Quantum key distribution protocols with high rates and low costs

In the age of information explosion, there is huge amount of information generated every second. Some of the information generated, for example news, is supposed to be shared by public and anyone in the world can get a copy of it. However, sometimes, information is only supposed to be maintain private or only shared by a given group of people. In the latter case, information protection becomes very important. There are various ways to protect information. One of the technical ways is cryptography, which is an area of interest for mathematicians, computer scientists and physicists. As a new area in cryptography, physical layer security has been paid great attention recently. Quantum key distribution is a hot research topic for physical layer security in the two decades. This thesis focuses on two quantum key distribution protocols that can potentially increase the key generation rate and lower the cost. On protocol is based on amplified spontaneous emission as signal source and the other one is based on discretely signaled continuous variable quantum communication. The security analysis and experimental implementation issues for both protocols are discussed.M.S.Committee Chair: Paul Voss; Committee Member: Abdallah Ougazzaden; Committee Member: David Citri

Large-Alphabet Encoding Schemes for Floodlight Quantum Key Distribution

Floodlight quantum key distribution (FL-QKD) uses binary phase-shift keying (BPSK) of multiple optical modes to achieve Gbps secret-key rates (SKRs) at metropolitan-area distances. We show that FL-QKD's SKR can be doubled by using 32-ary PSK.Comment: 2 pages, 2 figure

Distributed Quantum Sensing Using Continuous-Variable Multipartite Entanglement

Distributed quantum sensing uses quantum correlations between multiple sensors to enhance the measurement of unknown parameters beyond the limits of unentangled systems. We describe a sensing scheme that uses continuous-variable multipartite entanglement to enhance distributed sensing of field-quadrature displacement. By dividing a squeezed-vacuum state between multiple homodyne-sensor nodes using a lossless beam-splitter array, we obtain a root-mean-square (rms) estimation error that scales inversely with the number of nodes (Heisenberg scaling), whereas the rms error of a distributed sensor that does not exploit entanglement is inversely proportional to the square root of number of nodes (standard quantum limit scaling). Our sensor's scaling advantage is destroyed by loss, but it nevertheless retains an rms-error advantage in settings in which there is moderate loss. Our distributed sensing scheme can be used to calibrate continuous-variable quantum key distribution networks, to perform multiple-sensor cold-atom temperature measurements, and to do distributed interferometric phase sensing.Comment: 7 pages, 3 figure

Entanglement-Enhanced Lidars for Simultaneous Range and Velocity Measurements

Lidar is a well known optical technology for measuring a target's range and radial velocity. We describe two lidar systems that use entanglement between transmitted signals and retained idlers to obtain significant quantum enhancements in simultaneous measurement of these parameters. The first entanglement-enhanced lidar circumvents the Arthurs-Kelly uncertainty relation for simultaneous measurement of range and radial velocity from detection of a single photon returned from the target. This performance presumes there is no extraneous (background) light, but is robust to the roundtrip loss incurred by the signal photons. The second entanglement-enhanced lidar---which requires a lossless, noiseless environment---realizes Heisenberg-limited accuracies for both its range and radial-velocity measurements, i.e., their root-mean-square estimation errors are both proportional to $1/M$ when $M$ signal photons are transmitted. These two lidars derive their entanglement-based enhancements from use of a unitary transformation that takes a signal-idler photon pair with frequencies $\omega_S$ and $\omega_I$ and converts it to a signal-idler photon pair whose frequencies are $(\omega_S + \omega_I)/2$ and $\omega_S-\omega_I$. Insight into how this transformation provides its benefits is provided through an analogy to superdense coding.Comment: 7 pages, 3 figure

Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network

Many existing quantum supervised learning (SL) schemes consider data given a priori in a classical description. With only noisy intermediate-scale quantum (NISQ) devices available in the near future, their quantum speedup awaits the development of quantum random access memories (qRAMs) and fault-tolerant quantum computing. There, however, also exist a multitude of SL tasks whose data are acquired by sensors, e.g., pattern classification based on data produced by imaging sensors. Solving such SL tasks naturally requires an integrated approach harnessing tools from both quantum sensing and quantum computing. We introduce supervised learning assisted by an entangled sensor network (SLAEN) as a means to carry out SL tasks at the physical layer. The entanglement shared by the sensors in SLAEN boosts the performance of extracting global features of the object under investigation. We leverage SLAEN to construct an entanglement-assisted support-vector machine for data classification and entanglement-assisted principal component analyzer for data compression. In both schemes, variational circuits are employed to seek the optimum entangled probe states and measurement settings to maximize the entanglement-enabled {enhancement}. We observe that SLAEN enjoys an appreciable entanglement-enabled performance gain, even in the presence of loss, over conventional strategies in which classical data are acquired by separable sensors and subsequently processed by classical SL algorithms. SLAEN is realizable with available technology, opening a viable route toward building NISQ devices that offer unmatched performance beyond what the optimum classical device is able to afford.Comment: 9+2 pages, 9 figure