Programming (XP) also demand rigorous planning. On the other hand, quantum software engineering is faced with specific issues such as the unskilled personnel, testing issues, and hardware limitations. In that case, the Agile methodologies are salvaged and assist in the breaking down of complex and quantum tasks into manageable units and result in a continuous improvement. All in all, standardization of Agile practices and quantum computing does not just assist in the innovation, but also in reduction of the risk of development and speeding up the development of the practical quantum solutions to the current engineering projects.

Kang-Yang Zeng, Xiaodong Niu, Adnan Khan +2 more

Quantum computing (QC) has emerged as a potential computational tool in fluid dynamics. However, a practical QC protocol with end-to-end practical applications to fluid flow is still in nascent. We here propose a simple and practical hybrid QC algorithm based on lattice Boltzmann methods (LBM), which we term HQLBM, for fluid dynamics simulations. The existing QC-based LBMs are limited to simple low-Reynolds-number flow simulations due to their disability to handle the nonlinear collision term in LBM for QC. In this work, a linearized non-equilibrium collision, derived from kinetic theory, is presented for HQLBM simulating practical flows. The present approach ensures the unitary of quantum algorithms while keeping the collision relaxation parameter adjustable for simulating different Reynolds number flows. Most importantly, the linearized collision keeps the accuracy of the original nonlinear one in the lattice Boltzmann equation when it theoretically recovers its macroscopic counterpart—Navier–Stokes equations. The practicality of the proposed method is demonstrated by simulating two typical flows, including lid-driven and natural convection flows in a square cavity at different Reynolds and Rayleigh numbers, respectively. It is shown that the proposed HQLBM has good numerical accuracy compared to the conventional LBM, and lower complexity on comparisons to the QC-based LBM carried out on single-circuit QC even when it is performed on a Qiskit virtual quantum computer. This work offers a practical application of QC-based LBM for complex fluid dynamics problems.

Mikio Nakahara