Semiconductor quantum light sources

Shields, A. J.

doi:10.1142/9789814287005_0023

Cited by 22 publications

(23 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For Shor's factoring algorithm, coherent iterations are required 22 , which can be realized by employing the path entangling gates of ref. 13 single-photon source and detector efficiencies preclude scaling beyond an order of magnitude, rapid progress is being made in improving these efficiencies 23,24 . These technical issues aside, we note that the approach taken here is scalable.…”

Section: Discussionmentioning

confidence: 99%

Calculating unknown eigenvalues with a quantum algorithm

et al. 2013

View full text Add to dashboard Cite

A quantum algorithm solves computational tasks using fewer physical resources than the best-known classical algorithm. Of most interest are those for which an exponential reduction is achieved. The key example is the phase estimation algorithm, which provides the quantum speedup in Shor's factoring algorithm and quantum simulation algorithms. To date, fully quantum experiments of this type have demonstrated only the read-out stage of quantum algorithms, but not the steps in which input data is read in and processed to calculate the final quantum state. Indeed, knowing the answer beforehand was essential. We present a photonic demonstration of a full quantum algorithm-the iterative phase estimation algorithm (IPEA)-without knowing the answer in advance. This result suggests practical applications of the phase estimation algorithm, including quantum simulations and quantum metrology in the near term, and factoring in the long term.M any quantum computations can be roughly broken down into two stages: read-in and processing of the input data; and processing and read-out of the solution. In the first phase, the initial data are read into a quantum register and processed with quantum gates, sometimes multiple times. This produces a quantum state in which the solution is encoded. In the second phase the quantum state may be subjected to further processing followed by measurement, producing a classical data string containing the solution. Even though quantum computers are currently limited to a small number of qubits, there is considerable interest in the small-scale demonstration of quantum algorithms, even if the size of the problems solved means that they remain easily tractable with classical techniques. Such demonstrations remain challenging, even for small numbers of qubits, as they typically require the sequential application of a large number of quantum gates 1 . Note we are making a distinction here between quantum algorithms and direct quantum simulation (Supplementary Section S1).In recent years there have been a number of elegant demonstrations of the read-out phase of Shor's factoring algorithm 2-5 and a quantum chemistry simulation algorithm 6-8 . In these demonstrations, quantum gates have been used to produce the quantum state corresponding to a particular solution of the algorithm. It was then shown that the corresponding solution could be read out with high fidelity from this state. However, in each case, the method for producing the quantum state explicitly required the solution to be already known from a classical calculation. That is, the solution was put into the quantum state by hand, before being read out through further processing and measurement. It is clearly important to go beyond this restriction and demonstrate both stages of a quantum algorithm. Phase estimation algorithmFirst, we briefly review the standard phase estimation algorithm 1 . Given a unitary U and one of its eigenstates |cl that fulfil the equationthe task is to find what the corresponding eigenvalue is-in other words, find th...

show abstract

Section: Discussionmentioning

confidence: 99%

Calculating unknown eigenvalues with a quantum algorithm

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Semiconductor quantum dots (QDs) coupled to photonics crystal cavities (PCCs) have been widely employed as efficient and scalable single photon sources [1]. The small mode volumes and high quality factors achieved in two-dimensional PCCs fabricated on suspended slabs are extremely attractive for quantum information processing as they allow photon extraction efficiencies close to unity [2].…”

Section: Introductionmentioning

confidence: 99%

Controlling the emission from single quantum dots with electro-opto-mechanical photonic crystal cavities

Midolo

Pagliano

Hoang

et al. 2013

2013 15th International Conference on Transparent Optical Networks (ICTON)

View full text Add to dashboard Cite

We present a device for the spectral reconfiguration of a two-dimensional photonic crystal cavity (PCC) based on parallel semiconductor membranes. The mechanical motion of the slabs introduces a variation in the effective refractive index of the coupled waveguides where the photonic crystal is etched resulting in a shift of the freespace wavelength. Using an integrated electrostatic actuator we demonstrated independent tuning up to 15 nm with less than 6 V bias. We present the electromechanical control of a PCC mode in resonance to single quantum dot (QD) emission lines for the real-time compensation of the spectral mismatch due to the dots' inhomogeneity. We discuss the operation of the device at low temperatures (< 10 K) and we measured the Purcell effect via the electro-mechanical control. A reversible tuning range of 13 nm and a spontaneous emission rate control by a factor of ten has been achieved.

show abstract

“…The combination of quantum dots (QDs) and high-Q resonators like photonic crystals, micropillars, microdisks, have been studied in recent years [1][2][3][4]. Such structures serve for cavity quantum electrodynamics experiments and for advanced applications such as single photon sources and threshold-less nanolasers for quantum information processing.…”

Section: Introductionmentioning

confidence: 99%

Kinetic Monte Carlo simulation of site-controlled GaAs/AlGaAs QDs growth on structured substrate

Feng

Liu

2011

2011 International Conference on Information Photonics and Optical Communications

View full text Add to dashboard Cite

A three dimensional kinetic Monte Carlo growth model with structured substrate is developed to simulate site-controlled growth of self-assembled GaAs/AlGaAs quantum dot islands. A variety of different structured substrates are qualitatively studied by kinetic Monte Carlo techniques with different growth temperature. Diffusion behavior of atoms on the structured substrate with square and circle nanohole is obtained by the simulations. Quantum dot islands prefer to nucleate in the nanohole with temperature and slope of the nanohole wall increase. With the converage increase, atoms prefer o nucleate at the edge of the hole other than to nucleate in the hole because of the geometry shape of nanohole in the substrate.

show abstract

Semiconductor quantum light sources

Cited by 22 publications

References 0 publications

Calculating unknown eigenvalues with a quantum algorithm

Calculating unknown eigenvalues with a quantum algorithm

Controlling the emission from single quantum dots with electro-opto-mechanical photonic crystal cavities

Kinetic Monte Carlo simulation of site-controlled GaAs/AlGaAs QDs growth on structured substrate

Contact Info

Product

Resources

About