Quantum technology & computing

2021

1. C. Piveteau et al.
   “Quasiprobability decompositions with reduced sampling overhead,”
   arXiv:2101.09290 (2021).
2. P. Barkoutsos et al.
   “Quantum algorithm for alchemical optimization in material design,”
   Chem. Sci., (2021).
3. F. Benfenati et al.
   “Improved accuracy on noisy devices by non-unitary Variational Quantum Eigensolver for chemistry applications,”
   arXiv:2101.09316 (2021).
4. M. Werninghaus et al.,
   “Leakage reduction in fast superconducting qubit gates via optimal control,”
   npj Quantum Information 7, 14 (2021).
5. S. M. Harwood et al.
   “Improving the variational quantum eigensolver using variational adiabatic quantum computing,”
   arXiv:2102.02875 (2021).
6. A. Robert et al.,
   “Resource-Efficient Quantum Algorithm for Protein Folding,”
   npj Quantum Information 7, 38 (2021).
7. C. Zoufal et al.,
   “Variational Quantum Boltzmann Machines,”
   Quantum Machine Intelligence 3, 7 (2021).
8. I. Sokolov et al.
   “Microcanonical and finite-temperature ab initio molecular dynamics simulations on quantum computers,”
   Phys. Rev. Research 3, 013125 (2021).
9. S. Mangini et al.
   “Quantum computing models for artificial neural networks,”
   arXiv:2102.03879 (2021).
10. F. Tacchino et al.
    “Variational learning for quantum artificial neural networks,”
    in IEEE Transactions on Quantum Engineering, doi: 10.1109/TQE.2021.3062494 (2021).
11. C. Piveteau et al.
    “Error mitigation for universal gates on encoded qubits,”
    arXiv:2103.04915 (2021).
12. A. C. Vazquez, S. Woerner,
    “Efficient State Preparation for Quantum Amplitude Estimation,”
    Phys. Rev. Applied 15, 034027 (2021).
13. M. Rossmannek et al.
    “Quantum HF/DFT-embedding algorithms for electronic structure calculations: Scaling up to complex molecular systems,”
    J. Chem. Phys. 154, 114105 (2021).
14. J. Liu et al.
    “Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuits,”
    arXiv:2012.07711 (2021).
15. T. F. F. Santos et al.
    “Maximally effcient quantum thermal machines fuelled by nonequilibrium steady states,”
    arXiv:2103.09723 (2021).
16. F. Tacchino et al.
    “Molecular spin qudits for quantum simulation of light-matter interactions,”
    arXiv:2103.09706 (2021).
17. J. Gacon et al.
    “Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information,”
    arXiv:2103.09232 (2021).
18. D. Grinko et al.,
    “Iterative Quantum Phase Estimation,”
    npj Quantum Information 7, 52 (2021).

2020

1. N. Stamatopoulos et al.,
   “Option Pricing using Quantum Computers,”
   Quantum 4, 291 (2020).
2. G. Salis et al.,
   “Time-resolved tomography of a driven adiabatic quantum simulation,”
   arXiv:2001.05243 (2020).
3. J. R. Wootton
   “A Quantum Procedure for Map Generation,”
   Proceedings of the IEEE Conference on Games (2020).
4. J. Wootton,
   “Procedural generation using quantum computation,”
   FDG '20: International Conference on the Foundations of Digital GamesSeptember 2020, 98, pp. 1–8.
5. J. Wootton,
   “Benchmarking near-term devices with quantum error correction,”
   Quantum Sci. Technol., 5, 044004 (2020).
6. P. J. Ollitrault et al.,
   “Hardware Efficient Quantum Algorithms for Vibrational Structure Calculations,”
   Chem. Sci., 11, 6842-6855 (2020).
7. T. Alexander et al.,
   “Qiskit Pulse: Programming Quantum Computers Through the Cloud with Pulses,”
   Quantum Sci. Technol. 5 044006 (2020).
8. A. J. C. Woitzik et al.,
   “Entanglement Production and Convergence Properties of the Variational Quantum Eigensolver,”
   Phys. Rev. A 102, 042402 (2020).
9. S. Mathis et al.,
   “Toward Scalable Simulations of Lattice Gauge Theories on Quantum Computers,”
   Phys. Rev. D 102, 094501 (2020).
10. J. Gacon et al.,
    “Quantum-Enhanced Simulation Based Optimization,”
    2020 IEEE International Conference on Quantum Computing and Engineering (QCE), Denver, CO, USA, 2020, pp. 47-55, doi: 10.1109/QCE49297.2020.00017.
11. M. Ganzhorn et al.,
    “Benchmarking the noise sensitivity of different parametric two-qubit gates in a single superconducting quantum computing platform,”
    Phys. Rev. Research 2, 033447 (2020).
12. 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).
13. R. Helled et al.
    “Understanding dense hydrogen at planetary conditions,”
    Nature Reviews Physics 2, 562–574 (2020).
14. B. Cheng et al.,
    “Evidence for supercritical behavior of high-pressure liquid hydrogen,”
    Nature volume 585, 217–220 (2020).
15. D. Sutter et al.,
    “Quantum Legendre-Fenchel Transform,”
    arXiv:2006.04823 (2020).
16. P. Ollitrault et al.
    “Nonadiabatic Molecular Quantum Dynamics with Quantum Computers,”
    Phys. Rev. Lett. 125, 260511 (2020).
17. D. J. Egger et al.
    “Quantum Computing for Finance: State-of-the-Art and Future Prospects,”
    in IEEE Transactions on Quantum Engineering, 1, pp. 1-24, (2020).
18. A. Choquette et al.
    “Quantum-optimal-control-inspired ansatz for variational quantum algorithms,”
    arXiv:2008.01098 (2020).
19. T. Jones et al.
    “Efficient calculation of gradients in classical simulations of variational quantum algorithms,”
    arXiv:2009.02823 (2020).
20. A. C. Vazquez et al.
    “Enhancing the Quantum Linear Systems Algorithm using Richardson Extrapolation,”
    arXiv:2009.04484 (2020).
21. P. Suchsland et al.
    “Algorithmic Error Mitigation Scheme for Current Quantum Processors,”
    arXiv:2008.10914 (2020).
22. F. Tacchino et al.
    “Quantum implementation of an artificial feed-forward neural network,”
    Quantum Sci. Technol. 5 044010 (2020).
23. F. Tacchino et al.
    “Variational learning for quantum artificial neural networks,”
    2020 IEEE International Conference on Quantum Computing and Engineering (QCE), Denver, CO, USA, 2020, pp. 130-136, doi: 10.1109/QCE49297.2020.00026.
24. A. Paolo et al.
    “Variational quantum simulation of ultrastrong light-matter coupling,”
    Phys. Rev. Research 2, 033364 (2020).
25. D. Egger et al.
    “Warm-starting quantum optimization,”
    arXiv:2009.10095 (2020).
26. A. Abbas et al.
    “The power of quantum neural networks,”
    arXiv:2011.00027 (2020).
27. D. Sutter et al.
    “Quantum speedups for convex dynamic programming,”
    arXiv:2011.11654 (2020).
28. M. Pechal et al.
    “Characterization and tomography of a hidden qubit,”
29. M. Werninghaus et al.
    “High-speed calibration and characterization of superconducting quantum processors without qubit reset,”
    arXiv:2010.06576 (2020).
30. N. Wittler et al.
    “An integrated tool-set for Control, Calibration and Characterization of quantum devices applied to superconducting qubits,”
    arXiv:2009.09866 (2020).
31. D.J. Egger et al.,
    “Credit Risk Analysis using Quantum Computers,”
    in IEEE Transactions on Computers, doi: 10.1109/TC.2020.3038063 (2020).
32. 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).
33. P. Barkoutsos et al.,
    “Improving Variational Quantum Optimization using CVaR,”
    Quantum 4, 256 (2020).
34. S. Chakrabarti et al.
    “A Threshold for Quantum Advantage in Derivative Pricing,”
    arXiv:2012.03819 (2020).
35. J. P. T. Stenger et al.
    “Simulating the dynamics of braiding of Majorana zero modes using an IBM quantum computer,”
    arXiv:2012.11660 (2020).
36. P. J. Ollitrault et al.
    “Quantum equation of motion for computing molecular excitation energies on a noisy quantum processor,”
    Phys. Rev. Research 2, 043140 (2020).
37. C. Müller,
    “The dissipative Rabi model in the dispersive regime,”
    arXiv:2004.02519 (2020).
38. 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. R. Iten et al.,
   “Efficient template matching in quantum circuits,”
   arXiv:1909.05270 (2019).
8. M. Ganzhorn et al.,
   “Gate-efficient simulation of molecular eigenstates on a quantum computer,”
   Phys. Rev. Applied 11, 044092 (2019).
9. A. Gilliam et al.,
   “Grover Adaptive Search for Constrained Polynomial Binary Optimization,”
   arXiv:1912.04088 (2019).
10. A.V. Zasedatelev et al.,
    “A room-temperature organic polariton transistor,”
    Nature Photonics (2019).
11. C. Zoufal et al.,
    “Quantum Generative Adversarial Networks for Learning and Loading Random Distributions,”
    npj Quantum Information 5(2019).
12. S. Woerner, D.J. Egger,
    “Quantum Risk Analysis,”
    npj Quantum Information 5, 15 (2019).
13. 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).