Digitized Counterdiabatic Quantum Algorithm for Protein Folding

Chandarana, Pranav; Hegade, Narendra N.; Montalban, Iraitz; Solano, E.; Chen, Xi

doi:10.1103/physrevapplied.20.014024

Cited by 28 publications

(2 citation statements)

References 50 publications

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“…As another development, the power of single-layer QAOA [102] and improvement by higher-order counterdiabatic terms [103] were confirmed, and the theory was extended to photonic systems with continuous variables [104]. As practical applications, portfolio optimization [85] and protein folding [105] were discussed.…”

Section: Performance Improvementmentioning

confidence: 99%

Shortcuts to adiabaticity: theoretical framework, relations between different methods, and versatile approximations

Hatomura

2024

J. Phys. B: At. Mol. Opt. Phys.

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Shortcuts to adiabaticity guide given systems to final destinations of adiabatic control via fast tracks. Various methods were proposed as varieties of shortcuts to adiabaticity. Basic theory of shortcuts to adiabaticity was established in the 2010s, but it has still been developing and many fundamental findings have been reported. In this Topical Review, we give a pedagogical introduction to theory of shortcuts to adiabaticity and revisit relations between different methods. Some versatile approximations in counterdiabatic driving, which is one of the methods of shortcuts to adiabaticity, will be explained in detail. We also summarize recent progress in studies of shortcuts to adiabaticity.

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Section: Performance Improvementmentioning

confidence: 99%

Shortcuts to adiabaticity: theoretical framework, relations between different methods, and versatile approximations

Hatomura

2024

J. Phys. B: At. Mol. Opt. Phys.

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“…In addition, several strategies partly inspired by diabatic quantum computation have been ported to the QAOA language to further enhance its performance [60,61]. Successful results have been achieved, for instance, by adding unitaries generated by a pool of local operators [62], adding XX-YY interactions [63], using reinforcement learning [64,65], qubit-dependent angles like in multi-angle QAOA [66], or, ultimately, taking into account Trotterization errors via next-order Baker-Hausdorff-Campbell (BHC) expansion [67,68]. The latter scheme has been named digitized-counterdiabatic (CD) QAOA since it exploits operators arising from the Lie algebra of H X and H T to improve the algorithm, similarly to the nested commutator expansion of the adiabatic gauge potential used for shortcuts to adiabaticity in the adiabatic computing paradigm [69].…”

Section: Introductionmentioning

confidence: 99%

Convergence of digitized-counterdiabatic QAOA: circuit depth versus free parameters

Vizzuso,

Passarelli,

Cantele

et al. 2024

New J. Phys.

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Recently, Digitized-Counterdiabatic (CD) Quantum Approximate Optimization Algorithm (QAOA) has been proposed to make QAOA converge to the solution of an optimization problem in fewer steps, inspired by Trotterized counterdiabatic driving in continuous-time quantum annealing. In this paper, we critically revisit this approach by focusing on the paradigmatic weighted and unweighted one-dimensional MaxCut problem. We study two variants of QAOA with first and second-order CD corrections. Our results show that, indeed, higher order CD corrections allow for a quicker convergence to the exact solution of the problem at hand by increasing the complexity of the variational cost function. Remarkably, however, the total number of free parameters needed to achieve this result is independent of the particular QAOA variant analyzed for the problems considered.

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Automated quantum software engineering

Sarkar

2024

Autom Softw Eng

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As bigger quantum processors with hundreds of qubits become increasingly available, the potential for quantum computing to solve problems intractable for classical computers is becoming more tangible. Designing efficient quantum algorithms and software in tandem is key to achieving quantum advantage. Quantum software engineering is challenging due to the unique counterintuitive nature of quantum logic. Moreover, with larger quantum systems, traditional programming using quantum assembly language and qubit-level reasoning is becoming infeasible. Automated Quantum Software Engineering (AQSE) can help to reduce the barrier to entry, speed up development, reduce errors, and improve the efficiency of quantum software. This article elucidates the motivation to research AQSE (why), a precise description of such a framework (what), and reflections on components that are required for implementing it (how).

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Digitized Counterdiabatic Quantum Algorithm for Protein Folding

Cited by 28 publications

References 50 publications

Shortcuts to adiabaticity: theoretical framework, relations between different methods, and versatile approximations

Shortcuts to adiabaticity: theoretical framework, relations between different methods, and versatile approximations

Convergence of digitized-counterdiabatic QAOA: circuit depth versus free parameters

Automated quantum software engineering

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