An integrated tool-set for Control, Calibration and Characterization of quantum devices applied to superconducting qubits
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Frank K. Wilhelm | Daniel J. Egger | Nicolas Wittler | Stefan Filipp | Kevin Pack | Federico Roy | Max Werninghaus | Anurag Saha Roy | Shai Machnes
[1] Andrew D. Greentree,et al. Identifying an experimental two-state Hamiltonian to arbitrary accuracy (11 pages) , 2005 .
[2] E. Hahn,et al. Spin Echoes , 2011 .
[3] Masoud Mohseni,et al. Estimation of many-body quantum Hamiltonians via compressive sensing , 2011 .
[4] Steven L. Brunton,et al. Deep learning for universal linear embeddings of nonlinear dynamics , 2017, Nature Communications.
[5] Blake R. Johnson,et al. Simple all-microwave entangling gate for fixed-frequency superconducting qubits. , 2011, Physical review letters.
[6] Christiane P. Koch,et al. Training Schrödinger’s cat: quantum optimal control , 2015, 1508.00442.
[7] G. Lindblad. On the generators of quantum dynamical semigroups , 1976 .
[8] Daniel J. Egger,et al. Qiskit pulse: programming quantum computers through the cloud with pulses , 2020, Quantum Science and Technology.
[9] Kenneth Rudinger,et al. Compressed sensing for Hamiltonian reconstruction , 2014, 1410.3029.
[10] G. Box. Science and Statistics , 1976 .
[11] E. Sudarshan,et al. Completely Positive Dynamical Semigroups of N Level Systems , 1976 .
[12] I. Rabi. Space Quantization in a Gyrating Magnetic Field , 1937 .
[13] Norman F. Ramsey,et al. A Molecular Beam Resonance Method with Separated Oscillating Fields , 1950 .
[14] R. Barends,et al. Superconducting quantum circuits at the surface code threshold for fault tolerance , 2014, Nature.
[15] D J Egger,et al. Adaptive hybrid optimal quantum control for imprecisely characterized systems. , 2014, Physical review letters.
[16] Timo O. Reiss,et al. Optimal control of coupled spin dynamics: design of NMR pulse sequences by gradient ascent algorithms. , 2005, Journal of magnetic resonance.
[17] F. K. Wilhelm,et al. Single-qubit gates in frequency-crowded transmon systems , 2013, 1306.2279.
[18] Frank K. Wilhelm,et al. Simultaneous model selection and parameter estimation: A superconducting qubit coupled to a bath of incoherent two-level systems , 2016, 1608.05360.
[19] Robert L. Kosut,et al. IDENTIFICATION OF QUANTUM SYSTEMS , 2002 .
[20] Christiane P. Koch,et al. Krotov: A Python implementation of Krotov's method for quantum optimal control , 2019, SciPost Physics.
[21] John A. Nelder,et al. A Simplex Method for Function Minimization , 1965, Comput. J..
[22] Markus Brink,et al. Demonstration of quantum volume 64 on a superconducting quantum computing system , 2020, Quantum Science and Technology.
[23] Petros Koumoutsakos,et al. Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.
[24] Jiayin Chen,et al. Software tools for quantum control: Improving quantum computer performance through noise and error suppression , 2020 .
[25] Jianming Cai,et al. Complete Quantum-State Tomography with a Local Random Field. , 2020, Physical review letters.
[26] Steven T. Flammia,et al. Stochastic estimation of dynamical variables , 2018, Quantum Science and Technology.
[27] V. Negnevitsky,et al. Estimation of a general time-dependent Hamiltonian for a single qubit , 2016, Nature Communications.
[28] A. Gruslys,et al. Comparing, optimizing, and benchmarking quantum-control algorithms in a unifying programming framework , 2010, 1011.4874.
[29] Andrew W. Cross,et al. Validating quantum computers using randomized model circuits , 2018, Physical Review A.
[30] Shai Machnes,et al. An introduction into optimal control for quantum technologies , 2020, 2003.10132.
[31] Stephen V. Stehman,et al. Selecting and interpreting measures of thematic classification accuracy , 1997 .
[32] Howard M. Wiseman,et al. Characterization of a qubit Hamiltonian using adaptive measurements in a fixed basis , 2011, 1102.3700.
[33] John M. Martinis,et al. Scalable in situ qubit calibration during repetitive error detection , 2016, 1603.03082.
[34] M. A. Rol,et al. Time-domain characterization and correction of on-chip distortion of control pulses in a quantum processor , 2019, Applied Physics Letters.
[35] D. Schuster,et al. Speedup for quantum optimal control from automatic differentiation based on graphics processing units , 2016, 1612.04929.
[36] H. De Raedt,et al. Testing quantum fault tolerance on small systems , 2018, Physical Review A.
[37] Lennart Ljung,et al. Perspectives on system identification , 2010, Annu. Rev. Control..
[38] Olivier Teytaud,et al. Exploring the MLDA benchmark on the nevergrad platform , 2019, GECCO.
[39] A. Emery,et al. Optimal experiment design , 1998 .
[40] Frank K. Wilhelm,et al. Characterization of decohering quantum systems: Machine learning approach , 2015, 1510.05655.
[41] Franco Nori,et al. Coupling strength estimation for spin chains despite restricted access , 2008, 0810.2866.
[42] S. Girvin,et al. Cavity quantum electrodynamics for superconducting electrical circuits: An architecture for quantum computation , 2004, cond-mat/0402216.
[43] J. Nocedal. Updating Quasi-Newton Matrices With Limited Storage , 1980 .
[44] M S Kim,et al. Hamiltonian tomography in an access-limited setting without state initialization. , 2008, Physical review letters.
[45] A N Cleland,et al. Optimal quantum control using randomized benchmarking. , 2014, Physical review letters.
[46] Mario Krenn,et al. Active learning machine learns to create new quantum experiments , 2017, Proceedings of the National Academy of Sciences.
[47] C J Kalkman,et al. LabVIEW: A software system for data acquisition, data analysis, and instrument control , 1995, Journal of clinical monitoring.
[48] Arkady Fedorov,et al. In Situ Characterization of Qubit Control Lines: A Qubit as a Vector Network Analyzer. , 2017, Physical review letters.
[49] Dewei Li,et al. A gradient algorithm for Hamiltonian identification of open quantum systems , 2019, ArXiv.
[50] S. Filipp,et al. Leakage reduction in fast superconducting qubit gates via optimal control , 2020, 2003.05952.
[51] Hartmut Neven,et al. Learning Non-Markovian Quantum Noise from Moiré-Enhanced Swap Spectroscopy with Deep Evolutionary Algorithm , 2019, ArXiv.
[52] Peter A. N. Bosman. Proceedings of the Genetic and Evolutionary Computation Conference Companion , 2018, GECCO.
[53] J. Gambetta,et al. Procedure for systematically tuning up cross-talk in the cross-resonance gate , 2016, 1603.04821.
[54] J. Gambetta,et al. Efficient Z gates for quantum computing , 2016, 1612.00858.
[55] Rainer Blatt,et al. Characterizing large-scale quantum computers via cycle benchmarking , 2019, Nature Communications.
[56] Jay M. Gambetta,et al. Process verification of two-qubit quantum gates by randomized benchmarking , 2012, 1210.7011.
[57] Aonan Zhang,et al. Experimental Self-Characterization of Quantum Measurements. , 2020, Physical review letters.
[58] Per J. Liebermann,et al. Optimized cross-resonance gate for coupled transmon systems , 2017, 1701.01841.
[59] S G Schirmer,et al. Quantum system characterization with limited resources , 2012, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[60] Juan Pablo Paz,et al. QUANTUM COMPUTATION WITH PHASE DRIFT ERRORS , 1997 .
[61] Gershon Kedem,et al. Automatic Differentiation of Computer Programs , 1980, TOMS.
[62] C. K. Andersen,et al. Implementation of Conditional Phase Gates Based on Tunable ZZ Interactions. , 2020, Physical review letters.
[63] Paola Cappellaro,et al. Hamiltonian identifiability assisted by single-probe measurement , 2016, 1609.09446.
[64] F. Jin,et al. Gate-error analysis in simulations of quantum computers with transmon qubits , 2017, 1709.06600.
[65] A. Zeilinger,et al. Automated Search for new Quantum Experiments. , 2015, Physical review letters.
[66] Lixing Han,et al. Effect of dimensionality on the Nelder–Mead simplex method , 2006, Optim. Methods Softw..
[67] D K Smith,et al. Numerical Optimization , 2001, J. Oper. Res. Soc..
[68] Chad Rigetti,et al. Fully microwave-tunable universal gates in superconducting qubits with linear couplings and fixed transition frequencies , 2010 .
[69] Steven T. Flammia,et al. Randomized benchmarking with confidence , 2014, 1404.6025.
[70] David Poulin,et al. Practical characterization of quantum devices without tomography. , 2011, Physical review letters.
[71] Steven T. Flammia,et al. Efficient learning of quantum noise , 2019, Nature Physics.
[72] Cornelius Hempel,et al. Adaptive scheduling of noise characterization in quantum computers , 2019, 1904.07225.
[73] D. Tannor,et al. Tunable, Flexible, and Efficient Optimization of Control Pulses for Practical Qubits. , 2018, Physical review letters.
[74] Margaret Martonosi,et al. Software Mitigation of Crosstalk on Noisy Intermediate-Scale Quantum Computers , 2019, ASPLOS.
[75] Maxim Raginsky,et al. Quantum system identification , 2003, 2003 IEEE International Workshop on Workload Characterization (IEEE Cat. No.03EX775).
[76] Frank K Wilhelm,et al. Efficient estimation of resonant coupling between quantum systems. , 2014, Physical review letters.
[77] D. Schuster,et al. Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation , 2019, Physical Review A.
[78] Danna Zhou,et al. d. , 1934, Microbial pathogenesis.
[79] Zijun Chen,et al. Measuring and Suppressing Quantum State Leakage in a Superconducting Qubit. , 2015, Physical review letters.
[80] Herschel Rabitz,et al. Data-driven gradient algorithm for high-precision quantum control , 2017, 1712.01780.
[81] R. Laflamme,et al. Gradient-based closed-loop quantum optimal control in a solid-state two-qubit system , 2018, Physical Review A.
[82] John G. Rarity,et al. Learning models of quantum systems from experiments , 2020, Nature Physics.
[83] Travis S. Humble,et al. Quantum supremacy using a programmable superconducting processor , 2019, Nature.
[84] P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .
[85] Howard M. Wiseman,et al. Bayesian quantum noise spectroscopy , 2017, New Journal of Physics.
[86] H. Neven,et al. Physical qubit calibration on a directed acyclic graph , 2018, 1803.03226.
[87] Jun Zhang,et al. Quantum Hamiltonian identification from measurement time traces. , 2014, Physical review letters.
[88] F. K. Wilhelm,et al. Optimized controlled-Z gates for two superconducting qubits coupled through a resonator , 2013, 1306.6894.
[89] Michel Gevers,et al. Identification for Control: From the Early Achievements to the Revival of Experiment Design , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.
[90] Thomas Alexander,et al. Qiskit Backend Specifications for OpenQASM and OpenPulse Experiments , 2018, ArXiv.
[91] Emmanuel Flurin,et al. Using a Recurrent Neural Network to Reconstruct Quantum Dynamics of a Superconducting Qubit from Physical Observations , 2018, Physical Review X.
[92] Jens Koch,et al. Randomized benchmarking and process tomography for gate errors in a solid-state qubit. , 2008, Physical review letters.
[93] Edoardo Charbon,et al. Impact of Classical Control Electronics on Qubit Fidelity , 2018, Physical Review Applied.
[94] Felix Motzoi,et al. Global optimization of quantum dynamics with AlphaZero deep exploration , 2019, npj Quantum Information.
[95] T. Loredo. Bayesian Adaptive Exploration , 2004, astro-ph/0409386.
[96] C. Marcus,et al. Gatemon Benchmarking and Two-Qubit Operations. , 2015, Physical review letters.
[97] Simon Maskell,et al. Multiparameter estimation along quantum trajectories with sequential Monte Carlo methods , 2017, 1707.04725.
[98] A. Aspuru-Guzik,et al. Self-driving laboratory for accelerated discovery of thin-film materials , 2019, Science Advances.
[99] D. Cory,et al. Hamiltonian learning and certification using quantum resources. , 2013, Physical review letters.
[100] J M Gambetta,et al. Simple pulses for elimination of leakage in weakly nonlinear qubits. , 2009, Physical review letters.
[101] Gui-Lu Long,et al. Experimental quantum Hamiltonian identification from measurement time traces. , 2014, Science bulletin.
[102] Xavier Bombois,et al. Optimal experiment design for open and closed-loop system identification , 2011, Commun. Inf. Syst..
[103] Erik Lucero,et al. 1/f Flux noise in Josephson phase qubits. , 2007, Physical review letters.
[104] Nathan Wiebe,et al. Robust online Hamiltonian learning , 2012, TQC.
[105] Joseph Emerson,et al. Scalable and robust randomized benchmarking of quantum processes. , 2010, Physical review letters.
[106] S. G. Schirmer,et al. Quantum system identification by Bayesian analysis of noisy data: Beyond Hamiltonian tomography , 2009 .
[107] Christiane P Koch,et al. Monotonically convergent optimization in quantum control using Krotov's method. , 2010, The Journal of chemical physics.
[108] D. Cory,et al. Quantum bootstrapping via compressed quantum Hamiltonian learning , 2014, 1409.1524.
[109] Sarah Sheldon,et al. Characterizing errors on qubit operations via iterative randomized benchmarking , 2015, 1504.06597.
[110] Jay M. Gambetta,et al. Characterizing Quantum Gates via Randomized Benchmarking , 2011, 1109.6887.
[111] Yasunobu Nakamura,et al. Improving quantum gate fidelities by using a qubit to measure microwave pulse distortions. , 2012, Physical review letters.
[112] Andrew A. Houck,et al. Suppression of Qubit Crosstalk in a Tunable Coupling Superconducting Circuit , 2018, Physical Review Applied.
[113] Robin Blume-Kohout,et al. Gate fidelity fluctuations and quantum process invariants , 2009, 0910.1315.
[114] Daniel A. Lidar,et al. Lecture Notes on the Theory of Open Quantum Systems , 2019, 1902.00967.
[115] Graham C. Goodwin,et al. Robust optimal experiment design for system identification , 2007, Autom..
[116] E. Knill,et al. Randomized Benchmarking of Quantum Gates , 2007, 0707.0963.
[117] Jun Zhang,et al. Identification of open quantum systems from observable time traces , 2015, 1503.06918.
[118] Quantum logic via optimal control in holographic dipole traps , 2005, quant-ph/0503180.
[119] Samuel H. Tersigni,et al. Wavepacket dancing: Achieving chemical selectivity by shaping light pulses , 1989 .
[120] J. Rarity,et al. Experimental quantum Hamiltonian learning , 2017, Nature Physics.
[121] H. Rabitz,et al. Combining the synergistic control capabilities of modeling and experiments: Illustration of finding a minimum-time quantum objective , 2018, Physical Review A.
[122] C. F. Roos,et al. Efficient tomography of a quantum many-body system , 2016, Nature Physics.
[123] Tommaso Calarco,et al. Optimal control technique for many-body quantum dynamics. , 2010, Physical review letters.
[124] Daniel A. Lidar,et al. Demonstration of Fidelity Improvement Using Dynamical Decoupling with Superconducting Qubits. , 2018, Physical review letters.
[125] Troy W. Borneman,et al. Controlling Quantum Devices with Nonlinear Hardware , 2015 .
[126] Nathan Wiebe,et al. Quantum Hamiltonian learning using imperfect quantum resources , 2013, 1311.5269.
[127] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[128] Dennis Willsch,et al. Supercomputer simulations of transmon quantum computers , 2020, 2008.13490.
[129] H. Rabitz,et al. Optimal control of quantum-mechanical systems: Existence, numerical approximation, and applications. , 1988, Physical review. A, General physics.
[130] SG Schirmer,et al. Experimental Hamiltonian identification for controlled two-level systems , 2004 .