High-precision and low-depth quantum algorithm design for eigenstate problems

TL;DR

This paper introduces a quantum algorithm that efficiently estimates eigenstates and eigenenergies with high precision, low circuit depth, and robustness to noise, suitable for practical quantum hardware implementations.

Contribution

It presents a full-stack quantum algorithm design with logarithmic precision dependence and near-optimal system-size scaling, improving practical eigenstate estimation on quantum devices.

Findings

Achieves $ ilde{O} ( ext{log} rac{1}{ ext{precision}})$ gate complexity per circuit.

Demonstrates high-precision eigenenergy estimation on IBM quantum devices.

Shows robustness to noise in practical quantum computations.

Abstract

Estimating the eigenstate properties of quantum systems is a long-standing, challenging problem for both classical and quantum computing. Existing universal quantum algorithms typically rely on ideal and efficient query models (e.g. time evolution operator or block encoding of the Hamiltonian), which, however, become suboptimal for actual implementation at the quantum circuit level. Here, we present a full-stack design of quantum algorithms for estimating the eigenenergy and eigenstate properties, which can achieve high precision and good scaling with system size. The gate complexity per circuit for estimating generic Hamiltonians' eigenstate properties is $\tilde{O} (\log \varepsilon^{-1})$, which has a logarithmic dependence on the inverse precision $\varepsilon$. For lattice Hamiltonians, the circuit depth of our design achieves near-optimal system-size scaling, even with local qubit connectivity. Our full-stack algorithm has low overhead in circuit compilation, which thus results in a small actual gate count (CNOT and non-Clifford gates) for lattice and molecular problems compared to advanced eigenstate algorithms. The algorithm is implemented on IBM quantum devices using up to 2,000 two-qubit gates and 20,000 single-qubit gates, and achieves high-precision eigenenergy estimation for Heisenberg-type Hamiltonians, demonstrating its noise robustness.