Broughton, Michael

Suppressing quantum errors by scaling a surface code logical qubit

Google Quantum, A. I., Acharya, R., Aleiner, I., Allen, R., Andersen, T. I., Ansmann, M., Arute, F., Arya, K., Asfaw, A., Atalaya, J., et al. (2023) · Nature

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 1,103

Quantum error correction below the surface code threshold

Collaborators, G. Q. A. A., Acharya, R., Abanin, D. A., Aghababaie-Beni, L., Aleiner, I., Andersen, T. I., Ansmann, M., Arute, F., Arya, K., Asfaw, A., et al. (2025) · Nature

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 778

Quantum advantage in learning from experiments

Huang, H.-Y., Broughton, M., Cotler, J., Chen, S., Li, J., Mohseni, M., Neven, H., Babbush, R., Kueng, R., Preskill, J., et al. (2022) · Science

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 519

Exponential suppression of bit or phase errors with cyclic error correction

Google Quantum, A. I., Chen, Z., Satzinger, K. J., Atalaya, J., Korotkov, A. N., Dunsworth, A., Sank, D., Quintana, C., McEwen, M., Barends, R., et al. (2021) · Nature

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 393

Information scrambling in quantum circuits

Mi, X., Roushan, P., Quintana, C., Mandrà, S., Marshall, J., Neill, C., Arute, F., Arya, K., Atalaya, J., Babbush, R., et al. (2021) · Science

Quantum many-body systemsQuantum Computing Algorithms and Architecture

Cited by 283

Phase transition in Random Circuit Sampling

Morvan, A., Villalonga, B., Mi, X., Mandrà, S., Bengtsson, A., Klimov, P. V., Chen, Z., Hong, S., Erickson, C., Drozdov, I. K., et al. (2023) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 138

Non-Abelian braiding of graph vertices in a superconducting processor

Collaborators, G. Q. A. A., Andersen, T. I., Lensky, Y. D., Kechedzhi, K., Drozdov, I. K., Bengtsson, A., Hong, S., Morvan, A., Mi, X., Opremcak, A., et al. (2023) · Nature

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 108

For Fixed Control Parameters the Quantum Approximate Optimization Algorithm's Objective Function Value Concentrates for Typical Instances

Brandao, F. G. S. L., Broughton, M., Farhi, E., Gutmann, S., & Neven, H. (2018) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 88

A quantum algorithm to train neural networks using low-depth circuits

Verdon, G., Broughton, M., & Biamonte, J. (2017) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 87

A Fault-Tolerant Honeycomb Memory

Gidney, C., Newman, M., Fowler, A., & Broughton, M. (2021) · Quantum

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 83

A universal training algorithm for quantum deep learning

Verdon, G., Pye, J., & Broughton, M. (2018) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 68

Scaling and logic in the colour code on a superconducting quantum processor

Lacroix, N., Bourassa, A., Heras, F. J. H., Zhang, L. M., Bausch, J., Senior, A. W., Edlich, T., Shutty, N., Sivak, V., Bengtsson, A., et al. (2025) · Nature

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 51