Optimal control of quantum thermal machines using machine learning

Khait, Ilia; Carrasquilla, Juan; Segal, Dvira

doi:10.1103/physrevresearch.4.l012029

Cited by 37 publications

(20 citation statements)

References 68 publications

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“…Tasks that do not require a change of entropy may still benefit from it, for example by reaching the target while actively cooling [316,415]. (c) Quantum control can be used to optimize the operation cycle of heat engines and refrigerators [168,213,323,626]. (d) Experimental realizations of quantum information control and quantum heat devices share common platforms [269,332,372,442,495,595].…”

Section: Quantum Thermodynamicsmentioning

confidence: 99%

See 1 more Smart Citation

Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe

Koch,

Boscain,

Calarco

et al. 2022

Preprint

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Quantum optimal control, a toolbox for devising and implementing the shapes of external fields that accomplish given tasks in the operation of a quantum device in the best way possible, has evolved into one of the cornerstones for enabling quantum technologies. The last few years have seen a rapid evolution and expansion of the field. We review here recent progress in our understanding of the controllability of open quantum systems and in the development and application of quantum control techniques to quantum technologies. We also address key challenges and sketch a roadmap for future developments.

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Section: Quantum Thermodynamicsmentioning

confidence: 99%

“…There is a dispute if to associate a cost to this coherence [1,330,563]. To overcome this cost, control methods were applied, for example a combination of dynamic programming, machine learning and STA [213,323].…”

Section: Quantum Thermodynamicsmentioning

confidence: 99%

Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe

Koch,

Boscain,

Calarco

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Outside these regimes, specific cycle structures have been considered [38][39][40][41][42][43], such as the Otto cycle [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59]. Shortcuts to adiabaticity [60][61][62][63][64][65][66][67][68] and variational strategies [69][70][71] have been employed. The impact of quantum effects on the performance of QTMs is not straightforward.…”

Section: Introductionmentioning

confidence: 99%

Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning

Erdman¹,

Noé²

2022

Preprint

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The optimal control of non-equilibrium open quantum systems is a challenging task but has a key role in improving existing quantum information processing technologies. We introduce a general model-free framework based on Reinforcement Learning to identify out-of-equilibrium thermodynamic cycles that are Pareto optimal trade-offs between power and efficiency for quantum heat engines and refrigerators. The method does not require any knowledge of the quantum thermal machine, nor of the system model, nor of the quantum state. Instead, it only observes the heat fluxes, so it is both applicable to simulations and experimental devices. We test our method identifying Pareto-optimal trade-offs between power and efficiency in two systems: an experimentally realistic refrigerator based on a superconducting qubit, where we identify non-intuitive control sequences that reduce quantum friction and outperform previous cycles proposed in literature; and a heat engine based on a quantum harmonic oscillator, where we find cycles with an elaborate structure that outperform the optimized Otto cycle.

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“…For the two-qubit gates, such as the controlled-not (CNOT) gate, the time costs with optimal control have theoretically given bounds [20][21][22]. For the N -qubit gates with N > 2, such bounds are not rigorously given in most cases, and variational methods including the machine learning (ML) techniques are used in the optimal-control problems [23][24][25][26][27][28][29][30][31][32][33]. Besides, the quantum many-body systems have also been used to im-plement the measurement-based quantum computation [34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Circuit encapsulation for efficient quantum computing based on controlled many-body dynamics

Lü¹,

Peng-Fei²,

Fei³

et al. 2022

Preprint

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Controlling the time evolution of interacting spin systems is an important approach of implementing quantum computing. Different from the approaches by compiling the circuits into the product of multiple elementary gates, we here propose the quantum circuit encapsulation (QCE), where we encapsulate the circuits into different parts, and optimize the magnetic fields to realize the unitary transformation of each part by the time evolution. The QCE is demonstrated to possess well-controlled error and time cost, which avoids the error accumulations by aiming at finding the shortest path directly to the target unitary. We test four different encapsulation ways to realize the multi-qubit quantum Fourier transformations by controlling the time evolution of the quantum Ising chain. The scaling behaviors of the time costs and errors against the number of two-qubit controlled gates are demonstrated. The QCE provides an alternative compiling scheme that translates the circuits into a physicallyexecutable form based on the quantum many-body dynamics, where the key issue becomes the encapsulation way to balance between the efficiency and flexibility.

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Optimal control of quantum thermal machines using machine learning

Cited by 37 publications

References 68 publications

Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe

Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe

Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning

Circuit encapsulation for efficient quantum computing based on controlled many-body dynamics

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