May 18 – 22, 2026
Virginia Tech
America/New_York timezone

Efficient LCU block encodings through Dicke states preparation

May 19, 2026, 11:25 AM
25m
Goodwin Hall 115 (Virginia Tech)

Goodwin Hall 115

Virginia Tech

Minisymposium Talk Quantum Numerical Linear Algebra Quantum Numerical Linear Algebra

Speaker

Roel Van Beeumen (Lawrence Berkeley National Laboratory)

Description

With the Quantum Singular Value Transformation (QSVT) emerging as a unifying framework for diverse quantum speedups, the efficient construction of block encodings—their fundamental input model—has become increasingly crucial. However, devising explicit block encoding circuits remains a significant challenge. A widely adopted strategy is the Linear Combination of Unitaries (LCU) method. While general, its practical utility is often limited by substantial gate overhead. To address this, we introduce the Fast One-Qubit-Controlled Select LCU (FOQCS-LCU), a compact LCU formulation that requires only a linear number of ancilla qubits and is explicitly decomposed into one- and two-qubit gates. By exploiting the underlying Hamiltonian structure, we design a parametrized family of efficient Dicke state preparation routines, enabling systematic realization of the state preparation oracle at substantially reduced gate cost. The check matrix formalism further yields a constant-depth SELECT oracle, implemented as two fully parallelizable layers of singly controlled Pauli gates. We construct explicit block encoding circuits for representative spin models such as the Heisenberg and spin glass Hamiltonians and provide detailed, non-asymptotic gate counts. Our numerical benchmarks confirm the efficiency of the FOQCS-LCU approach, illustrating over an order-of-magnitude reduction in CNOT count compared to conventional LCU. This framework opens a pathway toward practical, low-depth block encodings for a broad class of structured matrices beyond those considered here.

Authors

Filippo Della Chiara (KU Leuven) Martina Nibbi (Technical University of Munich) Yizhi Shen (Lawrence Berkeley National Laboratory) Roel Van Beeumen (Lawrence Berkeley National Laboratory)

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