Randomised composite linear-combination-of-unitaries: its role in quantum simulation and observable estimation

• URL: http://arxiv.org/abs/2506.15658v1
• Date: Wed, 18 Jun 2025 17:36:01 GMT
• Title: Randomised composite linear-combination-of-unitaries: its role in quantum simulation and observable estimation
• Authors: Jinzhao Sun, Pei Zeng,
• Abstract summary: We discuss the role of randomised linear-combination-of-unitaries (LCU) in quantum simulations.<n>We show how to construct an unbiased estimator of the effective (unphysical) state $U rho Vdagger$ and its generalisation.<n>Our results reveal a natural connection between randomised LCU algorithms and shadow tomography.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Randomisation is widely used in quantum algorithms to reduce the number of quantum gates and ancillary qubits required. A range of randomised algorithms, including eigenstate property estimation by spectral filters, Hamiltonian simulation, and perturbative quantum simulation, though motivated and designed for different applications, share common features in the use of unitary decomposition and Hadamard-test-based implementation. In this work, we start by analysing the role of randomised linear-combination-of-unitaries (LCU) in quantum simulations, and present several quantum circuits that realise the randomised composite LCU. A caveat of randomisation, however, is that the resulting state cannot be deterministically prepared, which often takes an unphysical form $U \rho V^\dagger$ with unitaries $U$ and $V$. Therefore, randomised LCU algorithms are typically restricted to only estimating the expectation value of a single Pauli operator. To address this, we introduce a quantum instrument that can realise a non-completely-positive map, whose feature of frequent measurement and reset on the ancilla makes it particularly suitable in the fault-tolerant regime. We then show how to construct an unbiased estimator of the effective (unphysical) state $U \rho V^\dagger$ and its generalisation. Moreover, we demonstrate how to effectively realise the state prepared by applying an operator that admits a composite LCU form. Our results reveal a natural connection between randomised LCU algorithms and shadow tomography, thereby allowing simultaneous estimation of many observables efficiently. As a concrete example, we construct the estimators and present the simulation complexity for three use cases of randomised LCU in Hamiltonian simulation and eigenstate preparation tasks.

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