Measurement reduction in variational quantum algorithms
Andrew Zhao, Andrew Tranter, William M. Kirby, Shu Fay Ung, Akimasa, Miyake, and Peter Love

TL;DR
This paper introduces a measurement reduction technique for variational quantum algorithms using unitary partitioning, achieving significant term reduction and speedups for various Hamiltonian types on NISQ hardware.
Contribution
It develops a novel measurement reduction method based on unitary partitioning and asymmetric qubitization, enabling efficient Hamiltonian term reduction in variational quantum algorithms.
Findings
Achieves constant factor speedup for lattice and random Pauli Hamiltonians.
Proves linear term reduction is always possible for electronic structure Hamiltonians.
Reduces the number of terms by about an order of magnitude for 10-30 qubit systems.
Abstract
Variational quantum algorithms are promising applications of noisy intermediate-scale quantum (NISQ) computers. These algorithms consist of a number of separate prepare-and-measure experiments that estimate terms in a Hamiltonian. The number of terms can become overwhelmingly large for problems at the scale of NISQ hardware that may soon be available. We approach this problem from the perspective of contextuality, and use unitary partitioning (developed independently by Izmaylov et al. [J. Chem. Theory Comput. 16, 190 (2020)]) to define variational quantum eigensolver procedures in which additional unitary operations are appended to the ansatz preparation to reduce the number of terms. This approach may be scaled to use all coherent resources available after ansatz preparation. We also study the use of asymmetric qubitization to implement the additional coherent operations with lower…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum and electron transport phenomena · Quantum Information and Cryptography
