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Designing efficient and robust quantum control strategies is vital for developing quantum technologies. One recent strategy is the Quantum Alternating Operator Ansatz (QAOA) sequence that alternatively propagates under two noncommuting Hamiltonians, whose control parameters can be optimized to generate a gate or prepare a state. Here, we describe the design of a QAOA sequence to prepare long-lived singlet states (LLSs) from the thermal state in NMR. With extraordinarily long lifetimes exceeding the spin-lattice relaxation time constant T1, LLSs have been of great interest for various applications, from spectroscopy to medical imaging. Accordingly, designing sequences for efficiently preparing LLS in a general spin system is crucial. Using numerical analysis, we study the efficiency and robustness of our QAOA sequence over a wide range of errors in the control parameters. Using a two-qubit NMR register, we conduct an experimental study to benchmark our QAOA sequence against other prominent methods of LLS preparation and observe superior performance, especially under noisy conditions. Finally, we numerically demonstrate the applicability of our QAOA sequence beyond two-qubit registers, specifically for polychromatic excitation of delocalized LLS in a six-proton system.
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http://dx.doi.org/10.1063/5.0261878 | DOI Listing |
J Chem Phys
June 2025
Department of Physics and NMR Research Center, Indian Institute of Science Education and Research, Pune 411008, India.
Designing efficient and robust quantum control strategies is vital for developing quantum technologies. One recent strategy is the Quantum Alternating Operator Ansatz (QAOA) sequence that alternatively propagates under two noncommuting Hamiltonians, whose control parameters can be optimized to generate a gate or prepare a state. Here, we describe the design of a QAOA sequence to prepare long-lived singlet states (LLSs) from the thermal state in NMR.
View Article and Find Full Text PDFSci Adv
May 2024
Global Technology Applied Research, JPMorgan Chase, New York, NY 10017, USA.
The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tackle classically intractable problems remains unclear. Here, we perform an extensive numerical investigation of QAOA on the low autocorrelation binary sequences (LABS) problem, which is classically intractable even for moderately sized instances.
View Article and Find Full Text PDFPLoS One
September 2021
Department of Informatics Engineering, Faculty of Engineering, University of Porto, Porto, Portugal.
In this article, we present QuASeR, a reference-free DNA sequence reconstruction implementation via de novo assembly on both gate-based and quantum annealing platforms. This is the first time this important application in bioinformatics is modeled using quantum computation. Each one of the four steps of the implementation (TSP, QUBO, Hamiltonians and QAOA) is explained with a proof-of-concept example to target both the genomics research community and quantum application developers in a self-contained manner.
View Article and Find Full Text PDFPLoS One
May 2019
Department of Computer Science, North Carolina State University, Raleigh, NC, United States of America.
Tensor networks are powerful factorization techniques which reduce resource requirements for numerically simulating principal quantum many-body systems and algorithms. The computational complexity of a tensor network simulation depends on the tensor ranks and the order in which they are contracted. Unfortunately, computing optimal contraction sequences (orderings) in general is known to be a computationally difficult (NP-complete) task.
View Article and Find Full Text PDFJ Magn Reson Imaging
June 2018
Department of Pediatric Cardiology and Congenital Heart Disease, Deutsches Herzzentrum München an der Technischen Universität München, München, Germany.
Background: Aortopulmonary collateral flow is considered to have significant impact on the outcome of patients with single ventricle circulation and total cavopulmonary connection (TCPC). There is little information on collateral flow during exercise.
Purpose: To quantify aortopulmonary collateral flow at rest and during continuous submaximal exercise in clinical patients doing well with TCPC.