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Implementation Schemes for the Factorized Quantum Lattice-Gas Algorithm for the One Dimensional Diffusion Equation using Persistent-Current Qubits

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David M. Berns, T. P. Orlando

Quantum Physics · Submitted 13 Jan 2005

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We present two experimental schemes that can be used to implement the Factorized Quantum Lattice-Gas Algorithm for the 1D Diffusion Equation with Persistent-Current Qubits.

how it is stated · asserted 0.82 read the record →

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arXiv IDquant-ph/0501071 (version 1)
Pith ID2ZOYLDFD, Pith's permanent record of this paper and its review
CitationDavid M. Berns, T. P. Orlando. "Implementation Schemes for the Factorized Quantum Lattice-Gas Algorithm for the One Dimensional Diffusion Equation using Persistent-Current Qubits." arXiv:quant-ph/0501071 (2005). https://pith.science/paper/2ZOYLDFD
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,571 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.88 · human participants 0.02 · controlled comparison 0.06 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.59 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We present two experimental schemes that can be used to implement the Factorized Quantum Lattice-Gas Algorithm for the 1D Diffusion Equation with Persistent-Current Qubits. One scheme involves biasing the PC Qubit at multiple flux bias points throughout the course of the algorithm. An implementation analogous to that done in Nuclear Magnetic Resonance Quantum Computing is also developed. Errors due to a few key approximations utilized are discussed and differences between the PC Qubit and NMR systems are highlighted.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present two experimental schemes that can be used to implement the Factorized Quantum Lattice-Gas Algorithm for the 1D Diffusion Equation with Persistent-Current Qubits. contribution 1.00 0.90 0.36 asserted 0.82
s1 One scheme involves biasing the PC Qubit at multiple flux bias points throughout the course of the algorithm. method 0.93 0.78 0.06 asserted 1.00
s2 An implementation analogous to that done in Nuclear Magnetic Resonance Quantum Computing is also developed. method 0.58 0.87 0.06 asserted 1.00
s3 Errors due to a few key approximations utilized are discussed and differences between the PC Qubit and NMR systems are highlighted. limitation 0.84 0.80 0.10 asserted 0.98

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from argument 0.70.

What was examined, and what was not

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  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We present two experimental schemes that can be used to implement the Factorized Quantum Lattice-Gas Algorithm for the 1D Diffusion Equation with Persistent-Current Qubits. One scheme involves biasing the PC Qubit at multiple flux bias points throughout the course of the algorithm. An implementation analogous to that done in Nuclear Magnetic Resonance Quantum Computing is also developed. Errors due to a few key approximations utilized are discussed and differences between the PC Qubit and NMR systems are highlighted.

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