Quantum technology & computing
2020
- C. Zoufal et al.,
“Variational Quantum Boltzmann Machines,”
arXiv:2006.06004 (2020).
- D. Sutter et al.,
“Quantum Legendre-Fenchel Transform,”
arXiv:2006.04823 (2020).
- 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).
- J. Gacon et al.,
“Quantum-Enhanced Simulation Based Optimization,”
arXiv:2005.10780 (2020).
- S. Mathis et al.,
“Toward Scalable Simulations of Lattice Gauge Theories on Quantum Computers,”
arXiv:2005.10271 (2020).
- A. C. Vazquez, S. Woerner,
“Efficient State Preparation for Quantum Amplitude Estimation,”
arXiv:2005.07711 (2020).
- A. J. C. Woitzik et al.,
“Entanglement Production and Convergence Properties of the Variational Quantum Eigensolver,”
arXiv:2003.12490 (2020).
- M. Werninghaus et al.,
“Leakage reduction in fast superconducting qubit gates via optimal control,”
arXiv:2003.05952 (2020).
- T. Alexander et al.,
“Qiskit Pulse: Programming Quantum Computers Through the Cloud with Pulses,”
arXiv:2004.06755 (2020).
- 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).
- P. J. Ollitrault et al.,
“Hardware Efficient Quantum Algorithms for Vibrational Structure Calculations,”
arXiv:2003.12578 (2020).
- J. R. Wootton
“A Quantum Procedure for Map Generation,”
Proceedings of the IEEE Conference on Games (2020).
- G. Salis et al.,
“Time-resolved tomography of a driven adiabatic quantum simulation,”
arXiv:2001.05243 (2020).
- P. Barkoutsos et al.,
“Improving Variational Quantum Optimization using CVaR,”
Quantum 4, 256 (2020).
- C. Müller,
“The dissipative Rabi model in the dispersive regime,”
arXiv:2004.02519 (2020).
- 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).
- D. Szombati et al.,
“Quantum Rifling: Protecting a Qubit from Measurement Back Action,”
Phys. Rev. Lett. 124, 070401 (2020).
2019
- A. J. Landig et al.,
“Virtual-photon-mediated spin-qubit–transmon coupling,”
Nature Communications 10, 5037 (2019).
- D. T. Le et al.,
“Doubly nonlinear superconducting qubit,”
Phys. Rev. A 100, 062321 (2019).
- S. Schlör et al.,
“Correlating Decoherence in Transmon Qubits: Low Frequency Noise by Single Fluctuators,”
Phys. Rev. Lett. 123, 190502 (2019).
- G. Torlai et al.,
“Precise measurement of quantum observables with neural-network estimators,”
arXiv:1910.07596 (2019).
- C. Müller et al.,
“Towards understanding two-level-systems in amorphous solids: insights from quantum circuits,”
Reports on Progress in Physics 82, 124501 (2019).
- G. Mazzola et al.,
“Nonunitary Operations for Ground-State Calculations in Near-Term Quantum Computers,”
Phys. Rev. Lett. 123, 130501 (2019).
- D. Grinko et al.,
“Iterative Quantum Amplitude Estimation,”
arXiv:1912.05559 (2019).
- B. Cheng et al.,
“Evidence for supercritical behavior of high-pressure liquid hydrogen,”
arXiv:1906.03341 (2019).
- A. Gilliam et al.,
“Grover Adaptive Search for Constrained Polynomial Binary Optimization,”
arXiv:1912.04088 (2019).
- A. Robert et al.,
“Resource-Efficient Quantum Algorithm for Protein Folding,”
arXiv:1908.02163 (2019).
- R. Iten et al.,
“Efficient template matching in quantum circuits,”
arXiv:1909.05270 (2019).
- D.J. Egger et al.,
“Credit Risk Analysis using Quantum Computers,”
arXiv:1907.03044 (2019).
- N. Stamatopoulos et al.,
“Option Pricing using Quantum Computers,”
arXiv:1905.02666 (2019).
- M. Ganzhorn et al.,
“Gate-efficient simulation of molecular eigenstates on a quantum computer,”
Phys. Rev. Applied 11, 044092 (2019).
- A.V. Zasedatelev et al.,
“A room-temperature organic polariton transistor,”
Nature Photonics (2019).
- C. Zoufal et al.,
“Quantum Generative Adversarial Networks for Learning and Loading Random Distributions,”
npj Quantum Information 5(2019).
- S. Woerner, D.J. Egger,
“Quantum Risk Analysis,”
npj Quantum Information 5, 15 (2019).
- M. Roth et al.,
“Adiabatic quantum simulations with driven superconducting qubits,”
Phys. Rev. A 99, 022323 (2019).
2018
- M. Malis et al.,
“Local control theory for superconducting qubits,”
arXiv:1808.10773 (2018).
- S.B. Anantharaman et al.,
“Exciton Dynamics and Effects of Structural Order in Morphology Controlled J Aggregate Assemblies,”
Adv. Functional Materials, online (2018).
- F. Montanarella et al.,
“Lasing Supraparticles Self-Assembled from Nanocrystals,”
ACS Nano 12(12), 12788–12794 (2018).
- M.A. Becker et al.,
“Long Exciton Dephasing Time and Coherent Phonon Coupling in CsPbBr2Cl Perovskite Nanocrystals,”
Nano Letters 18(12), 7546–7551 (2018).
- G. Rainò et al.,
“Superfluorescence from Lead Halide Perovskite Quantum Dot Superlattices,”
Nature 563, 671–675 (2018).
- M.A. Becker et al.,
“Bright triplet excitons in caesium lead halide perovskites,”
Nature 553(7687), 189 (2018).
- F. Scafirimuto et al.,
“Room-Temperature Exciton-Polariton Condensation in a Tunable Zero-Dimensional Microcavity,”
ACS Photonics 5, 85–89 (2018).
- D.J. Egger et al.,
“Entanglement generation in superconducting qubits using holonomic operations,”
Phys. Rev. Applied 11, 014017 (2018).
- 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).
- D.J. Egger et al.,
“Pulsed reset protocol for fixed-frequency superconducting qubits,”
Phys. Rev. Applied 10, 044030 (2018).
- N. Moll et al.,
“Quantum optimization using variational algorithms on near-term quantum devices,”
Quantum Science and Technology 3, 030503 (2018).
- O. Viyuela et al.,
“Observation of topological Uhlmann phases with superconducting qubits,”
njp Quantum Information 4, 10 (2018).
2017
- C.D. Rawlings et al.,
“Control of the interaction strength of photonic molecules by nanometer precise 3D fabrication,”
Scientific reports 7(1), 16502 (2017).
- W. Xie et al.,
“On-Chip Integrated Quantum-Dot–Silicon-Nitride Microdisk Lasers,”
Advanced Materials 29(16) (2017).
- 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).
- P.K. Barkoutsos et al.,
“Fermionic Hamiltonians for quantum simulations: a general reduction scheme,”
arXiv:1706.03637 (2017).
2016
- 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).
- 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).
- 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).
- S. Sheldon et al.,
“Characterizing errors on qubit operations via iterative randomized benchmarking,”
Phys. Rev. A 93, 12301 (2016).
- S. Gasparinetti et al.,
“Measurement of a vacuum-induced geometric phase,”
Science Advances 2, e1501732 (2016).
- D.C. McKay et al.,
“A universal gate for fixed-frequency qubits via a tunable bus,”
Phys. Rev. Applied 6, 064007 (2016).
2014
- J.D. Plumhof et al.,
“Room-temperature Bose-Einstein condensation of cavity exciton-polaritons in a polymer,”
Nature Materials 13(3) (2014).