James Edward Steck

On the Correction of Anomalous Phase Oscillation in Entanglement Witnesses Using Quantum Neural Networks

Entanglement of a quantum system depends upon the relative phase in complicated ways, which no single measurement can reflect. Because of this, “entanglement witnesses” (measures that estimate entanglement) are necessarily limited in applicability and/or utility. We propose here a solution to the problem using quantum neural networks. A quantum system contains the information of its entanglement; thus, if we are clever, we can extract that information efficiently. As proof of concept, we show how this can be done for the case of pure states of a two-qubit system, using an entanglement indicator corrected for the anomalous phase oscillation. Both the entanglement indicator and the phase correction are calculated by the quantum system itself acting as a neural network.

Microtubules as a Quantum Hopfield Network

Penrose and Hameroff’s orchestrated objective (Orch OR) theory CHEXX[14] suggests the existence of quantum computation in microtubule protein assemblies inside living cells. To investigate this suggestion numerically, we model this system as a quantum Hopfield network (QHN) with qubits representing the tubulins and interacting coulombically with each other, at finite temperatures. The effects of energy losses, dissipation, and other environmental factors are not considered, or are considered to be screened. Simulations are carried out on this computational model, and we look for stable states (local minima) of the network. We find that quantum information processing in microtubules is feasible, though at temperatures (5.8 K) lower than physiological temperatures.

Quantum state transfer with untunable couplings

We present a general scheme for implementing bidirectional quantum state transfer in a quantum swapping channel. Unlike many other schemes for quantum computation and communication, our method does not require qubit couplings to be switched on and off. The only control variable is the bias acting on individual qubits. We show how to derive the parameters of the system (fixed and variable) such that perfect state transfer can be achieved. Since these parameters vary linearly with the pulse width, our scheme allows flexibility in the time scales under which qubits evolve. Unlike quantum spin networks, our scheme allows the transmission of several quantum states at a time, requiring only a two qubit separation between quantum states. By pulsing the biases of several qubits at the same time, we show that only eight bias control lines are required to achieve state transfer along a channel of arbitrary length. Furthermore, when the information to be transferred is purely classical in nature, only three bias control lines are required, greatly simplifying the circuit complexity.

Quantum neural computation of entanglement is robust to noise and decoherence

Abstract Measurement and witnesses of entanglement remain an important issue in quantum computing. Most witnesses will work for only a very restricted class of states, while measurements commonly require lengthy procedures. Quantum neural entanglement indicators are both more general and easier to implement. The neural network entanglement indicator can be used for a pure or a mixed state, and the system need not be “close” to any particular state; moreover, as the size of the system grows, the amount of additional training necessary diminishes. Here we show that the indicator is stable to noise and decoherence.

Learning quantum annealing

In this work we analyze a measurement-device-independent (MDI) protocol to establish continuous-variable (CV) quantum key distribution (QKD) between two ground stations. We assume communication occurs between the ground stations via satellite over two independent atmospheric-fading channels dominated by turbulence-induced beam wander. In this MDI protocol the measurement device is the satellite itself, and the security of the protocol is analyzed through an equivalent entanglement-based swapping scheme. We quantify the positive impact the fading channels can have on the final quantum key rates, demonstrating how the protocol is able to generate a positive key rate even over high-loss atmospheric channels. This is somewhat counter-intuitive given that the same outcome is only possible in the low-loss regime for a measurement device centrally positioned in a fiber-optic channel. Our results show that useful space-based quantum key generation rates between two ground stations are possible even when the relay satellite is held by an adversary. The cost in key rate incurred by altering the status of the satellite from trustworthy to untrustworthy is presented.In this work we analyze a measurement-device-independent (MDI) protocol to establish continuous-variable (CV) quantum key distribution (QKD) between two ground stations. We assume communication occurs between the ground stations via satellite over two independent atmospheric-fading channels dominated by turbulence-induced beam wander. In this MDI protocol the measurement device is the satellite itself, and the security of the protocol is analyzed through an equivalent entanglement-based swapping scheme. We quantify the positive impact the fading channels can have on the final quantum key rates, demonstrating how the protocol is able to generate a positive key rate even over high-loss atmospheric channels. This is somewhat counter-intuitive given that the same outcome is only possible in the low-loss regime for a measurement device centrally positioned in a fiber-optic channel. Our results show that useful space-based quantum key generation rates between two ground stations are possible even when the relay satellite is held by an adversary. The cost in key rate incurred by altering the status of the satellite from trustworthy to untrustworthy is presented.

Real-Time Google Glass Heads-Up Display for Rapid Air-Traffic Detection

As airspace becomes increasingly crowded, the need for next-generation traffic-advisory systems for pilots has become more crucial. To this end, a heads-up advisory display is developed within the ...

Alternate table look-up routine for real-time digital flight simulation

HE increase in complexity of flight simulators in the last decade has placed an increasingly larger computational burden on the simulation host computer. In the past, this has been dealt with by using separate processors for different tasks. This is not always financially feasible, especially in a university setting, where an entire simulation might be limited to one or two processors. While a perturbation model, sometimes used in a partial task trainer, can be regarded as a linear system, the total force and moment model of a real-time man-in-the-loop simulation requires a more complex mathematical representation of the vehicle. Coefficients and derivatives are often functions of several variables, including control surface position, angle of attack, Mach number, and several other parameters. These functions seldom can be represented in closed form by a reasonably small number of equations. Often a table is created that consists of an array of known dependent variable values, tabulated at certain known values of one or more independent variables. Table 1 is an example of such a table. On each pass through the simulation program, a search is initiated through the appropriate tables, and an interpolation between known values is performed in each of the independent variable directions. This has been shown to be one of the most computationally intense aspects of the simulation process.1 In order to retain model complexity and still achieve an acceptable frame rate, table look-up algorithms that reduce the computational load are highly desirable. Two widely used search procedures are outlined by Rolfe and Staples.2 The first, Method la, performs a top-down search between successive array values and exits once the proper interval is located. The second procedure, Method Ib, also performs a top-down search, but only if the position in the table has changed significantly. This is a method that has been used in industry.3 The search procedure is employed for each independent variable in a multidimensional table,

Quantum algorithm design using dynamic learning

nd the process is repeated for each simulation pass. Once the proper location in the array is reached, an approximation of the dependent variable must be calculated. This dependent variable is considered to be a function/of n independent variables and is approximated, as F, by carrying out successive linear interpolations in each of the n directions as outlined in Refs. 2 and 3. Alternate algorithms presented in this paper reduce the table search time by performing a top-down search during only the initialization pass. On all subsequent passes, the algorithm returns to the interval where the value will most likely reside. This is accomplished by tracking the direction and the rate of