QCI Qbsolv Delivers Strong Classical Performance for Quantum-Ready Formulation (2005.11294v1)

Abstract: Many organizations that vitally depend on computation for their competitive advantage are keen to exploit the expected performance of quantum computers (QCs) as soon as quantum advantage is achieved. The best approach to deliver hardware quantum advantage for high-value problems is not yet clear. This work advocates establishing quantum-ready applications and underlying tools and formulations, so that software development can proceed now to ensure being ready for quantum advantage. This work can be done independently of which hardware approach delivers quantum advantage first. The quadratic unconstrained binary optimization (QUBO) problem is one such quantum-ready formulation. We developed the next generation of qbsolv, a tool that is widely used for sampling QUBOs on early QCs, focusing on its performance executing purely classically, and deliver it as a cloud service today. We find that it delivers highly competitive results in all of quality (low energy value), speed (time to solution), and diversity (variety of solutions). We believe these results give quantum-forward users a reason to switch to quantum-ready formulations today, reaping immediate benefits in performance and diversity of solution from the quantum-ready formulation,preparing themselves for quantum advantage, and accelerating the development of the quantum computing ecosystem.

• The paper shows that QCI qbsolv achieves robust classical performance on 45 challenging QUBO instances with energy differences as low as 10⁻¹³.
• It utilizes a cloud-based, quantum-ready approach to deliver diverse solutions, with Hamming distances up to 350 observed among outputs.
• The paper implies that adopting such classical tools enables organizations to benefit now and smoothly transition to future quantum computing applications.

Analysis of "QCI Qbsolv Delivers Strong Classical Performance for Quantum-Ready Formulation"

The paper "QCI Qbsolv Delivers Strong Classical Performance for Quantum-Ready Formulation" investigates the classical performance of the qbsolv tool, particularly in its ability to solve Quadratic Unconstrained Binary Optimization (QUBO) problems. QUBO formulations are instrumental as they prepare the ground for quantum-ready applications. The authors argue that organizations aiming to leverage quantum computing should adopt quantum-ready formulations such as QUBO today to benefit from immediate performance improvements while preparing for future quantum advantages.

Overview of Qbsolv's Performance

The authors have developed a new generation of the qbsolv tool which is designed for strong classical performance, poised for future quantum integration. This iteration of qbsolv is offered as a cloud service and is designed to provide a robust classical solution to QUBO formulations. The performance of qbsolv is evaluated across several dimensions: quality of solutions (optimality), speed (time to solution), and diversity of solutions (variety).

The results demonstrate that qbsolv delivers highly competitive results. The tool is evaluated using a set of 45 challenging QUBO instances derived from the MQlib collection. The results show that qbsolv competes well in terms of solution quality and also delivers noteworthy solution diversity, which is a critical parameter in optimization scenarios.

Notable Numerical Results

Results from the benchmarks highlight qbsolv's capabilities, providing solutions with energy differences on magnitudes as low as 10\textsuperscript{-13}, indicating high precision. Moreover, qbsolv shows exemplary diversity in solutions, identifying a generous number of distinct solutions for some instances, with Hamming distances as high as 350 between them.

Implications and Future Considerations

The success of qbsolv on classical architectures implies that quantum-forward organizations can adopt quantum-ready software without waiting for the quantum hardware maturity. This positions organizations advantageously when quantum computing becomes more practical, allowing them to reap benefits in domains like drug design, traffic optimization, and industrial workflows as highlighted in the paper.

While the current results are promising, they also pave the way for potential improvements. Enhancements could include increased compute resources, improved recognition of solution-finding progress, and further exploration of sparse problem formulation.

The insights about diversity of solutions are particularly significant as they emphasize the variety rather than just the quality of solutions, resonating with real-world applications that often require multiple viable solutions due to external constraints.

In summary, the paper reinforces the argument for preparing computational tools that leverage quantum-ready formulations now, ensuring a seamless transition as quantum computing capabilities evolve. The demonstrated classical performance of qbsolv serves as a proof of concept for this strategic shift, offering immediate advantages and setting the foundation for future quantum application integration.