Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Bulletin of the American Physical Society

Scalable device structure for large-scale superconducting quantum annealing machines

Mutsuo Hidaka, Masaaki Maezawa, Kazumasa Makise, Shuichi Nagasawa, Takahiro Yamada, K. Inomata, Go Fujii, Hirotake Yamamori, Masahiro Ukibe, Shiro Kawabata

Published Mar 8, 2018• 0 citations
OpenAlex

Related papers

Flip-chip bonding superconducting connections for scalable superconducting quantum annealing machines

Kazumasa Makise, Mutsuo Hidaka, Hiroshi Nakagawa +4 more

The Japan Society of Applied Physics•Jul 10, 2019•0 citations

Superconducting solder bumping technology for scalable quantum annealing machines

Kazumasa Makise, Masaaki Maezawa, Mutsuo Hidaka +2 more

Bulletin of the American Physical Society•Mar 8, 2018•0 citations

Towards large-scale superconducting quantum annealers: 2.5D packaging technology and application specific architecture

Shiro Kawabata, Kazuhiko Endo, Go Fujii +19 more

Bulletin of the American Physical Society•Mar 8, 2018•0 citations

Temporal information processing on a 4,500-qubit quantum annealer

Antonio Sannia, Roberto Menta, Pratik Sathe +7 more

Quantum machine learning could uncover statistical structure beyond the reach of classical models, but this requires quantum models large and expressive enough to be useful and cheap enough to read out. Most approaches optimize many quantum parameters and are thus limited by expensive training loops. Here we report a quantum machine-learning model implemented on a programmable superconducting quantum annealer that processes temporal data at large scale using up to 4,500 qubits-the largest quantum machine-learning experiment performed to date. Following the quantum reservoir computing paradigm, the untrained native many-body dynamics generated by reverse annealing is directly used to process temporal data. We prove that the interactions produced during annealing are indispensable-a non-interacting reservoir retains no memory of its input. We evaluate our model experimentally on standard memory benchmarks and demonstrate that it can successfully forecast chaotic time series. These results establish quantum annealers as a scalable platform for large-scale quantum machine learning.

quant-pharXiv:2609.19308•Sep 18, 2026•0 citations

Thermal management of a 3D packaging structure for superconducting quantum annealing machines

Wei Feng, Katsuya Kikuchi, Mutsuo Hidaka +4 more

Quantum annealing (QA) is a computing paradigm for solving combinatorial optimization problems by finding the low-energy configurations of complicated Ising models through quantum fluctuation. Recently, because of the strictly limited number of superconducting qubits per chip, three-dimensional (3D) integrated technologies have attracted great attention for realizing a practical large-scale QA machine. Because a cryogenic environment is required for qubit chip operation, the less-effective heat transfer in the stacked structure has become a major concern. In the present paper, a method to realize a sufficiently low temperature for quantum operation in large-scale superconducting QA machines based on 3D packaging technology is proposed and demonstrated numerically. We reveal that the heating in such a 3D structure, which seriously reduces the quantum coherence of qubit chips, is effectively prevented by adding through-silicon vias (TSVs). Accordingly, the temperature of the qubit chip is drastically reduced from 57.8 mK to 16.4 mK. Our results indicate that using TSVs are a powerful and simple approach to realizing practical large-scale superconducting QA machines.

Applied Physics Letters•Apr 26, 2021•12 citations

Design, Manufacturing, and Packaging Technology for Superconducting Quantum Annealing Machines

Shiro Kawabata, Mutsuo Hidaka, Kazumasa Makise +4 more

Journal of The Japan Institute of Electronics Packaging•Aug 31, 2019•0 citations
Quantum machine learning could uncover statistical structure beyond the reach of classical models, but this requires quantum models large and expressive enough to be useful and cheap enough to read out. Most approaches optimize many quantum parameters and are thus limited by expensive training loops. Here we report a quantum machine-learning model implemented on a programmable superconducting quantum annealer that processes temporal data at large scale using up to 4,500 qubits-the largest quantum machine-learning experiment performed to date. Following the quantum reservoir computing paradigm, the untrained native many-body dynamics generated by reverse annealing is directly used to process temporal data. We prove that the interactions produced during annealing are indispensable-a non-interacting reservoir retains no memory of its input. We evaluate our model experimentally on standard memory benchmarks and demonstrate that it can successfully forecast chaotic time series. These results establish quantum annealers as a scalable platform for large-scale quantum machine learning.
Quantum annealing (QA) is a computing paradigm for solving combinatorial optimization problems by finding the low-energy configurations of complicated Ising models through quantum fluctuation. Recently, because of the strictly limited number of superconducting qubits per chip, three-dimensional (3D) integrated technologies have attracted great attention for realizing a practical large-scale QA machine. Because a cryogenic environment is required for qubit chip operation, the less-effective heat transfer in the stacked structure has become a major concern. In the present paper, a method to realize a sufficiently low temperature for quantum operation in large-scale superconducting QA machines based on 3D packaging technology is proposed and demonstrated numerically. We reveal that the heating in such a 3D structure, which seriously reduces the quantum coherence of qubit chips, is effectively prevented by adding through-silicon vias (TSVs). Accordingly, the temperature of the qubit chip is drastically reduced from 57.8 mK to 16.4 mK. Our results indicate that using TSVs are a powerful and simple approach to realizing practical large-scale superconducting QA machines.