001     1040944
005     20250325202233.0
024 7 _ |a 10.18420/SE2025-WS-24
|2 doi
024 7 _ |a 10.18420/se2025-ws-24
|2 doi
037 _ _ |a FZJ-2025-02066
041 _ _ |a English
100 1 _ |a Wadewitz, Victoria
|0 P:(DE-Juel1)198911
|b 0
|e Corresponding author
|u fzj
111 2 _ |a 2nd Quantum Software Engineering Meetup (QSE’25)
|c Karlsruhe
|d 2025-02-22 - 2025-02-28
|w Germany
245 _ _ |a Diagrammatic Quantum Circuit Compression for Hamiltonian Simulation
260 _ _ |c 2025
|b Gesellschaft für Informatik, Bonn
295 1 0 |a Software Engineering 2025 – Companion Proceedings
300 _ _ |a 223-255
336 7 _ |a CONFERENCE_PAPER
|2 ORCID
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
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336 7 _ |a Output Types/Conference Paper
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336 7 _ |a Contribution to a conference proceedings
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336 7 _ |a Contribution to a book
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520 _ _ |a One of the promising applications for early quantum computers is the simulation of of dynamical quantum systems. Due to the limited coherence time of such devices, the depth-compression of quantum circuits is crucial to facilitate useful results. It has been shown that certain quantum models can even be compressed to constant depth, meaning it is only linearly dependent on the number of qubits, but independent of the simulation time and the number of Trotter steps. This has been done by extracting the circuit structure derived from the model characteristics via Hamiltonian simulation. Based on these results, we present a diagrammatic approach to circuit compression utilizing a powerful technique for reasoning about quantum circuits called ZX-calculus. We demonstrate our approach by deriving constant-depth circuit compressions for quantum models known to be constant-depth, as well as novel models previously unstudied. Our method could serve as a first step toward the development of more advanced circuit compression methods, that could be employed to enable Hamiltonian simulation of a larger variety of quantum models, and beyond.
536 _ _ |a 5215 - Towards Quantum and Neuromorphic Computing Functionalities (POF4-521)
|0 G:(DE-HGF)POF4-5215
|c POF4-521
|f POF IV
|x 0
588 _ _ |a Dataset connected to DataCite
650 _ 7 |a Quantum Computing
|2 Other
650 _ 7 |a Circuit Compression
|2 Other
650 _ 7 |a Hamiltonian Simulation
|2 Other
650 _ 7 |a NISQ
|2 Other
650 _ 7 |a Phase Gadgets
|2 Other
650 _ 7 |a ZX-calculus
|2 Other
700 1 _ |a Szasz, Aaron
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Camps, Daan
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Klymko, Katherine
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Stollenwerk, Tobias
|0 P:(DE-Juel1)194697
|b 4
|u fzj
773 _ _ |a 10.18420/se2025-ws-24
909 C O |o oai:juser.fz-juelich.de:1040944
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910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)198911
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
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|b 4
|6 P:(DE-Juel1)194697
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-521
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Quantum Materials
|9 G:(DE-HGF)POF4-5215
|x 0
914 1 _ |y 2025
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)PGI-12-20200716
|k PGI-12
|l Quantum Computing Analytics
|x 0
980 _ _ |a contrib
980 _ _ |a VDB
980 _ _ |a contb
980 _ _ |a I:(DE-Juel1)PGI-12-20200716
980 _ _ |a UNRESTRICTED


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