Universal Quantum Computer Simulation of 50 Qubits on Europe`s First Exascale Supercomputer Harnessing Its Heterogeneous CPU-GPU Architecture

Andreas Herten; Hans De Raedt; Jiri Kraus; Kristel Michielsen; Markus Hrywniak; Mathis Bode; Thomas Lippert; Vrinda Mehta

arxiv: 2511.03359 · v3 · pith:WVXYEL47new · submitted 2025-11-05 · 🪐 quant-ph · cs.DC· physics.comp-ph

Universal Quantum Computer Simulation of 50 Qubits on Europe`s First Exascale Supercomputer Harnessing Its Heterogeneous CPU-GPU Architecture

Hans De Raedt , Jiri Kraus , Andreas Herten , Vrinda Mehta , Mathis Bode , Markus Hrywniak , Kristel Michielsen , Thomas Lippert This is my paper

Pith reviewed 2026-05-21 20:22 UTC · model grok-4.3

classification 🪐 quant-ph cs.DCphysics.comp-ph

keywords quantum simulationexascale computingGPUqubitshigh-performance computinguniversal quantum computermemory optimization

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The pith

JUQCS-50 simulates 50-qubit universal quantum computers on the JUPITER exascale supercomputer for the first time.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces an updated Jülich universal quantum computer simulator called JUQCS-50 that runs on the heterogeneous CPU-GPU architecture of the JUPITER supercomputer. It reaches 50 qubits by extending memory access through high-bandwidth CPU-GPU links and LPDDR5 memory, applying adaptive data encoding to shrink the memory footprint, and adding an on-the-fly network traffic optimizer. These changes deliver a 16.6-fold speedup compared with the prior 48-qubit record on the K computer. A reader would care because full simulation of 50-qubit circuits brings the behavior of near-term quantum hardware into reach without requiring physical devices.

Core claim

The authors have developed JUQCS-50, which through extending usable memory beyond GPU limits via high-bandwidth CPU-GPU interconnects and LPDDR5 memory, adaptive data encoding to reduce memory footprint with acceptable trade-offs in precision and compute effort, and an on-the-fly network traffic optimizer, enables simulations of a 50-qubit universal quantum computer for the first time and produces a 16.6-fold speedup over the previous 48-qubit record on the K computer.

What carries the argument

The JUQCS-50 simulator, which extends memory via CPU-GPU interconnects, applies adaptive data encoding to cut memory use, and optimizes network traffic on the fly.

If this is right

Larger quantum circuits can now be simulated directly on exascale machines without fitting entirely in GPU memory.
Researchers gain the ability to test 50-qubit algorithms and error-correction schemes on classical hardware before physical devices reach that scale.
The same memory-extension and encoding techniques can be applied to other CPU-GPU supercomputers for quantum simulations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The method opens a path to routine simulation of noisy 50-qubit devices, allowing direct comparison with experimental data from current quantum processors.
If the encoding scheme generalizes, similar gains may appear in classical simulations of other high-dimensional quantum systems such as many-body spin chains.
Performance data from JUPITER could guide hardware designers on the minimum interconnect bandwidth needed for future quantum simulators.

Load-bearing premise

The adaptive data encoding maintains sufficient numerical precision and produces acceptable compute trade-offs for the quantum simulations that the authors consider scientifically relevant.

What would settle it

Compare the output of JUQCS-50 on a 20-qubit circuit whose exact results are already known from smaller-scale runs or analytic formulas; any systematic deviation beyond expected floating-point error would indicate that the precision trade-off has failed.

Figures

Figures reproduced from arXiv: 2511.03359 by Andreas Herten, Hans De Raedt, Jiri Kraus, Kristel Michielsen, Markus Hrywniak, Mathis Bode, Thomas Lippert, Vrinda Mehta.

**Figure 1.** Figure 1: Overview of JUPITER’s quad GH200 node design with included technology and bandwidths. Each node contains four GH200 superchips, [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 3.** Figure 3: Total and compute elapsed times per gate operation for [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 2.** Figure 2: Graphical representation of the benchmark circuit [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 4.** Figure 4: Total elapsed and compute times per gate operation for the [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: Measured communication bandwidth (in GiB/s) as a func [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗

**Figure 7.** Figure 7: Structure of quantum circuit to add three [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗

**Figure 8.** Figure 8: Realization of the quantum adder circuit shown in Fig. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗

read the original abstract

We have developed a new version of the high-performance J\"ulich universal quantum computer simulator (JUQCS-50) that leverages key features of the GH200 superchips as used in the JUPITER supercomputer, enabling simulations of a 50-qubit universal quantum computer for the first time. JUQCS-50 achieves this through three key innovations: (1) extending usable memory beyond GPU limits via high-bandwidth CPU-GPU interconnects and LPDDR5 memory; (2) adaptive data encoding to reduce memory footprint with acceptable trade-offs in precision and compute effort; and (3) an on-the-fly network traffic optimizer. These advances result in a 16.6-fold speedup over the previous 48-qubit record on the K computer

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

They reached 50-qubit universal simulation on new exascale hardware via memory extensions and adaptive encoding, but without quantified precision checks.

read the letter

The main point is that this paper describes the first simulation of a 50-qubit universal quantum computer using an updated version of their JUQCS code on the JUPITER exascale supercomputer. They achieve this with a reported 16.6-fold speedup over the prior 48-qubit record. What the work actually does is adapt the simulator to the GH200 architecture through three changes. First, they extend memory using the CPU-GPU high-bandwidth links and LPDDR5. Second, they introduce adaptive data encoding to reduce the state vector memory needs while accepting some precision and compute costs. Third, they add an optimizer for network traffic during the run. These are concrete steps that leverage the new hardware's features, and the engineering effort to make it work at this scale is the solid part. The soft spot is in the adaptive encoding. It is necessary to fit the larger state, yet the paper gives no specific numbers on precision loss, no fidelity comparisons to double-precision runs, and no tests for error buildup in longer circuits. Without that, it is difficult to know if the 50-qubit results are accurate enough for the kinds of quantum computations people actually want to study. The speedup is benchmarked against an older system, which provides context but does not separate the contributions of each new technique. The overall approach looks reproducible in principle since it builds on prior JUQCS work. This paper will interest people working on large quantum circuit simulations and those optimizing codes for heterogeneous exascale machines. A reader focused on practical scaling limits in quantum emulation could pick up useful implementation ideas. It should go through peer review. The claim of reaching 50 qubits is worth checking in detail, especially the precision aspects, and referees can ask for the supporting data on the encoding scheme.

Referee Report

2 major / 2 minor

Summary. The manuscript presents JUQCS-50, an updated Jülich universal quantum computer simulator that achieves the first simulation of a 50-qubit universal quantum computer on the JUPITER exascale supercomputer. It reports three innovations—extending usable memory via high-bandwidth CPU-GPU interconnects and LPDDR5, adaptive data encoding to shrink the state-vector footprint, and an on-the-fly network traffic optimizer—yielding a 16.6-fold speedup over the prior 48-qubit record on the K computer.

Significance. If the adaptive encoding preserves sufficient fidelity for arbitrary circuits, the result would mark a clear advance in the scale of universal quantum simulation on heterogeneous CPU-GPU systems, extending the practical reach of full-state-vector methods beyond current GPU-memory limits.

major comments (2)

Abstract and Methods: the central claim of a verified 50-qubit universal simulation rests on the adaptive data encoding, yet no quantitative bounds on precision loss, no fidelity-versus-depth curves against a double-precision reference, and no statement of the maximum validated circuit depth or gate set are provided; without these the 50-qubit result cannot be assessed for scientific usability.
Results section (performance comparison): the 16.6-fold speedup is reported against the external K-computer record, but no internal baseline (e.g., full double-precision run on the same JUPITER hardware or a non-adaptive variant) is shown, leaving open whether the speedup is attributable to the new encoding or to architectural differences.

minor comments (2)

Title: the apostrophe in “Europe`s” is typographically incorrect and should be “Europe’s”.
Abstract: the phrase “acceptable trade-offs in precision and compute effort” is used without a definition or metric; a brief parenthetical (e.g., “fidelity > 0.99 for circuits up to depth D”) would clarify the claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and indicate the revisions we will make.

read point-by-point responses

Referee: Abstract and Methods: the central claim of a verified 50-qubit universal simulation rests on the adaptive data encoding, yet no quantitative bounds on precision loss, no fidelity-versus-depth curves against a double-precision reference, and no statement of the maximum validated circuit depth or gate set are provided; without these the 50-qubit result cannot be assessed for scientific usability.

Authors: We agree that explicit quantitative validation of the adaptive encoding is necessary to substantiate the 50-qubit claim. In the revised manuscript we will expand the Methods section with measured bounds on precision loss, fidelity-versus-depth curves for representative circuits against double-precision references, and explicit statements of the maximum validated circuit depth together with the supported gate set. revision: yes
Referee: Results section (performance comparison): the 16.6-fold speedup is reported against the external K-computer record, but no internal baseline (e.g., full double-precision run on the same JUPITER hardware or a non-adaptive variant) is shown, leaving open whether the speedup is attributable to the new encoding or to architectural differences.

Authors: The reported speedup is measured against the published 48-qubit record obtained on different hardware. To isolate the contribution of our techniques we will add internal benchmarks on JUPITER, comparing adaptive versus non-adaptive runs for feasible qubit counts. A full double-precision 50-qubit simulation exceeds available memory, so we will clarify this limitation and supply the strongest feasible internal baselines. revision: partial

Circularity Check

0 steps flagged

No circularity: performance claims benchmarked against external prior record with independent implementation details

full rationale

The paper's central claims rest on engineering innovations for memory extension, adaptive encoding, and network optimization, with the 16.6-fold speedup explicitly measured against an external 48-qubit record on the K computer rather than any self-referential fit or redefinition. No equations or derivations reduce the reported results to parameters defined by the same data; the adaptive encoding is presented as an implementation choice whose precision trade-offs are asserted but not derived from the target simulation outcomes. The work is self-contained against external benchmarks and does not invoke self-citations as load-bearing uniqueness theorems or smuggle ansatzes.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an engineering implementation paper; it introduces no mathematical axioms, free parameters fitted to data, or new postulated entities. The central claim rests on standard assumptions about supercomputer interconnect performance and the acceptability of reduced-precision arithmetic for quantum state vectors.

pith-pipeline@v0.9.0 · 5700 in / 1142 out tokens · 36840 ms · 2026-05-21T20:22:41.176813+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

32 extracted references · 32 canonical work pages · cited by 4 Pith papers · 4 internal anchors

[1]

S. S. Gill, S. Tuli, M. Xu, I. C. Singh, S. Dust- dar, R. Buyya, Quantum computing: A taxonomy, systematic review and future directions, Soft- ware: Practice and Experience 52 (2022) 92–136. doi:10.1002/spe.2958

work page doi:10.1002/spe.2958 2022
[2]

Nielsen, I

M. Nielsen, I. Chuang, Quantum Computation and Quantum Information, 10th anniversary edi- tion ed., Cambridge University Press, Cambridge,

work page
[3]

doi:10.1017/cbo9780511976667. 13

work page doi:10.1017/cbo9780511976667
[4]

De Raedt, K

K. De Raedt, K. Michielsen, H. De Raedt, B. Trieu, G. Arnold, M. Richter, Th. Lippert, H. Watanabe, N. Ito, Massively parallel quan- tum computer simulator, Comp. Phys. Comm. 176 (2007) 121 – 136. doi:10.1016/j.cpc.2006.0 8.007

work page doi:10.1016/j.cpc.2006.0 2007
[5]

URL: https://qiskit.org/

The Qiskit Community, Qiskit: An open-source framework for quantum computing, 2023. URL: https://qiskit.org/

work page 2023
[6]

URL:https: //quantumai.google/cirq

The Cirq Developers, Cirq: A python framework for nisq-era quantum circuits, 2023. URL:https: //quantumai.google/cirq

work page 2023
[7]

URL:https://eviden.c om/solutions/advanced-computing/quant um-computing/qaptiva-hpc/

Eviden, Qaptiva: Quantum application develop- ment platform, 2023. URL:https://eviden.c om/solutions/advanced-computing/quant um-computing/qaptiva-hpc/

work page 2023
[8]

De Raedt, F

H. De Raedt, F. Jin, D. Willsch, M. Willsch, N. Yoshioka, N. Ito, S. Yuan, K. Michielsen, Massively parallel quantum computer simulator, eleven years later, Comp. Phys. Comm. 237 (2019) 47 – 61. doi:10.1016/j.cpc.2018.1 1.005

work page doi:10.1016/j.cpc.2018.1 2019
[9]

Willsch, D

M. Willsch, D. Willsch, F. Jin, H. De Raedt, K. Michielsen, Benchmarking the quantum ap- proximate optimization algorithm, Quantum In- formation Processing 19 (2020). doi:10.1007/s1 1128-020-02692-8

work page doi:10.1007/s1 2020
[10]

Willsch, M

D. Willsch, M. Willsch, F. Jin, K. Michielsen, H. De Raedt, GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm, Comp. Phys. Comm. 278 (2022) 108411. doi:10.1016/j.cpc.2022.108 411

work page doi:10.1016/j.cpc.2022.108 2022
[11]

URL:https:// www.hpcwire.com/2010/06/28/quantum_com puter_simulation_new_world_record_on_j ugene/

HPCwire, Quantum computer simulation: New world record on jugene, 2010. URL:https:// www.hpcwire.com/2010/06/28/quantum_com puter_simulation_new_world_record_on_j ugene/

work page 2010
[12]

URL:https://www

HPCwire, World record: Quantum computer with 46 qubits simulated, 2017. URL:https://www. hpcwire.com/2017/12/18/world-record-q uantum-computer-46-qubits-simulated/

work page 2017
[13]

D. Alvarez, JUWELS cluster and booster: Exas- cale pathfinder with modular supercomputing ar- chitecture at Jülich Supercomputing Centre, Jour- nal of large-scale research facilities JLSRF 7 (2021). doi:10.17815/jlsrf-7-183

work page doi:10.17815/jlsrf-7-183 2021
[14]

quantum supremacy

Y . Liu, X. Liu, F. Li, H. Fu, Y . Yang, J. Song, P. Zhao, Z. Wang, D. Peng, H. Chen, C. Guo, H. Huang, W. Wu, D. Chen, Closing the “quantum supremacy” gap: achieving real-time simulation of a random quantum circuit using a new Sunway supercomputer, in: Proceedings of the Interna- tional Conference for High Performance Comput- ing, Networking, Storage and...

work page doi:10.1145/3458817.3487399 2021
[15]

doi:10 .1038/s41586-019-1666-5

Google AI Quantum, collaborators, Quantum supremacy using a programmable superconduct- ing processor, Nature 574 (2019) 505–510. doi:10 .1038/s41586-019-1666-5

work page 2019
[16]

Willsch, M

D. Willsch, M. Willsch, F. Jin, H. De Raedt, K. Michielsen, Large-scale simulation of Shor’s quantum factoring algorithm, Mathematics 11 (2023) 4222. doi:10.3390/math11194222

work page doi:10.3390/math11194222 2023
[17]

URL:ht tps://docs.nvidia.com/cuda/cuda-c-pro gramming-guide/index.html#data-usage -hints

Cuda c++ programming guide - data usage hints section, Last retrieved on April 1, 2025. URL:ht tps://docs.nvidia.com/cuda/cuda-c-pro gramming-guide/index.html#data-usage -hints

work page 2025
[18]

URL:https: //docs.nvidia.com/cuda/archive/12.4

Cuda 12.4 release notes - general cuda section, Last retrieved on April 1, 2025. URL:https: //docs.nvidia.com/cuda/archive/12.4. 0/cuda-toolkit-release-notes/index.h tml#general-cuda

work page 2025
[19]

Willsch, H

D. Willsch, H. Lagemann, M. Willsch, F. Jin, H. De Raedt, K. Michielsen, Benchmarking su- percomputers with the Jülich Universal Quantum Computer Simulator (2019). doi:10.48550/ARX IV.1912.03243

work page doi:10.48550/arx 2019
[20]

Shor, Polynomial-time algorithms for prime fac- torization and discrete logarithms on a quantum computer, SIAM Review 41 (1999) 303

P. Shor, Polynomial-time algorithms for prime fac- torization and discrete logarithms on a quantum computer, SIAM Review 41 (1999) 303

work page 1999
[21]

T. G. Draper, Addition on a quantum computer, arXiv:quant-ph/0008033 (2000)

work page internal anchor Pith review Pith/arXiv arXiv 2000
[22]

Michielsen, M

K. Michielsen, M. Nocon, D. Willsch, F. Jin, Th. Lippert, H. De Raedt, Benchmarking gate-based quantum computers, Comp. Phys. Comm. 220 (2017) 44 – 55

work page 2017
[23]

Hilfer fractional advection-diffusion equations with power-law initial condition; a Numerical study using variational iteration method

A. Herten, S. Achilles, D. Alvarez, J. Badwaik, E. Behle, M. Bode, T. Breuer, D. Caviedes- V oullième, M. Cherti, A. Dabah, S. E. Sayed, W. Frings, A. Gonzalez-Nicolas, E. B. Gregory, K. H. Mood, T. Hater, J. Jitsev, C. M. John, 14 J. H. Meinke, C. I. Meyer, P. Mezentsev, J.-O. Mirus, S. Nassyr, C. Penke, M. Römmer, U. Sinha, B. v. S. Vieth, O. Stein, E. ...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.1109/sc41406.2024.0 2024
[24]

JUNIQ: The Jülich UNified Infrastructure for Quantum Computing, Last retrieved on April 1,

work page
[25]

URL:https://www.fz-juelich.de/ en/ias/jsc/systems/quantum-computing/j uniq-facility/juniq

work page
[26]

Love, Alán Aspuru-Guzik, and Jeremy L

A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, J. L. O’Brien, A variational eigenvalue solver on a quantum processor, Nature Communications 5 (2014) 4213. doi:10.1038/ncomms5213

work page doi:10.1038/ncomms5213 2014
[27]

J. R. McClean, J. Romero, R. Babbush, A. Aspuru-Guzik, The theory of varia- tional hybrid quantum-classical algorithms, New Journal of Physics 18 (2016) 023023. doi:10.1088/1367-2630/18/2/023023

work page doi:10.1088/1367-2630/18/2/023023 2016
[28]

Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R

M. Cerezo, A. Arrasmith, R. Babbush, S. Ben- jamin, S. Endo, K. Fujii, J. McClean, K. Mitarai, X. Yuan, L. Cincio, P. Coles, Variational quan- tum algorithms, Nature Reviews Physics 3 (2021) 625–644. doi:10.1038/s42254-021-00348-9

work page doi:10.1038/s42254-021-00348-9 2021
[29]

A Quantum Approximate Optimization Algorithm

E. Farhi, J. Goldstone, S. Gutmann, A quan- tum approximate optimization algorithm, arXiv preprint arXiv:1411.4028 (2014). URL:https: //arxiv.org/abs/1411.4028

work page internal anchor Pith review Pith/arXiv arXiv 2014
[30]

Zhou, S.-T

L. Zhou, S.-T. Wang, S. Choi, H. Pichler, M. D. Lukin, Quantum approximate optimization algo- rithm: Performance, mechanism, and implemen- tation on near-term devices, Phys. Rev. X 10 (2020) 021067. URL:https://link.aps.o rg/doi/10.1103/PhysRevX.10.021067. doi:10.1103/PhysRevX.10.021067

work page doi:10.1103/physrevx.10.021067 2020
[31]

P. C. Lotshaw, T. Nguyen, A. Santana, et al., Scal- ing quantum approximate optimization on near- term hardware, Sci. Rep. 12 (2022). URL:https: //doi.org/10.1038/s41598-022-14767-w. doi:10.1038/s41598-022-14767-w

work page doi:10.1038/s41598-022-14767-w 2022
[32]

K. Vyas, F. Jin, H. De Raedt, K. Michielsen, Quantum speed-up for solving the one- dimensional Hubbard model using quantum annealing, arXiv:quant-ph/2510.02141 (2025). 15 Appendix A. Raw performance data Table A.5: Elapsed and compute times for executing Hadamard gates [2] in a sequence designed to challenge both computation and communication on JUPITER, ...

work page internal anchor Pith review Pith/arXiv arXiv 2025

[1] [1]

S. S. Gill, S. Tuli, M. Xu, I. C. Singh, S. Dust- dar, R. Buyya, Quantum computing: A taxonomy, systematic review and future directions, Soft- ware: Practice and Experience 52 (2022) 92–136. doi:10.1002/spe.2958

work page doi:10.1002/spe.2958 2022

[2] [2]

Nielsen, I

M. Nielsen, I. Chuang, Quantum Computation and Quantum Information, 10th anniversary edi- tion ed., Cambridge University Press, Cambridge,

work page

[3] [3]

doi:10.1017/cbo9780511976667. 13

work page doi:10.1017/cbo9780511976667

[4] [4]

De Raedt, K

K. De Raedt, K. Michielsen, H. De Raedt, B. Trieu, G. Arnold, M. Richter, Th. Lippert, H. Watanabe, N. Ito, Massively parallel quan- tum computer simulator, Comp. Phys. Comm. 176 (2007) 121 – 136. doi:10.1016/j.cpc.2006.0 8.007

work page doi:10.1016/j.cpc.2006.0 2007

[5] [5]

URL: https://qiskit.org/

The Qiskit Community, Qiskit: An open-source framework for quantum computing, 2023. URL: https://qiskit.org/

work page 2023

[6] [6]

URL:https: //quantumai.google/cirq

The Cirq Developers, Cirq: A python framework for nisq-era quantum circuits, 2023. URL:https: //quantumai.google/cirq

work page 2023

[7] [7]

URL:https://eviden.c om/solutions/advanced-computing/quant um-computing/qaptiva-hpc/

Eviden, Qaptiva: Quantum application develop- ment platform, 2023. URL:https://eviden.c om/solutions/advanced-computing/quant um-computing/qaptiva-hpc/

work page 2023

[8] [8]

De Raedt, F

H. De Raedt, F. Jin, D. Willsch, M. Willsch, N. Yoshioka, N. Ito, S. Yuan, K. Michielsen, Massively parallel quantum computer simulator, eleven years later, Comp. Phys. Comm. 237 (2019) 47 – 61. doi:10.1016/j.cpc.2018.1 1.005

work page doi:10.1016/j.cpc.2018.1 2019

[9] [9]

Willsch, D

M. Willsch, D. Willsch, F. Jin, H. De Raedt, K. Michielsen, Benchmarking the quantum ap- proximate optimization algorithm, Quantum In- formation Processing 19 (2020). doi:10.1007/s1 1128-020-02692-8

work page doi:10.1007/s1 2020

[10] [10]

Willsch, M

D. Willsch, M. Willsch, F. Jin, K. Michielsen, H. De Raedt, GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm, Comp. Phys. Comm. 278 (2022) 108411. doi:10.1016/j.cpc.2022.108 411

work page doi:10.1016/j.cpc.2022.108 2022

[11] [11]

URL:https:// www.hpcwire.com/2010/06/28/quantum_com puter_simulation_new_world_record_on_j ugene/

HPCwire, Quantum computer simulation: New world record on jugene, 2010. URL:https:// www.hpcwire.com/2010/06/28/quantum_com puter_simulation_new_world_record_on_j ugene/

work page 2010

[12] [12]

URL:https://www

HPCwire, World record: Quantum computer with 46 qubits simulated, 2017. URL:https://www. hpcwire.com/2017/12/18/world-record-q uantum-computer-46-qubits-simulated/

work page 2017

[13] [13]

D. Alvarez, JUWELS cluster and booster: Exas- cale pathfinder with modular supercomputing ar- chitecture at Jülich Supercomputing Centre, Jour- nal of large-scale research facilities JLSRF 7 (2021). doi:10.17815/jlsrf-7-183

work page doi:10.17815/jlsrf-7-183 2021

[14] [14]

quantum supremacy

Y . Liu, X. Liu, F. Li, H. Fu, Y . Yang, J. Song, P. Zhao, Z. Wang, D. Peng, H. Chen, C. Guo, H. Huang, W. Wu, D. Chen, Closing the “quantum supremacy” gap: achieving real-time simulation of a random quantum circuit using a new Sunway supercomputer, in: Proceedings of the Interna- tional Conference for High Performance Comput- ing, Networking, Storage and...

work page doi:10.1145/3458817.3487399 2021

[15] [15]

doi:10 .1038/s41586-019-1666-5

Google AI Quantum, collaborators, Quantum supremacy using a programmable superconduct- ing processor, Nature 574 (2019) 505–510. doi:10 .1038/s41586-019-1666-5

work page 2019

[16] [16]

Willsch, M

D. Willsch, M. Willsch, F. Jin, H. De Raedt, K. Michielsen, Large-scale simulation of Shor’s quantum factoring algorithm, Mathematics 11 (2023) 4222. doi:10.3390/math11194222

work page doi:10.3390/math11194222 2023

[17] [17]

URL:ht tps://docs.nvidia.com/cuda/cuda-c-pro gramming-guide/index.html#data-usage -hints

Cuda c++ programming guide - data usage hints section, Last retrieved on April 1, 2025. URL:ht tps://docs.nvidia.com/cuda/cuda-c-pro gramming-guide/index.html#data-usage -hints

work page 2025

[18] [18]

URL:https: //docs.nvidia.com/cuda/archive/12.4

Cuda 12.4 release notes - general cuda section, Last retrieved on April 1, 2025. URL:https: //docs.nvidia.com/cuda/archive/12.4. 0/cuda-toolkit-release-notes/index.h tml#general-cuda

work page 2025

[19] [19]

Willsch, H

D. Willsch, H. Lagemann, M. Willsch, F. Jin, H. De Raedt, K. Michielsen, Benchmarking su- percomputers with the Jülich Universal Quantum Computer Simulator (2019). doi:10.48550/ARX IV.1912.03243

work page doi:10.48550/arx 2019

[20] [20]

Shor, Polynomial-time algorithms for prime fac- torization and discrete logarithms on a quantum computer, SIAM Review 41 (1999) 303

P. Shor, Polynomial-time algorithms for prime fac- torization and discrete logarithms on a quantum computer, SIAM Review 41 (1999) 303

work page 1999

[21] [21]

T. G. Draper, Addition on a quantum computer, arXiv:quant-ph/0008033 (2000)

work page internal anchor Pith review Pith/arXiv arXiv 2000

[22] [22]

Michielsen, M

K. Michielsen, M. Nocon, D. Willsch, F. Jin, Th. Lippert, H. De Raedt, Benchmarking gate-based quantum computers, Comp. Phys. Comm. 220 (2017) 44 – 55

work page 2017

[23] [23]

Hilfer fractional advection-diffusion equations with power-law initial condition; a Numerical study using variational iteration method

A. Herten, S. Achilles, D. Alvarez, J. Badwaik, E. Behle, M. Bode, T. Breuer, D. Caviedes- V oullième, M. Cherti, A. Dabah, S. E. Sayed, W. Frings, A. Gonzalez-Nicolas, E. B. Gregory, K. H. Mood, T. Hater, J. Jitsev, C. M. John, 14 J. H. Meinke, C. I. Meyer, P. Mezentsev, J.-O. Mirus, S. Nassyr, C. Penke, M. Römmer, U. Sinha, B. v. S. Vieth, O. Stein, E. ...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.1109/sc41406.2024.0 2024

[24] [24]

JUNIQ: The Jülich UNified Infrastructure for Quantum Computing, Last retrieved on April 1,

work page

[25] [25]

URL:https://www.fz-juelich.de/ en/ias/jsc/systems/quantum-computing/j uniq-facility/juniq

work page

[26] [26]

Love, Alán Aspuru-Guzik, and Jeremy L

A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, J. L. O’Brien, A variational eigenvalue solver on a quantum processor, Nature Communications 5 (2014) 4213. doi:10.1038/ncomms5213

work page doi:10.1038/ncomms5213 2014

[27] [27]

J. R. McClean, J. Romero, R. Babbush, A. Aspuru-Guzik, The theory of varia- tional hybrid quantum-classical algorithms, New Journal of Physics 18 (2016) 023023. doi:10.1088/1367-2630/18/2/023023

work page doi:10.1088/1367-2630/18/2/023023 2016

[28] [28]

Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R

M. Cerezo, A. Arrasmith, R. Babbush, S. Ben- jamin, S. Endo, K. Fujii, J. McClean, K. Mitarai, X. Yuan, L. Cincio, P. Coles, Variational quan- tum algorithms, Nature Reviews Physics 3 (2021) 625–644. doi:10.1038/s42254-021-00348-9

work page doi:10.1038/s42254-021-00348-9 2021

[29] [29]

A Quantum Approximate Optimization Algorithm

E. Farhi, J. Goldstone, S. Gutmann, A quan- tum approximate optimization algorithm, arXiv preprint arXiv:1411.4028 (2014). URL:https: //arxiv.org/abs/1411.4028

work page internal anchor Pith review Pith/arXiv arXiv 2014

[30] [30]

Zhou, S.-T

L. Zhou, S.-T. Wang, S. Choi, H. Pichler, M. D. Lukin, Quantum approximate optimization algo- rithm: Performance, mechanism, and implemen- tation on near-term devices, Phys. Rev. X 10 (2020) 021067. URL:https://link.aps.o rg/doi/10.1103/PhysRevX.10.021067. doi:10.1103/PhysRevX.10.021067

work page doi:10.1103/physrevx.10.021067 2020

[31] [31]

P. C. Lotshaw, T. Nguyen, A. Santana, et al., Scal- ing quantum approximate optimization on near- term hardware, Sci. Rep. 12 (2022). URL:https: //doi.org/10.1038/s41598-022-14767-w. doi:10.1038/s41598-022-14767-w

work page doi:10.1038/s41598-022-14767-w 2022

[32] [32]

K. Vyas, F. Jin, H. De Raedt, K. Michielsen, Quantum speed-up for solving the one- dimensional Hubbard model using quantum annealing, arXiv:quant-ph/2510.02141 (2025). 15 Appendix A. Raw performance data Table A.5: Elapsed and compute times for executing Hadamard gates [2] in a sequence designed to challenge both computation and communication on JUPITER, ...

work page internal anchor Pith review Pith/arXiv arXiv 2025