Digitized-Counterdiabatic Quantum Optimization

TL;DR

This paper introduces digitized-counterdiabatic quantum optimization (DCQO), a method that enhances adiabatic quantum algorithms using non-stoquastic terms, potentially enabling quantum speed-up for complex optimization problems on NISQ devices.

Contribution

The paper proposes a novel digitized approach to counterdiabatic quantum optimization that outperforms traditional methods and is adaptable to current quantum hardware.

Findings

Polynomial enhancement in success probability with simple 2-local counterdiabatic terms.

Flexibility to introduce arbitrary non-stoquastic interactions in gate-based quantum computing.

Potential to achieve quantum speed-up for NP-hard problems on NISQ devices.

Abstract

We propose digitized-counterdiabatic quantum optimization (DCQO) to achieve polynomial enhancement over adiabatic quantum optimization for the general Ising spin-glass model, which includes the whole class of combinatorial optimization problems. This is accomplished via the digitization of adiabatic quantum algorithms that are catalysed by the addition of non-stoquastic counterdiabatic terms. The latter are suitably chosen, not only for escaping classical simulability, but also for speeding up the performance. Finding the ground state of a general Ising spin-glass Hamiltonian is used to illustrate that the inclusion of k-local non-stoquastic counterdiabatic terms can always outperform the traditional adiabatic quantum optimization with stoquastic Hamiltonians. In particular, we show that a polynomial enhancement in the ground-state success probability can be achieved for a finite-time evolution, even with the simplest 2-local counterdiabatic terms. Furthermore, the considered digitization process, within the gate-based quantum computing paradigm, provides the flexibility to introduce arbitrary non-stoquastic interactions. Along these lines, using our proposed paradigm on current NISQ computers, quantum speed-up may be reached to find approximate solutions for NP-complete and NP-hard optimization problems. We expect DCQO to become a fast-lane paradigm towards quantum advantage in the NISQ era.