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Phase transitions in random circuit sampling
by
Huang, T.
, Hansen, M.
, Xing, C.
, Satzinger, K. J.
, Burkett, B.
, Fatemi, R.
, Nguyen, A.
, Debroy, D. M.
, Laws, L.
, Bengtsson, A.
, Juhas, P.
, Villalonga, B.
, Giustina, M.
, Chou, C.
, Zhang, Y.
, Faoro, L.
, Chau, J.
, Kreikebaum, J. M.
, Bushnell, N.
, Gidney, C.
, Kitaev, A.
, Klimov, P. V.
, Megrant, A.
, Arute, F.
, Brill, L.
, Martin, S.
, Young, G.
, Demura, S.
, Livingston, W. P.
, Bourassa, A.
, Iveland, J.
, Omonije, S.
, Babbush, R.
, Burger, T.
, Klots, A. R.
, Petukhov, A.
, Hilton, J.
, Kafri, D.
, Yeh, P.
, Neill, C.
, Zobrist, N.
, Jeffrey, E.
, Mi, X.
, McClean, J. R.
, Woo, B. W. K.
, Erickson, C.
, Jones, C.
, Broughton, M.
, Gosula, R.
, Opremcak, A.
, Buckley, B. B.
, Strain, D.
, Hong, S.
, Bortoli, G.
, Lee, K. W.
, Abanin, D.
, O’Brien, T. E.
, Paolo, A. Di
, Khezri, M.
, Kelly, J.
, Ansmann, M.
, Burgos, L. Flores
, Chik, D.
, Schuster, C.
, Torres, A.
, Dunsworth, A.
, Asfaw, A.
, Laptev, P.
, Huggins, W. J.
, Lill, A. T.
, Shutty, N.
, Malone, F. D.
, Boixo, S.
, Kieferová, M.
, Kim, S.
, Anderson, K.
, Acharya, R.
, Rhodes, D. M.
, Hamilton, M. C.
, Pryadko, L. P.
, Smelyanskiy, V.
, Habegger, S.
, Newman, M.
, Hoffmann, M. R.
, Rubin, N. C.
, Sankaragomathi, K.
, Khattar, T.
, Chen, Z.
, Movassagh, R.
, Lensky,
in
639/766/483/3926
/ 639/766/483/481
/ Algorithms
/ Benchmarks
/ Computer applications
/ Computing costs
/ Cost analysis
/ Entropy
/ Entropy (Information theory)
/ Evolution
/ Experiments
/ Hilbert space
/ Humanities and Social Sciences
/ multidisciplinary
/ Phase transitions
/ Processors
/ Qubits (quantum computing)
/ Sampling
/ Science
/ Science (multidisciplinary)
/ Spoofing
/ Statistical analysis
/ Statistical models
2024
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Phase transitions in random circuit sampling
by
Huang, T.
, Hansen, M.
, Xing, C.
, Satzinger, K. J.
, Burkett, B.
, Fatemi, R.
, Nguyen, A.
, Debroy, D. M.
, Laws, L.
, Bengtsson, A.
, Juhas, P.
, Villalonga, B.
, Giustina, M.
, Chou, C.
, Zhang, Y.
, Faoro, L.
, Chau, J.
, Kreikebaum, J. M.
, Bushnell, N.
, Gidney, C.
, Kitaev, A.
, Klimov, P. V.
, Megrant, A.
, Arute, F.
, Brill, L.
, Martin, S.
, Young, G.
, Demura, S.
, Livingston, W. P.
, Bourassa, A.
, Iveland, J.
, Omonije, S.
, Babbush, R.
, Burger, T.
, Klots, A. R.
, Petukhov, A.
, Hilton, J.
, Kafri, D.
, Yeh, P.
, Neill, C.
, Zobrist, N.
, Jeffrey, E.
, Mi, X.
, McClean, J. R.
, Woo, B. W. K.
, Erickson, C.
, Jones, C.
, Broughton, M.
, Gosula, R.
, Opremcak, A.
, Buckley, B. B.
, Strain, D.
, Hong, S.
, Bortoli, G.
, Lee, K. W.
, Abanin, D.
, O’Brien, T. E.
, Paolo, A. Di
, Khezri, M.
, Kelly, J.
, Ansmann, M.
, Burgos, L. Flores
, Chik, D.
, Schuster, C.
, Torres, A.
, Dunsworth, A.
, Asfaw, A.
, Laptev, P.
, Huggins, W. J.
, Lill, A. T.
, Shutty, N.
, Malone, F. D.
, Boixo, S.
, Kieferová, M.
, Kim, S.
, Anderson, K.
, Acharya, R.
, Rhodes, D. M.
, Hamilton, M. C.
, Pryadko, L. P.
, Smelyanskiy, V.
, Habegger, S.
, Newman, M.
, Hoffmann, M. R.
, Rubin, N. C.
, Sankaragomathi, K.
, Khattar, T.
, Chen, Z.
, Movassagh, R.
, Lensky,
in
639/766/483/3926
/ 639/766/483/481
/ Algorithms
/ Benchmarks
/ Computer applications
/ Computing costs
/ Cost analysis
/ Entropy
/ Entropy (Information theory)
/ Evolution
/ Experiments
/ Hilbert space
/ Humanities and Social Sciences
/ multidisciplinary
/ Phase transitions
/ Processors
/ Qubits (quantum computing)
/ Sampling
/ Science
/ Science (multidisciplinary)
/ Spoofing
/ Statistical analysis
/ Statistical models
2024
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Do you wish to request the book?
Phase transitions in random circuit sampling
by
Huang, T.
, Hansen, M.
, Xing, C.
, Satzinger, K. J.
, Burkett, B.
, Fatemi, R.
, Nguyen, A.
, Debroy, D. M.
, Laws, L.
, Bengtsson, A.
, Juhas, P.
, Villalonga, B.
, Giustina, M.
, Chou, C.
, Zhang, Y.
, Faoro, L.
, Chau, J.
, Kreikebaum, J. M.
, Bushnell, N.
, Gidney, C.
, Kitaev, A.
, Klimov, P. V.
, Megrant, A.
, Arute, F.
, Brill, L.
, Martin, S.
, Young, G.
, Demura, S.
, Livingston, W. P.
, Bourassa, A.
, Iveland, J.
, Omonije, S.
, Babbush, R.
, Burger, T.
, Klots, A. R.
, Petukhov, A.
, Hilton, J.
, Kafri, D.
, Yeh, P.
, Neill, C.
, Zobrist, N.
, Jeffrey, E.
, Mi, X.
, McClean, J. R.
, Woo, B. W. K.
, Erickson, C.
, Jones, C.
, Broughton, M.
, Gosula, R.
, Opremcak, A.
, Buckley, B. B.
, Strain, D.
, Hong, S.
, Bortoli, G.
, Lee, K. W.
, Abanin, D.
, O’Brien, T. E.
, Paolo, A. Di
, Khezri, M.
, Kelly, J.
, Ansmann, M.
, Burgos, L. Flores
, Chik, D.
, Schuster, C.
, Torres, A.
, Dunsworth, A.
, Asfaw, A.
, Laptev, P.
, Huggins, W. J.
, Lill, A. T.
, Shutty, N.
, Malone, F. D.
, Boixo, S.
, Kieferová, M.
, Kim, S.
, Anderson, K.
, Acharya, R.
, Rhodes, D. M.
, Hamilton, M. C.
, Pryadko, L. P.
, Smelyanskiy, V.
, Habegger, S.
, Newman, M.
, Hoffmann, M. R.
, Rubin, N. C.
, Sankaragomathi, K.
, Khattar, T.
, Chen, Z.
, Movassagh, R.
, Lensky,
in
639/766/483/3926
/ 639/766/483/481
/ Algorithms
/ Benchmarks
/ Computer applications
/ Computing costs
/ Cost analysis
/ Entropy
/ Entropy (Information theory)
/ Evolution
/ Experiments
/ Hilbert space
/ Humanities and Social Sciences
/ multidisciplinary
/ Phase transitions
/ Processors
/ Qubits (quantum computing)
/ Sampling
/ Science
/ Science (multidisciplinary)
/ Spoofing
/ Statistical analysis
/ Statistical models
2024
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Journal Article
Phase transitions in random circuit sampling
2024
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Overview
Undesired coupling to the surrounding environment destroys long-range correlations in quantum processors and hinders coherent evolution in the nominally available computational space. This noise is an outstanding challenge when leveraging the computation power of near-term quantum processors
1
. It has been shown that benchmarking random circuit sampling with cross-entropy benchmarking can provide an estimate of the effective size of the Hilbert space coherently available
2
–
8
. Nevertheless, quantum algorithms’ outputs can be trivialized by noise, making them susceptible to classical computation spoofing. Here, by implementing an algorithm for random circuit sampling, we demonstrate experimentally that two phase transitions are observable with cross-entropy benchmarking, which we explain theoretically with a statistical model. The first is a dynamical transition as a function of the number of cycles and is the continuation of the anti-concentration point in the noiseless case. The second is a quantum phase transition controlled by the error per cycle; to identify it analytically and experimentally, we create a weak-link model, which allows us to vary the strength of the noise versus coherent evolution. Furthermore, by presenting a random circuit sampling experiment in the weak-noise phase with 67 qubits at 32 cycles, we demonstrate that the computational cost of our experiment is beyond the capabilities of existing classical supercomputers. Our experimental and theoretical work establishes the existence of transitions to a stable, computationally complex phase that is reachable with current quantum processors.
By implementing random circuit sampling, experimental and theoretical results establish the existence of transitions to a stable, computationally complex phase that is reachable with current quantum processors.
Publisher
Nature Publishing Group UK,Nature Publishing Group
Subject
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