A curated list of libraries, projects, tutorials, papers, and other resources on quantum control systems. Organized to help researchers and developers navigate machine-learning and control methods for quantum computing.
- FPGA-tailored algorithms for real-time decoding of quantum LDPC codes - IBM Quantum: We analyze FPGA-tailored versions of three decoder classes for quantum low-density parity-check (qLDPC) codes: message passing, ordered statistics, and clustering.
- A scalable and real-time neural decoder for topological quantum codes: AlphaQubit 2, a neural-network decoder that achieves near-optimal logical error rates for both surface and color codes at scale under realistic noise.
- Local Clustering Decoder as a fast and adaptive hardware decoder for the surface code-Riverlane, Cambridge, UK: Local Clustering Decoder as a solution that simultaneously achieves the accuracy and speed requirements of a real-time decoding system. Our decoder is implemented. on FPGAs and exploits hardware parallelism to keep pace with the fastest qubit types.
- Local active error correction from simulated confinement - Ethan Lake (Berkeley): Studies the problem of performing real-time decoding on topological stabilizer codes in a way where all operations—both classical and quantum—are geometrically local.
- Quantum error correction below the surface code threshold - Google Quantum AI: Distance-7 code and a distance-5 code integrated with a real-time decoder.
- ML-Powered FPGA-based Real-Time Quantum State Discrimination Enabling Mid-circuit Measurements - Berkeley: QubiCML, a field-programmable gate array (FPGA) based system for real-time qubit state discrimination enabling mid-circuit measurement—the ability to measure the qubit state at the electronic control circuit before/without transferring quantum data to a host computer.
- Realizing a deep reinforcement learning agent for real-time quantum feedback - ETH: RL-agent for sub-microsecond latency control to realize the full potential of quantum technologies, which requires precise real-time control on time scales much shorter than the coherence time.
- Reinforcement Learning Control of Quantum Error Correction - Google Quantum AI/DeepMind: Reinforcement learning framework that repurposes error detection events to continuously calibrate quantum control parameters without halting computation.
- [arXiv] Fully autonomous tuning of a spin qubit - ETH: Autonomous tuning of a semiconductor qubit, from a grounded device to Rabi oscillations, integrates deep learning, Bayesian optimization and computer vision techniques. Demonstrate this automation in a germanium–silicon core–shell nanowire device.
- Reinforcement Learning for Quantum Technology - Max Planck Institute: A review of reinforcement learning for quantum control, covering major applications, experiments, and open challenges.
- Millisecond-Scale Calibration and Benchmarking of Superconducting Qubits - Uni of Copenhagen: Closed-loop on-FPGA calibration protocol in ms latency on Quantum Machines. Many techniques enssembled together in order to enable the automatic on-chip loop.
- Silicon spin qubit noise characterization using real-time feedback protocols and wavelet analysis - Applied Physics Letters: benefits and drawbacks of qubit parameter feedback, as feedback related overhead increases
- Real-time two-axis control of a spin qubit: FPGA-based control for full Hamiltonian estimation to dynamically stabilize and optimize qubit performance.
- Suppressing qubit dephasing using real-time Hamiltonian estimation - Harvard: Improve the coherent time of singlet-triplet qubit formed by two gate-defined lateral quantum dots (QDs) in a GaAs/AlGaAs heterostructure by using FPGA-based Hamiltonian parameter estimation.
- Real-Time Adaptive Tracking of Fluctuating Relaxation Rates in Superconducting Qubits - Fabrizio Berritta (Uni of Copenhagen): FPGA-based controller (Bayesian Estimation) that accurately tracks the relaxation time of superconducting qubits and reports that the relaxation time switches orders of magnitude within milisecond timescales as opposed to hours.
- Robust online Hamiltonian learning: Combining Monte Carlo and Bayesian inference for online estimation dynamical parameters of a quantum system.
- Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models - Vinicius Hernandes et al, Delt: Diffusion models to accurately measure charge stability diagrams from sparse measurement.
- A two-dimensional 10-qubit array in germanium with robust and localised qubit control - Delt: Two-dimensional 10-spin qubit array.
- Learning Quantum Systems: Theoretical proposals and successful implementations across different multiple-qubit architectures.
- Model-Free Quantum Control with Reinforcement Learning
- Transversal Algorithmic Fault Tolerance for Low-Overhead Quantum Computing - Harry Zhou, MIT.