Daniel J. Gauthier

While most current quantum network nodes are connected via fiber-based or free-space fixed point-to-point links, there have been many advancements in the last decade that expand these nodes to include mobile, re-configurable, and wireless platforms such as uncrewed aerial vehicles (UAVs) and satellites. The size, weight, and power (SWaP) restrictions of these platforms pose constraints that potentially impact the system performance of mobile nodes. Here, we will discuss our progress towards developing a low-SWaP mobile quantum key distribution (QKD) platform that can exchange quantum-secured random keys between both drones and cars. We implement a finite-key security proof that incorporates system imperfections in state preparation and analysis, including channel losses. These imperfections, present in any system, require consideration during key consolidation to minimize information leakage. We demonstrate average finite secure key rates between mobile platforms up to 19.6 kbit/s.

Quantum key distribution (QKD) systems provide a method for two users to exchange a provably secure key that can be used to establish an unconditionally secure communication channel. Here we present an FPGA-controlled prepare-and-measure BB84 polarization-based decoy state protocol using light-emitting diodes (LEDs). Our setup uses three separate LEDs driven by a field-programmable gate array (FPGA) that go through different optical paths that set the state of polarization. Each LED is connected to two GPIO pins via a different resistive path. By setting one pin to high impedance and driving the other with a nanosecond-scale electrical signal, we can choose between signal and decoy states. We can thus send 3 signal states, 3 decoy states, and 3 vacuum states. To prevent side-channel attacks multi-source QKD systems require that each state is indistinguishable from the others in the spatial, spectral, and temporal degrees-of-freedom on the photon. We do this by passing the 3 photonic wavepackets through the same single-mode fiber and 1-nm-bandwith spectral filter and use dynamic shifting of the FPGA phase-locked-loops to control the phase and the width of the electrical pulses that drive the LEDs, which allows us to control the optical pulses produced by the LEDs. Both spectral and temporal profiles are shown in Figure 1. We control the timing of the photonic wavepackets to a resolution of 78 ps. Additionally, we use the FPGA to generate true random states as required by the BB84 protocol. To quantify the indistinguishability of Alice’s various states, we use the mutual information to calculate the fraction of the final sifted key that an eavesdropper would know after making temporal and/or spectral measurements on every state that is sent. We are able to achieve 2.39e-05 and 4.31e-05 mutual information fraction leaked in the spectral and temporal waveforms, respectively. Furthermore we put our scheme into practice with a simple tabletop QKD setup where we are able to achieve 1.7% quantum bit-error rate (QBER) in the L/R bases and 2.1% QBER in the H/V bases. Additionally, our system's SWaP restrictions make it very desirable for highly mobile platforms such as drones.

Quantum key distribution (QKD) provides a method for two users to exchange a provably secure key, which requires synchronizing the user’s clocks. Qubit-based synchronization protocols directly use the transmitted quantum states and thus avoid the need for additional classical synchronization hardware, but previous approaches sacrifice secure key either directly or indirectly. Here, we introduce a Bayesian probabilistic algorithm that incorporates all published information to efficiently find the clock offset without sacrificing any secure key [1]. Additionally, the output of the algorithm is a probability, which allows us to quantify our confidence in the synchronization. Our experimental system employs an efficient three-state BB84 prepare-and-measure protocol with decoy states. Our algorithm exploits the correlations between Alice’s published basis and mean photon number choices (which must already be published for the protocol) and Bob’s measurement outcomes to probabilistically determine the most likely clock offset. We perform cross-correlations using Fast Fourier Transforms to count the number of each type of event pairing for each potential offset (e.g., how many times Alice sent a decoy state in the horizontal/vertical polarization basis and Bob registered a click in the horizontal detector). Taking these along with a lookup table for the probabilities of the different event pairings, we determine the synchronization probability of the different potential offsets using Bayesian analysis. To demonstrate the robust nature of this algorithm, we tracked its performance using simulated data with varying parameters. We find that we can achieve a 95% synchronization confidence using a string length of only 4,140 communication bin widths, meaning we can tolerate clock drift approaching 1 part in 4,140 in this example when simulating this system with a dark count probability per communication bin width of 8⨉10-4 and a received mean photon number of 0.01. The relationship between the received mean photon number and the number of communication bin widths required to achieve a 95% synchronization confidence is shown in Fig. 1. We applied this algorithm to data collected from our drone-to-done QKD experiments, with a received mean photon number of 0.043, achieving quantum bit error rates of 0.0106, 0.0287, and 0.0361 for our 3 states.