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This research analyzes resource trade-offs between double-factorization and sparse-single-factorization methods within mixed block encodings for quantum chemistry simulations on fault-tolerant hardware. The study concludes that hybrid approaches rarely lower total Toffoli counts compared to optimized pure circuits, except in specific regimes where logical qubit overhead is the primary cost metric. These findings establish critical circuit-level conditions required for such mixed encoding strategies to offer genuine computational advantages.
Quantum resource trade-offs of mixed sparse single and double factorization for qubitization-based phase estimation
arXiv:2611.05603v1 Announce Type: new Abstract: We study a mixed block encoding of quantum-chemistry Hamiltonians for quantum phase estimation (QPE) on fault-tolerant quantum computers. Following a Cholesky decomposition of the electron repulsion integral tensor, we assign each Cholesky tensor to either a double-factorization (DF) branch or a sparse-single-factorization (SPSF) branch. This freedom permits a trade-off between reducing the block-encoding normalization factor $\lambda$ with DF and reducing the coefficient data loaded through quantum read-only memory (QROM) with SPSF. Resource estimates across molecular benchmarks show, however, that the mixed circuit does not generally reduce the Toffoli count relative to the less expensive pure circuit. The main obstacles are the overhead of loading substantial data through separate branch-specific QROMs and the limited benefit of mixing when the pure DF and SPSF circuits have dissimilar costs. Modest savings occur only in restricted parameter regimes, most clearly when logical-qubit requirements are included in the cost metric. These results identify the circuit-level conditions that a useful mixed block encoding must satisfy.
Abstract:We study a mixed block encoding of quantum-chemistry Hamiltonians for quantum phase estimation (QPE) on fault-tolerant quantum computers. Following a Cholesky decomposition of the electron repulsion integral tensor, we assign each Cholesky tensor to either a double-factorization (DF) branch or a sparse-single-factorization (SPSF) branch. This freedom permits a trade-off between reducing the block-encoding normalization factor $\lambda$ with DF and reducing the coefficient data loaded through quantum read-only memory (QROM) with SPSF. Resource estimates across molecular benchmarks show, however, that the mixed circuit does not generally reduce the Toffoli count relative to the less expensive pure circuit. The main obstacles are the overhead of loading substantial data through separate branch-specific QROMs and the limited benefit of mixing when the pure DF and SPSF circuits have dissimilar costs. Modest savings occur only in restricted parameter regimes, most clearly when logical-qubit requirements are included in the cost metric. These results identify the circuit-level conditions that a useful mixed block encoding must satisfy.
arXiv:2611.05603 [quant-ph]
(or arXiv:2611.05603v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2611.05603 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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