We propose a modified quantum annealing protocol, i. e., pulsed quantum annealing} (PQA), in order to increase the success probability by a pulse application during the quantum annealing process. It is well known that the success probability of the conventional quantum annealing is reduced due to the Landau-Zener transitions. By applying a pulse to the system, we modulate the success probability and increase it, compared to the conventional quantum annealing, by optimizing the pulse parameters. We demonstrate our findings for a single qubit both numerically and analytically. The analytical model is based on the tranfer matrix approach and it is in good agreement with the full numerical results. We also investigate the PQA protocol for multi-qubit cases $i. e.,$ random spin-glass instances, and we present an overall increase of the success probability over the conventional quantum annealing, by optimizing the pulse parameters. Our results indicate that PQA can be used to design future high-performance quantum annealing machines, especially for hard instances that the conventional QA protocol behaves poorly.We propose a modified quantum annealing protocol, i. e., pulsed quantum annealing} (PQA), in order to increase the success probability by a pulse application during the quantum annealing process. It is well known that the success probability of the conventional quantum annealing is reduced due to the Landau-Zener transitions. By applying a pulse to the system, we modulate the success probability and increase it, compared to the conventional quantum annealing, by optimizing the pulse parameters. We demonstrate our findings for a single qubit both numerically and analytically. The analytical model is based on the tranfer matrix approach and it is in good agreement with the full numerical results. We also investigate the PQA protocol for multi-qubit cases $i. e.,$ random spin-glass instances, and we present an overall increase of the success probability over the conventional quantum annealing, by optimizing the pulse parameters. Our results indicate that PQA can be used to design future high-performance quantum annealing machines, especially for hard instances that the conventional QA protocol behaves poorly.

arXiv:1806.08517Journal of the Physical Society of JapanAug 17, 20207 citations

Multi-Task Quantum Annealing for Rapid Multi-Class Classification

Jargalsaikhan Artag, Moe Shimada, Jun‐ichi Shirakashi

Quantum computing offers new paradigms in machine learning, notably through Quantum Annealing (QA), which offers promising approaches to optimization problems. This study introduces an application of Multi-tasking Quantum Annealing (MTQA) to enhance multi-class Support Vector Machines (SVMs). Traditional QA methods require multiple cycles to classify multi-class datasets, significantly increasing computational demands. In contrast, MTQA embeds multiple SVM classifiers in parallel on a quantum annealer, thereby reducing the number of required annealing cycles to a single cycle. Utilizing the D-Wave Advantage 6.4 system, we evaluated our method across three benchmark datasets: a synthetic dataset (Blob), subsets of handwritten digits, and the Iris dataset with different feature focuses. Our findings demonstrate that MTQA achieves accuracy equivalent to that of classical Sequential Minimal Optimization (SMO), Simulated Annealing (SA), and standard QA methods while significantly improving computational efficiency. Despite the current limitations in quantum technology and the empirical selection of model parameters, these results highlight the emergent potential of quantum computing in machine learning. Further advancements in quantum algorithm optimization and parameter tuning are anticipated to enhance the efficacy of QA and MTQA, solidifying their practical application roles.Quantum computing offers new paradigms in machine learning, notably through Quantum Annealing (QA), which offers promising approaches to optimization problems. This study introduces an application of Multi-tasking Quantum Annealing (MTQA) to enhance multi-class Support Vector Machines (SVMs). Traditional QA methods require multiple cycles to classify multi-class datasets, significantly increasing computational demands. In contrast, MTQA embeds multiple SVM classifiers in parallel on a quantum annealer, thereby reducing the number of required annealing cycles to a single cycle. Utilizing the D-Wave Advantage 6.4 system, we evaluated our method across three benchmark datasets: a synthetic dataset (Blob), subsets of handwritten digits, and the Iris dataset with different feature focuses. Our findings demonstrate that MTQA achieves accuracy equivalent to that of classical Sequential Minimal Optimization (SMO), Simulated Annealing (SA), and standard QA methods while significantly improving computational efficiency. Despite the current limitations in quantum technology and the empirical selection of model parameters, these results highlight the emergent potential of quantum computing in machine learning. Further advancements in quantum algorithm optimization and parameter tuning are anticipated to enhance the efficacy of QA and MTQA, solidifying their practical application roles.

IEEE International Conference on Quantum Computing and Engineering (QCE)Sep 15, 20241 citation

Assessing the Quantum in Quantum Annealing

Wade DeGottardi, David Ferguson, С. А. Новиков +2 more

Bulletin of the American Physical SocietyMar 7, 20190 citations

Quantum annealing of disordered spin systems

Lukas M. Sieberer, Philipp Hauke, Ehud Altman +1 more

Bulletin of the American Physical SocietyMar 13, 20170 citations

Multi-qubit correction for quantum annealers

Ramin Ayanzadeh, John E. Dorband, Milton Halem +1 more

We present multi-qubit correction (MQC) as a novel postprocessing method for quantum annealers that views the evolution in an open-system as a Gibbs sampler and reduces a set of excited states to a new synthetic state with lower energy value. After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels-i.e., a group of qubits that changing their states simultaneously can result in a new state with lower energy value-and successively converges to the ground state. Experimental results using D-Wave 2000Q quantum annealers demonstrate that MQC finds samples with notably lower energy values and improves the reproducibility of results when compared to recent hardware/software advances in the realm of quantum annealing, such as spin-reversal transforms, classical postprocessing techniques, and increased inter-sample delay between successive measurements.We present multi-qubit correction (MQC) as a novel postprocessing method for quantum annealers that views the evolution in an open-system as a Gibbs sampler and reduces a set of excited states to a new synthetic state with lower energy value. After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels-i.e., a group of qubits that changing their states simultaneously can result in a new state with lower energy value-and successively converges to the ground state. Experimental results using D-Wave 2000Q quantum annealers demonstrate that MQC finds samples with notably lower energy values and improves the reproducibility of results when compared to recent hardware/software advances in the realm of quantum annealing, such as spin-reversal transforms, classical postprocessing techniques, and increased inter-sample delay between successive measurements.

arXiv:2010.00115Scientific ReportsAug 9, 202122 citations

i. e.,

random spin-glass instances, and we present an overall increase of the success probability over the conventional quantum annealing, by optimizing the pulse parameters. Our results indicate that PQA can be used to design future high-performance quantum annealing machines, especially for hard instances that the conventional QA protocol behaves poorly.