Efficient routing of information between end-nodes is a key enabler for secure quantum networks and quantum secret key sharing, which rely on creating and sustaining entangled states over time. However, such pairwise entanglements degrade due to channel loss and the storage of the entangled photons at the network nodes. The state of entanglement in turn impacts fidelity, a metric which quantifies the degree of similarity between a pair of quantum states. In this paper, we propose a routing solution that satisfies threshold fidelity requirements imposed by a receiver on the quantum information received from multiple transmitter nodes. Our solution selects intermediate repeaters from a pool of such nodes within the network to maximize the sum-rate of quantum information transfer. To this extent, we first provide expressions for the fidelity loss between adjacent nodes as well as for the end-to-end quantum data rate. Then, we propose a novel two-stage routing solution that (i) identifies the k-shortest paths for each transmitter using fidelity as cost metric and (ii) (heuristically) assigns a path for each transmitter depending on whether the repeater nodes have a single or multiple available memory units. Simulation results demonstrate that our proposed fidelity-based routing solution satisfies a wide range of fidelity requirements [0.6-0.79] while maximizing the quantum information transfer rate, outperforming the existing distance- and hop-based routing approaches.

Finding a path with the least overall noise from quantum memories, fibers, and gate operations in a quantum network involves the challenge of acquiring knowledge of these noises, their sources, and the time of occurrences. In this paper, we propose a reinforcement learning-based route selection approach that uses a multi-arm bandit algorithm to find the least noisy path from a transmitter (Tx) to a receiver (Rx), without considering any information on qubit decoherence due to probabilistic noises inherent in quantum memories and imperfect gate operations. It only uses network deployment knowledge to find a set of feasible paths from Tx to Rx. We provide a key finding from a network design perspective which says that performing entanglement swapping on nodes within a path in a non-synchronized and parallel manner not always reduces the decoherence experienced in achieving the end-to-end entanglement on that path. Further, we design and open-source a new simulator for simulating probabilistic noises encountered during entanglement distribution between Tx-Rx on a path, which has supporting callable functions for connecting the unknown network environment required for interaction with the multi-arm bandit agent. The simulation results demonstrate that our proposed route selection approach provides a path up to ~ 33% better fidelity (less noise) compared to conventional, distance-based route selection approach for the considered quantum network.

Quantum links are inherently noisy and quantum information bits (qubits) suffer upto 13% degradation in their entangled states within time-scale of 0.5 ms. Thus, mitigating errors becomes essential for reliable end-to-end data communication in a multi-hop quantum network. Compared to the typical operations performed within the contained environment of a single quantum computer, removal of both bit- and phase-flip errors in a distributed network of such computers is challenging due to the stochastic variations in the noise at each intermediate link. This paper describes a scheme that determines both the bit-and phase-flip errors (abbreviated as ‘BiP’) and mitigates them for distributed and networked quantum systems. To achieve this, we model the environment noise using general error models and obtain error calibration matrices in different computational bases for bit-phase-flip errors. Results reveal that BiP improves the fidelity beyond 95% for the received qubits compared to the state-of-the-art error mitigation method by correcting the elevation θ and azimuthal angles φin the Bloch sphere representation.

Object sensing and tracking using electric and magnetic fields allow intelligent interaction, automation, and adaptation in cyber-physical systems. Our approach, called iSense, uses a software-defined collaborative sensing technique for the detection of the type of object when placed on a large surface and tracking its mobility. iSense is cost effective, low power, and scalable, which allows its use over large surfaces. First, we introduce a dual-coil magnetic resonant sensing architecture based on nested coils, i.e., passive (outer) and active (inner) coils, for low-power contactless sensing. Second, a data-driven support vector machine-based approach helps to classify different types of objects using the voltage readings obtained at the passive coil. iSense combines sensed voltage information from multiple different coils spread over the surface with a group-based interference mitigation mechanism between coils for collaborative sensing. We validate our system with real-time prototype and experimental evaluations. We demonstrate the detection of seven different types of objects over three different materials, and real-time detection and tracking of mobile objects including a robot car. Experimental results show that each sensing coil only consumes few milliwatts, i.e., 18× less than inductive sensing and 15× less than classical magnetic resonance sensing, extend sensing depth to 3 cm, and enable tracking on the large surface sensing with more than 90% accuracy for velocity estimation.

This paper proposes MAGIC: magnetic resonant (MR) coupling for intra-body communication between implants and wearables. MAGIC includes not only the hardware-software design of the coupled coils and methods of manipulating the magnetic field for relaying information, but also the ability to raise immediate emergency-related alerts with guaranteed delivery time. MR coupling makes the design of the transmission link robust to channel-related parameters, as the magnetic permeability of skin and muscle is close to that of air. Thus, changes in tissue moisture content and thickness does not impact the design, which is a persistent problem in other approaches for implant communications like RF, ultrasound and galvanic coupling (GC). The paper makes three main contributions: It develops the theory leading to the design of the information relaying coils in MAGIC. It proposes a systems-level design of a communication link that extends up to 50cm with a low expected BER of 10-4. Finally, the paper includes an experimental setup demonstrating how MAGIC operates in air and muscle tissue, as well as a comparison with alternative implant communication technologies, such as classical radio frequency and GC. Results reveal that MAGIC offers instantaneous alerts with up to 5 times lower power consumption compared to other forms of communication.

Object sensing and tracking using electric and magnetic fields allow intelligent interaction, automation, and adaptation in cyber-physical systems. Our approach, called Softsense, uses a software-defined collaborative sensing technique for detection of the type of object and where it is placed on a large surface. Unlike RF based sensing approaches that are generally dependent on channel conditions, capacitive sensing that detects only low conductive objects, and Qi-based inductive sensing that is effective at few mm in range, Softsense is designed to operate without the above limitations. Softsense is cost effective, low power and scalable, which allows extension over large surfaces. First, we introduce a dual-coil inductive sensing architecture based on nested coils, i.e., passive (outer) and active (inner) coils, for low-power contact-less sensing. A data driven support vector machine-based approach helps to classify different materials using the voltage readings obtained at the passive coil. SoftSense combines sensed voltage information from multiple different coils spread over the surface for collaborative sensing. We validate our design on a real sensing prototype with customized coils, fabricated sensing circuit, and a network software controller. Experimental results show that each sensing coil only consumes few milliwatts, i.e., 18x less than the inductive sensing and 15x less than classical magnetic resonance sensing, extends sensing depth to 3 cm, and enables coverage of large surface sensing.