Quantum technology & computing

2020

1. C. Zoufal et al.,
   “Variational Quantum Boltzmann Machines,”
   arXiv:2006.06004 (2020).
2. D. Sutter et al.,
   “Quantum Legendre-Fenchel Transform,”
   arXiv:2006.04823 (2020).
3. K. Nakano et al.,
   “TurboRVB: a many-body toolkit for {\it ab initio} electronic simulations by quantum Monte Carlo,”
   The Journal of Chemical Physics 152, 204121 (2020).
4. J. Gacon et al.,
   “Quantum-Enhanced Simulation Based Optimization,”
   arXiv:2005.10780 (2020).
5. S. Mathis et al.,
   “Toward Scalable Simulations of Lattice Gauge Theories on Quantum Computers,”
   arXiv:2005.10271 (2020).
6. A. C. Vazquez, S. Woerner,
   “Efficient State Preparation for Quantum Amplitude Estimation,”
   arXiv:2005.07711 (2020).
7. A. J. C. Woitzik et al.,
   “Entanglement Production and Convergence Properties of the Variational Quantum Eigensolver,”
   arXiv:2003.12490 (2020).
8. M. Werninghaus et al.,
   “Leakage reduction in fast superconducting qubit gates via optimal control,”
   arXiv:2003.05952 (2020).
9. T. Alexander et al.,
   “Qiskit Pulse: Programming Quantum Computers Through the Cloud with Pulses,”
   arXiv:2004.06755 (2020).
10. M. Ganzhorn et al.,
    “Benchmarking the noise sensitivity of different parametric two-qubit gates in a single superconducting quantum computing platform,”
    arXiv:2005.05696 (2020).
11. P. J. Ollitrault et al.,
    “Hardware Efficient Quantum Algorithms for Vibrational Structure Calculations,”
    arXiv:2003.12578 (2020).
12. J. R. Wootton
    “A Quantum Procedure for Map Generation,”
    Proceedings of the IEEE Conference on Games (2020).
13. G. Salis et al.,
    “Time-resolved tomography of a driven adiabatic quantum simulation,”
    arXiv:2001.05243 (2020).
14. P. Barkoutsos et al.,
    “Improving Variational Quantum Optimization using CVaR,”
    Quantum 4, 256 (2020).
15. C. Müller,
    “The dissipative Rabi model in the dispersive regime,”
    arXiv:2004.02519 (2020).
16. I. Sokolov et al.,
    “Quantum orbital-optimized unitary coupled cluster methods in the strongly correlated regime: Can quantum algorithms outperform their classical equivalents?,”
    The Journal of Chemical Physics 152, 124107 (2020).
17. D. Szombati et al.,
    “Quantum Rifling: Protecting a Qubit from Measurement Back Action,”
    Phys. Rev. Lett. 124, 070401 (2020).

2019

1. A. J. Landig et al.,
   “Virtual-photon-mediated spin-qubit–transmon coupling,”
   Nature Communications 10, 5037 (2019).
2. D. T. Le et al.,
   “Doubly nonlinear superconducting qubit,”
   Phys. Rev. A 100, 062321 (2019).
3. S. Schlör et al.,
   “Correlating Decoherence in Transmon Qubits: Low Frequency Noise by Single Fluctuators,”
   Phys. Rev. Lett. 123, 190502 (2019).
4. G. Torlai et al.,
   “Precise measurement of quantum observables with neural-network estimators,”
   arXiv:1910.07596 (2019).
5. C. Müller et al.,
   “Towards understanding two-level-systems in amorphous solids: insights from quantum circuits,”
   Reports on Progress in Physics 82, 124501 (2019).
6. G. Mazzola et al.,
   “Nonunitary Operations for Ground-State Calculations in Near-Term Quantum Computers,”
   Phys. Rev. Lett. 123, 130501 (2019).
7. D. Grinko et al.,
   “Iterative Quantum Amplitude Estimation,”
   arXiv:1912.05559 (2019).
8. B. Cheng et al.,
   “Evidence for supercritical behavior of high-pressure liquid hydrogen,”
   arXiv:1906.03341 (2019).
9. A. Gilliam et al.,
   “Grover Adaptive Search for Constrained Polynomial Binary Optimization,”
   arXiv:1912.04088 (2019).
10. A. Robert et al.,
    “Resource-Efficient Quantum Algorithm for Protein Folding,”
    arXiv:1908.02163 (2019).
11. R. Iten et al.,
    “Efficient template matching in quantum circuits,”
    arXiv:1909.05270 (2019).
12. D.J. Egger et al.,
    “Credit Risk Analysis using Quantum Computers,”
    arXiv:1907.03044 (2019).
13. N. Stamatopoulos et al.,
    “Option Pricing using Quantum Computers,”
    arXiv:1905.02666 (2019).
14. M. Ganzhorn et al.,
    “Gate-efficient simulation of molecular eigenstates on a quantum computer,”
    Phys. Rev. Applied 11, 044092 (2019).
15. A.V. Zasedatelev et al.,
    “A room-temperature organic polariton transistor,”
    Nature Photonics (2019).
16. C. Zoufal et al.,
    “Quantum Generative Adversarial Networks for Learning and Loading Random Distributions,”
    npj Quantum Information 5(2019).
17. S. Woerner, D.J. Egger,
    “Quantum Risk Analysis,”
    npj Quantum Information 5, 15 (2019).
18. M. Roth et al.,
    “Adiabatic quantum simulations with driven superconducting qubits,”
    Phys. Rev. A 99, 022323 (2019).

2018

1. M. Malis et al.,
   “Local control theory for superconducting qubits,”
   arXiv:1808.10773 (2018).
2. S.B. Anantharaman et al.,
   “Exciton Dynamics and Effects of Structural Order in Morphology Controlled J Aggregate Assemblies,”
   Adv. Functional Materials, online (2018).
3. F. Montanarella et al.,
   “Lasing Supraparticles Self-Assembled from Nanocrystals,”
   ACS Nano 12(12), 12788–12794 (2018).
4. M.A. Becker et al.,
   “Long Exciton Dephasing Time and Coherent Phonon Coupling in CsPbBr₂Cl Perovskite Nanocrystals,”
   Nano Letters 18(12), 7546–7551 (2018).
5. G. Rainò et al.,
   “Superfluorescence from Lead Halide Perovskite Quantum Dot Superlattices,”
   Nature 563, 671–675 (2018).
6. M.A. Becker et al.,
   “Bright triplet excitons in caesium lead halide perovskites,”
   Nature 553(7687), 189 (2018).
7. F. Scafirimuto et al.,
   “Room-Temperature Exciton-Polariton Condensation in a Tunable Zero-Dimensional Microcavity,”
   ACS Photonics 5, 85–89 (2018).
8. D.J. Egger et al.,
   “Entanglement generation in superconducting qubits using holonomic operations,”
   Phys. Rev. Applied 11, 014017 (2018).
9. P.K. Barkoutsos et al.,
   “Quantum algorithms for electronic structure calculations: particle/hole Hamiltonian and optimized wavefunction expansions,”
   Phys. Rev. A 98(2), 022322 (2018).
10. D.J. Egger et al.,
    “Pulsed reset protocol for fixed-frequency superconducting qubits,”
    Phys. Rev. Applied 10, 044030 (2018).
11. N. Moll et al.,
    “Quantum optimization using variational algorithms on near-term quantum devices,”
    Quantum Science and Technology 3, 030503 (2018).
12. O. Viyuela et al.,
    “Observation of topological Uhlmann phases with superconducting qubits,”
    njp Quantum Information 4, 10 (2018).

2017

1. C.D. Rawlings et al.,
   “Control of the interaction strength of photonic molecules by nanometer precise 3D fabrication,”
   Scientific reports 7(1), 16502 (2017).
2. W. Xie et al.,
   “On-Chip Integrated Quantum-Dot–Silicon-Nitride Microdisk Lasers,”
   Advanced Materials 29(16) (2017).
3. M. Roth et al.,
   “Analysis of parametrically driven exchange-type (iSWAP) and two-photon (bSWAP) interactions between superconducting qubits,”
   Phys. Rev. A 96, 062323 (2017).
4. P.K. Barkoutsos et al.,
   “Fermionic Hamiltonians for quantum simulations: a general reduction scheme,”
   arXiv:1706.03637 (2017).

2016

1. D. Urbonas et al.,
   “Zero-Dimensional Organic Exciton—Polaritons in Tunable Coupled Gaussian Defect Microcavities at Room Temperature,”
   ACS Photonics 3(9), 1542–1545 (2016).
2. G. Raino et al.,
   “Single Cesium Lead Halide Perovskite Nanocrystals at Low Temperature: Fast Single-Photon Emission, Reduced Blinking, and Exciton Fine Structure,”
   ACS Nano 10(2), 2485–2490 (2016).
3. N. Moll et al.,
   “Optimizing qubit resources for quantum chemistry simulations in second quantization on a quantum computer,”
   J. Phys. A: Math. Theor. 49, 295301 (2016).
4. S. Sheldon et al.,
   “Characterizing errors on qubit operations via iterative randomized benchmarking,”
   Phys. Rev. A 93, 12301 (2016).
5. S. Gasparinetti et al.,
   “Measurement of a vacuum-induced geometric phase,”
   Science Advances 2, e1501732 (2016).
6. D.C. McKay et al.,
   “A universal gate for fixed-frequency qubits via a tunable bus,”
   Phys. Rev. Applied 6, 064007 (2016).

2014

1. J.D. Plumhof et al.,
   “Room-temperature Bose-Einstein condensation of cavity exciton-polaritons in a polymer,”
   Nature Materials 13(3) (2014).