Machine-Learning-Assisted Manipulation and Readout of Molecular Spin Qubits

Bonizzoni, Claudio; Tincani, Mirco; Santanni, Fabio; Affronte, Marco

doi:10.1103/physrevapplied.18.064074

Cited by 5 publications

(7 citation statements)

References 85 publications

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“…Note that in the reward function √ F , instead of F , is used, because it gives higher rewards since 0 ≤ F ≤ 1, leading to faster convergence to the optimal policies during training. Note that in Equation (13), the actual fidelity is used for comparison with the target threshold.…”

Section: Reward Functionmentioning

confidence: 99%

“…The metric that is usually used for measuring the success of a state transition is the fidelity (F ) of the final state with respect to the target state. There are also other types of machine learning, such as supervised learning, which have been used to solve quantum control problems, like qubit characterization [12] and qubit manipulation and readout [13]. This article only deals with RL methods.…”

Section: Introductionmentioning

confidence: 99%

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Control of Qubit Dynamics Using Reinforcement Learning

Koutromanos,

Stefanatos,

Paspalakis

2024

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The progress in machine learning during the last decade has had a considerable impact on many areas of science and technology, including quantum technology. This work explores the application of reinforcement learning (RL) methods to the quantum control problem of state transfer in a single qubit. The goal is to create an RL agent that learns an optimal policy and thus discovers optimal pulses to control the qubit. The most crucial step is to mathematically formulate the problem of interest as a Markov decision process (MDP). This enables the use of RL algorithms to solve the quantum control problem. Deep learning and the use of deep neural networks provide the freedom to employ continuous action and state spaces, offering the expressivity and generalization of the process. This flexibility helps to formulate the quantum state transfer problem as an MDP in several different ways. All the developed methodologies are applied to the fundamental problem of population inversion in a qubit. In most cases, the derived optimal pulses achieve fidelity equal to or higher than 0.9999, as required by quantum computing applications. The present methods can be easily extended to quantum systems with more energy levels and may be used for the efficient control of collections of qubits and to counteract the effect of noise, which are important topics for quantum sensing applications.

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Section: Reward Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Control of Qubit Dynamics Using Reinforcement Learning

Koutromanos,

Stefanatos,

Paspalakis

2024

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“…1a), on which we have already integrated molecular spin ensembles 72,73 and implemented microwave sequences for spin manipulation 53,55 . The Continuous Wave (CW) and Pulsed Wave (PW) microwave spectroscopy of VO(TPP) through the resonator has been previously reported in 53,57 , while the one for BDPA has been previously reported in 55 (see also Supplementary Section 2.1). The sample and the resonator are cooled-down to 3K into a commercial Quantum Design Physical Properties Measurement System (QD PPMS), which is also used to apply the external static magnetic field [53][54][55] .…”

Section: Samples and Resonatormentioning

confidence: 99%

“…The experimental setup is essentially the microwave heterodyne spectrometer previously reported in 53,57 in which one channel of the waveform generator is used to generate the microwave excitation tone through frequency upconversion. The other channel of the waveform generator is used to generate a RF excitation, which is routed directly to the copper coil with a dedicated RF coaxial line.…”

Section: Set Up For the Generation Of Sensing Protocolsmentioning

confidence: 99%

Quantum sensing of magnetic fields with molecular spins

Bonizzoni,

Ghirri,

Santanni

et al. 2024

npj Quantum Inf

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Spins are prototypical systems with the potential to probe magnetic fields down to the atomic scale limit. Exploiting their quantum nature through appropriate sensing protocols allows to enlarge their applicability to fields not always accessible by classical sensors. Here we first show that quantum sensing protocols for AC magnetic fields can be implemented with molecular spin ensembles embedded into hybrid quantum circuits. We then show that, using only echo detection at microwave frequency and no optical readout, Dynamical Decoupling protocols synchronized with the AC magnetic fields can enhance sensitivity up to S ≈ 10−10 − 10−9 T Hz−1/2 with a low (4-5) number of applied pulses. These results paves the way for the development of strategies to exploit molecular spins as quantum sensors.

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“…Magnetic molecules characterized by good spin coherence properties are appealing candidates for improving the quantum logical units of quantum computers [2,3]. They can work as qubits when two electronic spin levels are employed to encode the information (e.g., the m S = ±1/2 projections along the quantization axis of an S = 1/2 system) [4,5] or as qudits when more electronic and nuclear spin levels are involved [6,7].…”

Section: Introductionmentioning

confidence: 99%

Structural and Magnetic Properties of the {Cr(pybd)3[Cu(cyclen)]2}(BF4)4 Heteronuclear Complex

et al. 2023

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Heterotopic ligands containing chemically different binding centers are appealing candidates for obtaining heteronuclear metal complexes. By exploiting this strategy, it is possible to introduce different paramagnetic centers characterized by specific anisotropic magnetic properties that make them distinguishable when weakly magnetically coupled. This molecular approach has great potential to yield multi-spin adducts capable of mimicking logical architectures necessary for quantum information processing (QIP), i.e., quantum logic gates. A possible route for including a single-ion magnetic center within a finite-sized heterometallic compound uses the asymmetric (1-pyridyl)-butane-1,3-dione (pybd) ligand reported in the literature for obtaining Cr3+−Cu2+ metallo-cages. To avoid the formation of cages, we adopted the cyclen (1,4,7,10-tetraazacyclododecane) ligand as a “capping” agent for the Cu2+ ions. We report here the structural and magnetic characterization of the unprecedented adduct {Cr(pybd)3[Cu(cyclen)]2}(BF4)4, whose structure is characterized by a central Cr3+ ion in a distorted octahedral coordination environment and two peripheral Cu2+ ions with square-pyramidal coordination geometries. As highlighted by Continuous Wave Electron Paramagnetic Resonance (EPR) spectroscopy and Direct Current (DC) magnetometry measurements, this adduct shows negligible intramolecular magnetic couplings, and it maintains the characteristic EPR signals of Cr3+ and Cu2+ moieties when diluted in frozen solutions.

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Machine-Learning-Assisted Manipulation and Readout of Molecular Spin Qubits

Cited by 5 publications

References 85 publications

Control of Qubit Dynamics Using Reinforcement Learning

Control of Qubit Dynamics Using Reinforcement Learning

Quantum sensing of magnetic fields with molecular spins

Structural and Magnetic Properties of the {Cr(pybd)3[Cu(cyclen)]2}(BF4)4 Heteronuclear Complex

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