Exploring Quantum Properties and Applications of Spin Qubits in Diamond and Molecules
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li-boningli-phd-physics-2026-thesis.pdf
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21.06 MB
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43511e522538b600ccc3ad1368c3eab8
Author(s)
Li, Boning
Advisor(s)
Cappellaro, Paola
Date Issued
May 2026
Publisher
Massachusetts Institute of Technology
Abstract
Quantum technologies and quantum information science promise unprecedented capabilities in computation, sensing, and communication. Realizing these capabilities requires the development of robust quantum platforms tailored to specific information-processing tasks. Among the leading candidates, electronic and nuclear spins in solid-state systems offer an attractive combination of controllability, robustness, and accessibility. In particular, nitrogenvacancy (NV) centers in diamond provide excellent quantum control and long coherence times under ambient conditions, while molecular spin systems offer rich internal structure and exceptional tunability through chemical design. In this thesis, we explore and integrate these platforms to advance both the fundamental understanding and practical applications of spin-based quantum systems. The work is organized around three main directions: understanding decoherence mechanisms in molecular spin qubits, developing hybrid NV–molecular platforms, and enabling quantum sensing and information processing through experimental and computational techniques. First, we develop a combined experimental and theoretical framework to investigate transverse relaxation in molecular spin systems, using copper(II) phthalocyanine (CuPc) as a model platform. By combining pulsed electron paramagnetic resonance (EPR) spectroscopy with cluster correlation expansion (CCE) simulations, we quantitatively disentangle the contributions of nuclear spins, spin–lattice relaxation, and electron–electron dipolar interactions. We find that nuclear spins contribute negligibly to decoherence, while dipolar interactions between electron spins dominate T2 through instantaneous and spectral diffusion, even at moderate spin densities. This framework provides quantitative design guidelines for molecular spin qubits and establishes a transferable methodology for predicting coherence in complex spin environments. Second, we investigate hybrid quantum platforms that combine paramagnetic molecular spins with NV centers in diamond. Molecular systems offer tunability but lack efficient singlespin control, whereas NV centers provide optical addressability and high sensitivity but face scalability challenges due to stochastic formation. To bridge these complementary strengths, we develop a hybrid NV–molecular approach in which shallow NV centers act as local probes of nearby molecular spins. Using T1 relaxometry, we detect and characterize the electron spin ensemble of a CuPc thin film under ambient conditions. Through quantitative modeling of the NV–molecule interaction, we extract key spin parameters inaccessible to conventional EPR and achieve nanometer-scale estimation of NV depth. These results establish the feasibility of hybrid NV–molecular systems and lay the groundwork for their coherent control. Third, we explore molecular qubits based on optically addressable transient electronic states, exemplified by photoexcited pentacene molecules. Despite their finite lifetimes, these systems enable sensing capabilities beyond conventional solid-state defects. We demonstrate microwave vector magnetometry at zero magnetic field and room temperature, and identify nuclear-spin-induced low-frequency noise as the dominant decoherence source. By developing a phase-alternated control protocol, we suppress this noise and extend coherence by more than an order of magnitude, achieving sensitivities of 1 µT / √ Hz with sub-micrometer spatial resolution. We further enhance coherence via nuclear spin hyperpolarization, establishing molecular triplet systems as a powerful platform for quantum sensing. Finally, to extend these advances across disciplines, we develop complementary computational and algorithmic approaches. We introduce a denoising framework for NV dynamical decoupling data that improves the accuracy of extracting nuclear spin coupling parameters. We also propose a communication-efficient quantum algorithm for estimating vector inner products, a key primitive in modern machine learning. Together, these results advance the understanding, control, and application of spin-based quantum systems, and establish new pathways toward scalable and versatile quantum technologies.
MIT Department
Massachusetts Institute of Technology. Department of Physics
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