Quantum Algorithms for Traveling Salesperson Show Varied Efficiency Across Hardware Platforms

The Traveling Salesperson Problem, a classic challenge in optimising routes, serves as a crucial test for emerging quantum computers, but assessing their true potential remains difficult given the diversity of hardware technologies currently available. Amine Bentellis, Benedikt Poggel, and Jeanette Miriam Lorenz from the Fraunhofer Institute for Cognitive Systems IKS lead a study that directly benchmarks several quantum computing platforms, including neutral atom, ion trap, and both gate-based and annealing hardware, using this complex problem. Their research moves beyond theoretical capabilities, detailing the practical steps required to run real-world applications on quantum devices and revealing significant differences in performance between providers. By focusing on application-level efficiency rather than underlying technology, this work provides an essential benchmark for understanding the current state of quantum computing and identifying the necessary developments to unlock its full potential.

Ahead of maturity, the performance of quantum computations and the processes involved in running quantum-enhanced algorithms vary significantly between different providers. This study includes a comparative analysis of various hardware architectures, using the Traveling Salesperson Problem as a central example of a combinatorial optimisation challenge, highlighting the necessary steps to implement real-world applications on quantum hardware and presenting results demonstrating the relative efficiency of exemplary quantum algorithms on neutral atom-based, ion trap and superconducting hardware, including both gate-based and annealing devices.

Benchmarking Diverse Quantum Computing Hardware Platforms

This extensive research paper details a comprehensive benchmarking study of various quantum computing platforms, aiming to evaluate their performance and suitability for solving practical problems. Understanding the strengths and weaknesses of each platform, including superconducting qubits, trapped ions, neutral atoms, and quantum annealers, is crucial to guide development and application. The study covers a wide range of quantum computing technologies and focused on the Maximum Independent Set (MIS) problem and the Traveling Salesman Problem (TSP) as representative challenges. Researchers implemented and tested various quantum and hybrid quantum-classical algorithms on each platform, evaluating performance using metrics like solution quality, runtime, scalability, and error rates.

The study emphasizes that no single quantum computing platform consistently outperforms all others, with superconducting qubits susceptible to noise, trapped ions offering higher fidelity and coherence times, and neutral atoms promising scalability but remaining under development. Quantum annealers are effective for specific optimization problems but less versatile than gate-based quantum computers, and algorithm performance is heavily influenced by the underlying hardware architecture. Combining classical and quantum computation often yields the best results, and addressing errors in quantum computations is essential for achieving meaningful results. The paper highlights the need for standardized benchmarking protocols, improved error correction and mitigation techniques, and the development of new algorithms better suited for specific hardware architectures, as well as scalability studies to investigate how algorithms perform as the number of qubits increases. This research provides a valuable snapshot of the current state of quantum computing hardware and offers guidance for researchers and developers seeking to leverage these technologies for practical applications.

Traveling Salesperson Problem Solved on Three Platforms

The study presents a comprehensive comparison of superconducting qubits, ion traps, and neutral atoms, focusing on their ability to solve practical problems, specifically the Traveling Salesperson Problem. Researchers moved beyond abstract performance metrics and assessed each technology through the entire process of solving a complex optimization challenge, acknowledging that raw hardware specifications don’t fully translate to real-world performance. The investigation revealed significant differences in how each technology tackles the problem, requiring tailored algorithms and implementation strategies. While superconducting qubits were able to handle the largest problem instances, ion trap and neutral atom systems were constrained by the number of qubits available.

The study emphasizes that simply increasing qubit count isn’t enough; the architecture and control mechanisms of each system play a crucial role in performance. Researchers utilized the Variational Quantum Eigensolver (VQE) algorithm as a baseline, adapting it where necessary to accommodate the specific constraints of each hardware platform. The results demonstrate that superconducting annealers require a fundamentally different approach, directly encoding the problem’s cost function, while gate-based systems rely on more complex quantum circuits. This work showcases how seemingly equivalent algorithms can perform drastically differently depending on the underlying hardware.

A key finding is the importance of considering the entire solution process, from problem encoding to algorithm implementation and data analysis. The study demonstrates that even with comparable qubit counts, the performance of each technology is heavily influenced by factors like connectivity, gate fidelity, and control precision. By focusing on a practical application, the research provides valuable insights for both hardware developers and end-users.

Hardware Impacts Algorithm Performance for TSP

This study advances understanding of quantum computing by comparatively analyzing the performance of various hardware architectures when applied to a practical problem, the Traveling Salesperson Problem. The research demonstrates that each hardware platform, including neutral atom, ion trap, and gate-based and annealing devices, possesses unique features that can be leveraged through tailored algorithmic design, highlighting the need to consider hardware characteristics alongside algorithmic development. Currently, progress remains limited by hardware capabilities and the compatibility of existing algorithms with specialized hardware. While variational algorithms offer potential early advantages across different platforms, further development is needed to address these limitations.

The field requires a unified approach to hardware access and standardized metrics for comparison, reducing vendor-specific adaptations and enabling more direct evaluation of progress. Future work should focus on exploring a wider range of practical problems and identifying the specific failure modes of different hardware types when applied to those applications, ultimately paving the way for practical quantum computing solutions. This research underscores the importance of a holistic approach to quantum computing, considering both hardware and software aspects to unlock the full potential of this emerging technology.