Integrated tool-set for Control, Calibration and Characterization of quantum devices applied to superconducting qubits

Abstract

Efforts to scale-up quantum computation have reached a point where the principal limiting factor is not the number of qubits, but the entangling gate infidelity. However, the highly detailed system characterization required to understand the underlying error sources is an arduous process and impractical with increasing chip size. Open-loop optimal control techniques allow for the improvement of gates but are limited by the models they are based on. To rectify the situation, we provide an integrated open-source tool-set for Control, Calibration and Characterization, capable of open-loop pulse optimization, model-free calibration, model fitting and refinement. We present a methodology to combine these tools to find a quantitatively accurate system model, high-fidelity gates and an approximate error budget, all based on a high-performance, feature-rich simulator. We illustrate our methods using simulated fixed-frequency superconducting qubits for which we learn model parameters with less than 1% error and derive a coherence limited cross-resonance (CR) gate that achieves 99.6% fidelity without need for calibration.

Source code available at http://q-optimize.org; added references; updated with journal version; updated references

References in corpus (20)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Randomized Benchmarking of Quantum Gates
• Simple pulses for elimination of leakage in weakly nonlinear qubits
• Robust randomized benchmarking of quantum processes
• Decoherence benchmarking of superconducting qubits
• Randomized benchmarking and process tomography for gate errors in a solid-state qubit
• Process verification of two-qubit quantum gates by randomized benchmarking
• Robust Online Hamiltonian Learning
• 1/f Flux Noise in Josephson Phase Qubits
• Coupling strength estimation for spin chains despite restricted access
• Hamiltonian tomography in an access-limited setting without state initialization
• Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation
• Identifying an Experimental Two-State Hamiltonian to Arbitrary Accuracy
• Benchmarking the noise sensitivity of different parametric two-qubit gates in a single superconducting quantum computing platform
• Hamiltonian identifiability assisted by single-probe measurement
• Gate-error analysis in simulations of quantum computers with transmon qubits
• Identification of open quantum systems from observable time traces
• Quantum Bootstrapping via Compressed Quantum Hamiltonian Learning
• Multi-parameter estimation along quantum trajectories with Sequential Monte Carlo methods
• Simultaneous model selection and parameter estimation: A superconducting qubit coupled to a bath of incoherent two-level systems

Cited by in corpus (46)

• Introduction to the Pontryagin Maximum Principle for Quantum Optimal Control
• Learning Quantum Systems
• Experimental Deep Reinforcement Learning for Error-Robust Gateset Design on a Superconducting Quantum Computer
• Pulse-efficient circuit transpilation for quantum applications on cross-resonance-based hardware
• Pulser: An open-source package for the design of pulse sequences in programmable neutral-atom arrays
• Neural-network quantum state tomography
• Introduction to Theoretical and Experimental aspects of Quantum Optimal Control
• Pulse-level noisy quantum circuits with QuTiP
• Experimental benchmarking of an automated deterministic error suppression workflow for quantum algorithms
• Predicting non-Markovian superconducting qubit dynamics from tomographic reconstruction
• QuOCS: The Quantum Optimal Control Suite
• Pulse variational quantum eigensolver on cross-resonance based hardware
• Minimum quantum run-time characterization and calibration via restless measurements with dynamic repetition rates
• Quantum Optimal Control via Semi-Automatic Differentiation
• Tunable coupler to fully decouple and maximally localize superconducting qubits
• Controlled-Controlled-Phase Gates for Superconducting Qubits Mediated by a Shared Tunable Coupler
• Single Shot i-Toffoli Gate in Dispersively Coupled Superconducting Qubits
• Open Hardware Solutions in Quantum Technology
• Learning-based Calibration of Flux Crosstalk in Transmon Qubit Arrays
• Model predictive control for robust quantum state preparation
• Protocol for generating an arbitrary quantum state of the magnetization in cavity magnonics
• qopt: An experiment-oriented Qubit Simulation and Quantum Optimal Control Package
• Compensating for non-linear distortions in controlled quantum systems
• Classification and reconstruction of optical quantum states with deep neural networks
• Designing globally optimal entangling gates using geometric space curves
• The influence of pinholes and weak-points in aluminium-oxide Josephson junctions
• Markovian Noise Modelling and Parameter Extraction Framework for Quantum Devices
• A greedy reconstruction algorithm for the identification of spin distribution
• Simulation of Quantum Computers: Review and Acceleration Opportunities
• Gate-set evaluation metrics for closed-loop optimal control on nitrogen-vacancy center ensembles in diamond
• Bidirectional deep learning of polarization transfer in liquid crystals with application to quantum state preparation
• Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe
• Compact optical waveform generator with digital feedback
• Characterization of a driven two-level quantum system by Supervised Learning
• Differentiable master equation solver for quantum device characterisation
• Comparing planar quantum computing platforms at the quantum speed limit
• Leakage in restless quantum gate calibration
• On the fragility of gate-error metrics in simulation models of flux-tunable transmon quantum computers
• Determining the ability for universal quantum computing: Testing controllability via dimensional expressivity
• An Open-Source Data Storage and Visualization Platform for Collaborative Qubit Control
• Reinforcement Learning for Optimal Control of Spin Magnetometers
• Software tool-set for automated quantum system identification and device bring up
• Hamiltonian Learning via Inverse Physics-Informed Neural Networks
• Effective non-local parity-dependent couplings in qubit chains
• GRAPE.jl: Gradient Ascent Pulse Engineering in Julia
• Engineering Strong Beamsplitter Interaction between Bosonic Modes via Quantum Optimal Control Theory