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Bulletin of the American Physical Society

Hardware Considerations for High-connectivity Quantum Annealers

Steven J. Weber, Gabriel Samach, Danna Rosenberg, Jonilyn Yoder, David K. Kim, Andrew J. Kerman, William D. Oliver

Published Mar 5, 2018• 8 citations
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Abstract Quantum annealers like those manufactured by D-Wave Systems are designed to find high quality solutions to optimization problems that are typically hard for classical computers. They utilize quantum effects like tunneling to evolve toward low-energy states representing solutions to optimization problems. However, their analog nature and limited control functionalities present challenges to correcting or mitigating hardware errors. As quantum computing advances towards applications, effective error suppression is an important research goal. We propose a new approach called replication based mitigation (RBM) based on parallel quantum annealing (QA). In RBM, physical qubits representing the same logical qubit are dispersed across different copies of the problem embedded in the hardware. This mitigates hardware biases, is compatible with limited qubit connectivity in current annealers, and is well-suited for currently available noisy intermediate-scale quantum annealers. Our experimental analysis shows that RBM provides solution quality on par with previous methods while being more flexible and compatible with a wider range of hardware connectivity patterns. In comparisons against standard QA without error mitigation on larger problem instances that could not be handled by previous methods, RBM consistently gets better energies and ground state probabilities across parameterized problem sets.
Quantum annealing can potentially be used to find better solutions to hard optimization problems faster than purely classical hardware. To take full advantage of quantum effects such as tunneling, a physical annealer should be comprised of qubits with a sufficient degree of quantum coherence. In addition, to encode useful problems, an annealer should provide a dense physical connectivity graph between qubits. Towards these goals, we develop superconducting ``fluxmon'' flux qubits suitable for high-connectivity quantum annealing without the use of performance-degrading lossy dielectrics. We carry out in-depth studies of noise and dissipation, and of qubit-qubit coupling in the strongly nonlinear regime. We perform the first frequency-resolved measurements extracting both the quantum and classical parts of the 1/f flux noise intrinsic to superconducting devices, and observe the classical-to-quantum crossover of the noise. We also identify atomic hydrogen as a magnetic dissipation source. We then implement tunable inter-qubit coupling compatible with high connectivity, and provide direct spectroscopic measurements of ultra-strong coupling between qubits. Finally, we use our system to explore quantum annealing faster than the system thermalization time, a previously unaccessed regime.
Quantum annealing (QA) has the potential to significantly improve solution quality and reduce time complexity in solving combinatorial optimization problems compared to classical optimization methods. However, due to the limited number of qubits and their connectivity, the QA hardware did not show such an advantage over classical methods in past benchmarking studies. Recent advancements in QA with more than 5000 qubits, enhanced qubit connectivity, and the hybrid architecture promise to realize the quantum advantage. Here, we use a quantum annealer with state-of-the-art techniques and benchmark its performance against classical solvers. To compare their performance, we solve over 50 optimization problem instances represented by large and dense Hamiltonian matrices using quantum and classical solvers. The results demonstrate that a state-of-the-art quantum solver has higher accuracy (~0.013%) and a significantly faster problem-solving time (~6561×) than the best classical solver. Our results highlight the advantages of leveraging QA over classical counterparts, particularly in hybrid configurations, for achieving high accuracy and substantially reduced problem solving time in large-scale real-world optimization problems.