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Ground-State Preparation and Energy Estimation on Early Fault-Tolerant Quantum Computers via Quantum Eigenvalue Transformation of Unitary Matrices

Publication ,  Journal Article
Dong, Y; Lin, L; Tong, Y
Published in: PRX Quantum
October 1, 2022

Under suitable assumptions, some recently developed quantum algorithms can estimate the ground-state energy and prepare the ground state of a quantum Hamiltonian with near-optimal query complexities. However, this is based on a block-encoding input model of the Hamiltonian, the implementation of which is known to require a large resource overhead. We develop a tool called quantum eigenvalue transformation of unitary matrices with real polynomials (QETU), which uses a controlled Hamiltonian evolution as the input model, a single ancilla qubit, and no multiqubit control operations and is thus suitable for early fault-tolerant quantum devices. This leads to a simple quantum algorithm that outperforms all previous algorithms with a comparable circuit structure for estimating the ground-state energy. For a class of quantum spin Hamiltonians, we propose a new method that exploits certain anticommutation relations and further removes the need to implement the controlled Hamiltonian evolution. Coupled with a Trotter-based approximation of the Hamiltonian evolution, the resulting algorithm can be very suitable for early fault-tolerant quantum devices. We demonstrate the performance of the algorithm using IBM qiskit for the transverse-field Ising model. If we are further allowed to use multiqubit Toffoli gates, we can then implement amplitude amplification and a new binary amplitude-estimation algorithm, which increases the circuit depth but decreases the total query complexity. The resulting algorithm saturates the near-optimal complexity for ground-state preparation and energy estimation using a constant number of ancilla qubits (no more than three).

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Published In

PRX Quantum

DOI

EISSN

2691-3399

Publication Date

October 1, 2022

Volume

3

Issue

4
 

Published In

PRX Quantum

DOI

EISSN

2691-3399

Publication Date

October 1, 2022

Volume

3

Issue

4