A new ground-state energy estimation benchmark rates SHCI, DMRG, and double-factorized QPE, reporting near-universal SHCI solvability from an ML extrapolation that its own empirical table only partially supports.
Low-Overhead Parallelisation of LCU via Commuting Operators
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abstract
The Linear Combination of Unitaries (LCU) method is a powerful scheme for the block encoding of operators but suffers from high overheads. In this work, we discuss the parallelisation of LCU and in particular the SELECT subroutine of LCU based on partitioning of observables into groups of commuting operators, as well as the use of adaptive circuits and teleportation that allow us to perform required Clifford circuits in constant depth. We additionally discuss the parallelisation of QROM circuits which are a special case of our main results, and provide methods to parallelise the action of multi-controlled gates on the control register. We only require an $O(\log n)$ factor increase in the number of qubits in order to produce a significant depth reduction, with prior work suggesting that for molecular Hamiltonians, the depth saving is $O(n)$, and numerics indicating depth savings of a factor approximately $n/2$. The implications of our method in the fault-tolerant setting are also considered, noting that parallelisation reduces the $T$-depth by the same factor as the logical algorithm, without changing the $T$-count, and that our method can significantly reduce the overall space-time volume of the computation, even when including the increased number of $T$ factories required by parallelisation.
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QB Ground State Energy Estimation Benchmark
A new ground-state energy estimation benchmark rates SHCI, DMRG, and double-factorized QPE, reporting near-universal SHCI solvability from an ML extrapolation that its own empirical table only partially supports.