Kavli Affiliate: Jing Wang
| First 5 Authors: Shengbin Wang, Peng Wang, Guihui Li, Shubin Zhao, Dongyi Zhao
| Summary:
Great efforts have been dedicated in recent years to explore practical
applications for noisy intermediate-scale quantum (NISQ) computers, which is a
fundamental and challenging problem in quantum computing. As one of the most
promising methods, the variational quantum eigensolver (VQE) has been
extensively studied. In this paper, VQE is applied to solve portfolio
optimization problems in finance by designing two hardware-efficient Dicke
state ansatze that reach a maximum of 2n two-qubit gate depth and n^2/4
parameters, with n being the number of qubits used. Both ansatze are
partitioning-friendly, allowing for the proposal of a highly scalable
quantum/classical hybrid distributed computing (HDC) scheme. Combining
simultaneous sampling, problem-specific measurement error mitigation, and
fragment reuse techniques, we successfully implement the HDC experiments on the
superconducting quantum computer Wu Kong with up to 55 qubits. The simulation
and experimental results illustrate that the restricted expressibility of the
ansatze, induced by the small number of parameters and limited entanglement, is
advantageous for solving classical optimization problems with the cost function
of the conditional value-at-risk (CVaR) for the NISQ era and beyond.
Furthermore, the HDC scheme shows great potential for achieving quantum
advantage in the NISQ era. We hope that the heuristic idea presented in this
paper can motivate fruitful investigations in current and future quantum
computing paradigms.
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