Shortcuts to Adiabaticity in Digitized Adiabatic Quantum Computing

Hegade, Narendra N.; Paul, Koushik; Ding, Yongcheng; Sanz, Mikel; Albarrán-Arriagada, F.; Solano, E.; Chen, Xi

doi:10.1103/physrevapplied.15.024038

Cited by 82 publications

(58 citation statements)

References 63 publications

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“…In the quantum mechanical time-evolution, the topic of shortcut to adiabaticity has been intensively studied to find protocols for transitions between ground states [1][2][3][4]. The technique and concept have been successfully applied and extended in providing better protocols in quantum computing [5][6][7], optimizing the atom cooling [8][9][10] and improving the performance of microscopic heat engines [11][12][13][14]. Similarly, finding shortcuts in thermodynamic transformations is also a fundamental problem.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Approach to Shortcuts between Thermodynamic States with Extra Constraints

2021

Preprint

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We propose a systematic method based on reinforcement learning (RL) techniques to find the optimal path that can minimize the total entropy production between two equilibrium states of open systems at the same temperature in a given fixed time period. Benefited from the generalization of the deep RL techniques, our method can provide a powerful tool to address this problem in quantum systems even with two-dimensional continuous controllable parameters. We successfully apply our method on the classical and quantum two-level systems.

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Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Approach to Shortcuts between Thermodynamic States with Extra Constraints

2021

Preprint

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“…The counterdiabatic (CD) technique, borrowing from shortcuts to adiabaticity (STA) [19,20], was introduced to speed up adiabatic evolutions by adding Hamiltonian terms to suppress the non-adiabatic transitions [21][22][23][24]. Recently, a number of developments have shown the advantage of CD techniques in digitized-adiabatic quantum computing [25][26][27][28], quantum annealing [29][30][31][32][33], and quantum approximate optimization algorithms (QAOA) [34][35][36]. In this manuscript, we propose digitized-counterdiabatic quantum optimization (DCQO) as a novel paradigm to solve the general class of combinatorial optimization problems with quantum speed-up.…”

mentioning

confidence: 99%

“…We remark that we derive a general analytical expression for the CD coefficients, so that one does not have to calculate them explicitly for each case. Finally, we follow the gate-model approach for the digitized-adiabatic quantum evolution [25,40], allowing the digital implementation of ar-bitrary non-stoquastic CD terms in current noisy intermediatescale quantum (NISQ) computers. Consequently, the proposed DCQO paradigm will be useful to solve the general class of combinatorial optimization problems with quantum speed-up in the NISQ era.…”

mentioning

confidence: 99%

“…Along with that, the lack of flexibility in realizing arbitrary interactions is one of the main drawbacks of analog quantum computers and quantum annealers. To overcome all these problems, we used digitized-adiabatic quantum computing techniques [25,40], which provides the flexibility to introduce arbitrary multi-qubit and non-stoquastic interactions. The model is also consistent with error correction [47], and error mitigation techniques are being developed for NISQ computers [48].…”

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confidence: 99%

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Digitized-Counterdiabatic Quantum Optimization

Hegade¹,

Chen²,

Solano³

2022

Preprint

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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.

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“…For example, mixed quantum-classical algorithms have been proposed in the goal of solving unaffordable quantum chemistry problems with quantum computers [1][2][3][4][5][6][7][8][9][10]. A variational quantum eigensolver (VQE) was successfully implemented in the determination of electronic states for a hydrogen molecule and multi-atom hydrogen chains [1][2][3][4][5].…”

mentioning

confidence: 99%

Experimental Determination of Multi-Qubit Ground State via a Cluster Mean-Field Algorithm

Zhan¹,

Run²,

Zong³

et al. 2021

Preprint

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A quantum eigensolver is designed under a multi-layer cluster mean-field (CMF) algorithm by partitioning a quantum system into spatially-separated clusters. For each cluster, a reduced Hamiltonian is obtained after a partial average over its environment cluster. The products of eigenstates from different clusters construct a compressed Hilbert space, in which an effective Hamiltonian is diagonalized to determine certain eigenstates of the whole Hamiltonian. The CMF method is numerically verified in multi-spin chains and experimentally studied in a fully-connected three-spin network, both yielding an excellent prediction of their ground states.

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Shortcuts to Adiabaticity in Digitized Adiabatic Quantum Computing

Cited by 82 publications

References 63 publications

Reinforcement Learning Approach to Shortcuts between Thermodynamic States with Extra Constraints

Reinforcement Learning Approach to Shortcuts between Thermodynamic States with Extra Constraints

Digitized-Counterdiabatic Quantum Optimization

Experimental Determination of Multi-Qubit Ground State via a Cluster Mean-Field Algorithm

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