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.

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.