Optimizing Counterdiabaticity by Variational Quantum Circuits

Utilizing counterdiabatic (CD) driving - aiming at suppression of diabatic transition - in digitized adiabatic evolution have garnered immense interest in quantum protocols and algorithms. However, improving the approximate CD terms with a nested commutator ansatz is a challenging task. In this work, we propose a technique of finding optimal coefficients of the CD terms using a variational quantum circuit. By classical optimizations routines, the parameters of this circuit are optimized to provide the coefficients corresponding to the CD terms. Then their improved performance is exemplified in Greenberger-Horne-Zeilinger state preparation on nearest-neighbor Ising model. Finally, we also show the advantage over the usual quantum approximation optimization algorithm, in terms of fidelity with bounded time.Comment: 7 pages, 5 figures, accepted for publication in the upcoming theme issue of Philosophical Transactions

Digitized-Counterdiabatic Quantum Algorithm for Protein Folding

We propose a hybrid classical-quantum digitized-counterdiabatic algorithm to tackle the protein folding problem on a tetrahedral lattice. Digitized-counterdiabatic quantum computing is a paradigm developed to compress quantum algorithms via the digitization of the counterdiabatic acceleration of a given adiabatic quantum computation. Finding the lowest energy configuration of the amino acid sequence is an NP-hard optimization problem that plays a prominent role in chemistry, biology, and drug design. We outperform state-of-the-art quantum algorithms using problem-inspired and hardware-efficient variational quantum circuits. We apply our method to proteins with up to 9 amino acids, using up to 17 qubits on quantum hardware. Specifically, we benchmark our quantum algorithm with Quantinuum's trapped ions, Google's and IBM's superconducting circuits, obtaining high success probabilities with low-depth circuits as required in the NISQ era

Portfolio optimization with digitized counterdiabatic quantum algorithms

We consider digitized-counterdiabatic quantum computing as an advanced paradigm to approach quantum advantage for industrial applications in the NISQ era. We apply this concept to investigate a discrete meanvariance portfolio optimization problem, showing its usefulness in a key finance application. Our analysis shows a drastic improvement in the success probabilities of the resulting digital quantum algorithm when approximate counterdiabatic techniques are introduced. Along these lines, we discuss the enhanced performance of our methods over variational quantum algorithms like QAOA and DC-QAOA.This work is supported by NSFC (Grant No. 12075145) , STCSM (Grant No. 2019SHZDZX01-ZX04) , EU FET Open Grant EPIQUS (No. 899368) , QUANTEK project (Grant No. KK-2021/00070) , the Basque Government through Grant No. IT1470-22, the project Grant No. PID2021-126273NB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe and ERDF Invest in your Future and the Ramon y Cajal program (Grant No. RYC-2017-22482) . F. A. -A. acknowledges ANID Subvencion a la Instalacion en la Academia SA77210018 ANID Proyecto Basal AFB 180001. Authors would also like to acknowledge the Azure quantum credits program for providing access to the Quantinuum H1 emulator