Quantum networks might enable quantum communication and distributed quantum computation. Solid-state defects are promising platforms for such networks, because they provide an optical interface for remote entanglement distribution and a nuclear-spin register to store and process quantum information. A key challenge toward larger networks is to improve the storage of previously generated entangled states during new entanglement generation. Here, we introduce a method that uses “spectator” qubits combined with real-time decision making and feedforward to mitigate dephasing of stored quantum states during remote entanglement sequences. We implement the protocol using a single nitrogen-vacancy (NV) center in diamond and demonstrate improved memory fidelity. Our results show that spectator qubits can improve quantum network memory using minimal overhead and naturally present resources, making them a promising addition for near-term testbeds for quantum networks.

Quantum networks connecting quantum processing nodes via photonic links enable distributed and modular quantum computation. In this framework, quantum gates between remote qubits can be realized using quantum teleportation protocols. The essential requirements for such non-local gates are remote entanglement, local quantum logic within each processor, and classical communication between nodes to perform operations based on measurement outcomes. Here, we demonstrate an unconditional Controlled-NOT quantum gate between remote diamond-based qubit devices. The control and target qubits are Carbon-13 nuclear spins, while NV electron spins enable local logic, readout, and remote entanglement generation. We benchmark the system by creating a Greenberger-Horne-Zeilinger state, showing genuine 4-partite entanglement shared between nodes. Using deterministic logic, single-shot readout, and real-time feed-forward, we implement non-local gates without post-selection. These results demonstrate a key capability for solid-state quantum networks, enabling exploration of distributed quantum computing and testing of complex network protocols on full-stack systems.

Solid-state spin defects are promising qubits for quantum network nodes. A key challenge toward larger networks is creating defects with high yield into nanophotonic devices while maintaining good optical and spin properties. Here, we demonstrate the creation of single V2 centers in nanopillars fabricated from commercial bulk-grown 4H-silicon carbide using a pulsed above-bandgap (UV) laser. We observe an 11-fold increase in the V2 center occurrence after UV laser illumination. These laser-induced V2 centers exhibit narrow optical line widths and spectral diffusion rates comparable to naturally occurring V2 centers in nanopillars of the same material. Furthermore, we measure a spin coherence time of (Formula presented) under dynamical decoupling, consistent with dephasing by the nuclear-spin bath. This demonstration of the in situ, postfabrication generation of coherent V2 centers in nanostructures in widely available bulk-grown 4H-SiC shows the potential for above-bandgap laser illumination for scalable defect creation in integrated photonic devices.

High-throughput characterization often requires estimating parameters and model dimension from experimental data of limited quantity and quality. Such data may result in an ill-posed inverse problem, where multiple sets of parameters and model dimensions are consistent with available data. This ill-posed regime may render traditional machine learning and deterministic methods unreliable or intractable, particularly in high-dimensional, nonlinear, and mixed continuous and discrete parameter spaces. To address these challenges, we present a Bayesian framework that hybridizes several Markov chain Monte Carlo (MCMC) sampling techniques to estimate both parameters and model dimension from sparse, noisy data. By integrating sampling for mixed continuous and discrete parameter spaces, reversible-jump MCMC to estimate model dimension, and parallel tempering to accelerate exploration of complex posteriors, our approach enables principled parameter estimation and model selection in data-limited regimes. We apply our framework to a specific ill-posed problem in quantum information science: recovering the locations and hyperfine couplings of nuclear spins surrounding a spin-defect in a semiconductor from sparse, noisy coherence data. We show that a hybridized MCMC method can recover meaningful posterior distributions over physical parameters using an order of magnitude less data than existing approaches, and we validate our results on experimental measurements. More generally, our work provides a flexible, extensible strategy for solving a broad class of ill-posed inverse problems under realistic experimental constraints.

The advancement and scaling of quantum technology has made the learning and identification of quantum systems and devices in highly multidimensional parameter spaces a pressing task for a variety of applications. In many cases, the integration of real-time feedback control and adaptive choice of measurement settings places strict demands on the speed of this task. Here we present a joint model selection and parameter estimation algorithm that is fast and operable on a large number of model parameters. The algorithm is based on variational Bayesian inference, which approximates the target posterior distribution by optimizing a tractable family of distributions, making it more scalable than exact inference methods relying on sampling and that generally suffer from high variance and computational cost in high-dimensional spaces. We show how a regularizing prior can be used to select between competing models, each comprising a different number of parameters, identifying the simplest model that explains the experimental data. The regularization can further separate the degrees of freedom, e.g., quantum systems in the environment or processes, which contribute to major features in the observed dynamics, with respect to others featuring small coupling, which only contribute to a background. As an application of the introduced framework, we consider the problem of the identification of multiple individual nuclear spins with a single electron spin quantum sensor, relevant for nanoscale nuclear magnetic resonance and for the implementation of multiqubit quantum networking nodes. With the number of environmental spins unknown a priori, our Bayesian approach is able to correctly identify the model, i.e., the number of spins and their couplings. We benchmark the algorithm on both simulated and experimental data, using standard figures of merit, and demonstrate that we can estimate dozens of parameters within minutes. Our methodology is compatible with the implementation of real-time adaptive choice of experimental settings in multiparameter estimation, which has the potential to greatly decrease the estimation time in a variety of metrology, tomography, and characterization tasks by prioritizing the measurements with the highest content of information.

Solid-state quantum registers consisting of optically active electron spins with nearby nuclear spins are promising building blocks for future quantum technologies. For electron spin-1 registers, dynamical decoupling (DD) quantum gates have been developed that enable the precise control of multiple nuclear spin qubits. However, for the important class of electron spin-1/2 systems, this control method suffers from intrinsic selectivity limitations, resulting in reduced nuclear spin gate fidelities. Here, we demonstrate improved control of single nuclear spins by an electron spin-1/2 using dynamically decoupled radio-frequency (DDRF) gates. We make use of the electron spin-1/2 of a diamond tin-vacancy center, showing high-fidelity single-qubit gates, single-shot readout, and spin coherence beyond a millisecond. The DD control is used as a benchmark to observe and control a single 31C nuclear spin. Using the DDRF control method, we demonstrate improved control on that spin. In addition, we find and control an additional nuclear spin that is insensitive to the DD control method. Using these DDRF gates, we show entanglement between the electron and the nuclear spin with 72(3)% state fidelity. Our extensive simulations indicate that DDRF gate fidelities well in excess are feasible. Finally, we employ time-resolved photon detection during readout to quantify the hyperfine coupling for the electron's optically excited state. Our work provides key insights into the challenges and opportunities for nuclear spin control in electron spin-1/2 systems, opening the door to multiqubit experiments on these promising qubit platforms.

Color-center quantum bits (qubits), such as the Nitrogen-Vacancy center (NV) in diamond, have demonstrated entanglement between remote (>1.3km) qubits and excellent coherence times [1], all while operating at a few Kelvins. Compared to other qubit technologies typically operating at mK temperatures, the higher operating temperature of NVs enables scalable 3D integration with cryo-CMOS control electronics [2], provides significantly more cooling power, and removes the interconnect bottleneck between the qubits and the electronics in prior art [3-5]. Yet, no cryo-CMOS controller for NV-based quantum computers (QC) has been demonstrated.

We realize high-fidelity gates for the two-qubit system formed by NV center. Using gate set tomography, we report gate fidelities exceeding 99%, and analyze the origin of the errors.

Striving toward a scalable quantum processor, this article presents the first cryo-CMOS quantum bit (qubit) controller targeting color centers in diamond. Color-center qubits enable a modular architecture that allows for the 3-D integration of photonics, cryo-CMOS control electronics, and qubits in the same package. However, performing quantum operations in a scalable manner requires large currents in the driving coils due to low coil-to-qubit coupling. Moreover, active calibration of the qubit Larmor frequency is required to compensate inhomogeneities of the bias magnetic field. To overcome these challenges, this work proposes both a cryo-CMOS alternating current (AC) controller consisting of a class-DE series-resonant driver and a DC current regulator (DC CR) that uses a triode-biased H-bridge for scalable low-power qubit operations. By experimentally validating the cryo-CMOS performance with a nitrogen-vacancy (NV) color-center qubit, the AC controller can drive a Rabi oscillation up to 2.5 MHz with a supply draw of 6.5 mA, and the DC CR can tune the Larmor frequency by ±9 MHz while driving up to ±20 mA in the bias coil. T ^∗ ₂ coherence times up to 5.3μs and single-qubit gate fidelities above 98% are demonstrated with the cryo-CMOS control using Ramsey experiments and gate set tomography (GST), respectively. The results demonstrate the efficacy of the proposed cryo-CMOS chips and enable the development of a modular quantum processor based on color centers.

Nuclear quadrupolar resonance (NQR) spectroscopy reveals chemical bonding patterns in materials and molecules through the unique coupling between nuclear spins and local fields. However, traditional NQR techniques require macroscopic ensembles of nuclei to yield a detectable signal, which obscures molecule-to-molecule variations. Solid-state spin qubits, such as the nitrogen-vacancy (NV) center in diamond, facilitate the detection and control of individual nuclei through their local magnetic couplings. Here, we use NV centers to perform NQR spectroscopy on their associated nitrogen-14 (¹⁴N) nuclei at room temperature. In mapping the nuclear quadrupolar Hamiltonian, we resolve minute variations between individual nuclei. The measurements reveal correlations between the Hamiltonian parameters associated with the NV center’s electronic and nuclear spin states, as well as a previously unreported symmetry-breaking quadrupolar term. We further design pulse sequences to initialize, read out, and control the quantum evolution of the ¹⁴N nuclear state using the nuclear quadrupolar Hamiltonian.

We discuss measurements on single NV centers in isotopically purified diamond and show coherent optical transitions combined with enhanced electron and carbon spin coherence. These results open avenues for new quantum network applications.

In the search for scalable, fault-tolerant quantum computing, distributed quantum computers are promising candidates. These systems can be realized in large-scale quantum networks or condensed onto a single chip with closely situated nodes. We present a framework for numerical simulations of a memory channel using the distributed toric surface code, where each data qubit of the code is part of a separate node, and the error-detection performance depends on the quality of four-qubit Greenberger-Horne-Zeilinger (GHZ) states generated between the nodes. We quantitatively investigate the effect of memory decoherence and evaluate the advantage of GHZ creation protocols tailored to the level of decoherence. We do this by applying our framework for the particular case of color centers in diamond, employing models developed from experimental characterization of nitrogen-vacancy centers. For diamond color centers, coherence times during entanglement generation are orders of magnitude lower than coherence times of idling qubits. These coherence times represent a limiting factor for applications, but previous surface code simulations did not treat them as such. Introducing limiting coherence times as a prominent noise factor makes it imperative to integrate realistic operation times into simulations and incorporate strategies for operation scheduling. Our model predicts error probability thresholds for gate and measurement reduced by at least a factor of three compared to prior work with more idealized noise models. We also find a threshold of 4 × 10 2 in the ratio between the entanglement generation and the decoherence rates, setting a benchmark for experimental progress.