This PDF file contains the front matter associated with SPIE Proceedings Volume 11391, including the Title Page, Copyright information, and Table of Contents.

A simple, room-temperature, cavity- and vacuum-free interface for an efficient photon-matter interaction is implemented. In the experiment a heralded single photon generated by the process of spontaneous parametric down-conversion is absorbed by a single atom-like system, specifically a nitrogen-vacancy color center in diamond. Here phonon-assisted absorption solves the mismatch problem of a narrow absorption bandwidth in a typical atomic medium and broadband spectrum of quantum light. The source is tunable in the spectral range $452-575$ nm, which overlaps well with the absorption spectrum of nitrogen-vacancy centers. This can also be considered as a useful technique paving the way for development of novel quantum information processing and sensing applications.

Secure quantum key distribution (QKD) is limited by the Earth’s horizon, weather, and turbulence in the atmosphere. We will outline the analysis, design, and early stages of our mobile small unmanned aircraft system (sUAS) free-space optical quantum communication system. This reconfigurable, quantum-secure communication platform can potentially open up a novel communication layer that can avoid local weather interruptions as well as provide a substantial reduction in turbulence-related loss by transmitting above ground level. We review the design and testing of a custom cage-system mode analyzer to be implemented as the receiving optics module on a sUAS. The tests of our design, within predicted operational parameters, suggests that this is a feasible option for a mode analyzer on a light-weight mobile platform.

This paper addresses the practical aspects of quantum algorithms used in numerical integration, specifically their implementation on Noisy Intermediate-Scale Quantum (NISQ) devices. Quantum algorithms for numerical integration utilize Quantum Amplitude Estimation (QAE) (Brassard et al., 2002) in conjunction with Grover’s algorithm. However, QAE is daunting to implement on NISQ devices since it typically relies on Quantum Phase Estimation (QPE), which requires many ancilla qubits and controlled operations. To mitigate these challenges, a recently published QAE algorithm (Suzuki et al., 2020), which does not rely on QPE, requires a much smaller number of controlled operations and does not require ancilla qubits. We implement this new algorithm for numerical integration on IBM quantum devices using Qiskit and optimize the circuit on each target device. We discuss the application of this algorithm on two qubits and its scalability to more than two qubits on NISQ devices.

Atomic ions can be isolated from their environment through laser-cooling and trapping, making them useful for quantum information processing, measurement, and sensing. A variety of atomic ion species have been used as qubits. Hyperfine qubits with nuclear spin I = 1/2 have demonstrated the long qubit coherence times with simple, robust laser manipulation. Other qubits (I ≠ 1/2) have easily-prepared, long-lived metastable electronic excited states, and simple discrimination between these states allows high fidelity readout. However, none of the naturally- occurring, atomic ions with nuclear spin I = 1/2 have these excited states that are simultaneously long-lived and easy to prepare. We demonstrate loading, cooling, and qubit manipulation of an artificial, I = 1/2 species of barium with visible wavelength lasers: 133Ba+. We achieved a single shot qubit state preparation and readout fidelity of F = 0.9997, the lowest error rate ever achieved by any qubit on any platform.

We present two novel approaches to state detection of qubits defined with trapped ions. The first uses simple pulse sequences from a mode-locked laser to induce state-dependent excitations in less than 1 ns. The resulting atomic fluorescence occurs in the dark, allowing the placement of non-imaging detectors right next to the atom to improve the qubit state detection efficiency and speed.The second employs the long lived F state in Yb+ which has been used in quantum information science almost exclusively for clocks and optical-frequency qubits. We describe how this resource can be used in conjunction with the ground state S1/2 manifold to aid in the scaling of trapped ion quantum information science. Narrow-band optical pumping into the 2F7/2 from one of the conventional 2S1/2 qubit states is projected to achieve a higher state preparation and measurement (SPAM) fidelity than any other demonstrated technique.

Quantum sensing is an important application of Quantum Information techniques. In this work, a mathematical model for a single Qutrit in three different representations is presented and their Shannon Mutual Information is compared.

Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work explores the feasibility of using the D-Wave as a sampler for a machine learning task. We describe a hybrid method that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave for image generation. Our method overcomes two key limitations in the 2000-qubit D-Wave processor, namely the limited number of qubits available to accommodate typical problem sizes for fully connected quantum objective functions, and samples that are binary pixel representations. As a consequence of these limitations we are able to show how we achieved nearly a 22-fold compression factor of grayscale 28 x 28 sized images to binary 6 x 6 sized images with a lossy recovery of the original 28 x 28 grayscale images. We further show how generating samples from the D-Wave after training the RBM, resulted in 28 x 28 images that were variations of the original input data distribution, as opposed to recreating the training samples. We evaluated the quality of this method by using a downstream classification method. We formulated a MNIST classification problem using a deep convolutional neural network that used samples from the quantum RBM to train the MNIST classifier and compared the results with a MNIST classifier trained with the original MNIST training data set, as well as a MNIST classifier trained using classical RBM samples. We also explored using a secondary dataset, the MNIST Fashion dataset and demonstrate the first quantum-generated fashion. Our hybrid autoencoder approach indicates advantage for RBM results relative to the use of a current RBM classical computer implementation for image-based machine learning and even more promising results for the next generation D-Wave quantum system. Our method for compression and image mappings is not constrained to RBMs, the autoencoder part of this method could be coupled with other quantum-based algorithms.

Quantum technologies containing key GaN laser components will enable a new generation of high precision quantum sensors, optical atomic clocks and secure communication systems for many applications such as next generation navigation, gravity mapping and timing since the AlGaInN material system allows for laser diodes to be fabricated over a wide range of wavelengths from the u.v. to the visible. We report our latest results on a range of AlGaInN diode-lasers targeted to meet the linewidth, wavelength and power requirements suitable for optical clocks and cold-atom interferometry systems. This includes the [5s²S_1/2-5p²P_1/2] cooling transition in strontium^† ion optical clocks at 422 nm, the [5s₂¹S₀-5p¹P¹] cooling transition in neutral strontium clocks at 461 nm and the [5s²_s1/2 – 6p²P_3/2] transition in rubidium at 420 nm. Several approaches are taken to achieve the required linewidth, wavelength and power, including an extended cavity laser diode (ECLD) system and an on-chip grating, distributed feedback (DFB) GaN laser diode.

Wavefronts for single and multiple photons are derived and combined with quantum hyper-entanglement, multiphoton entanglement, and network properties. Each node of the network will transmit at least one signal and one ancilla photon in a hyper-entangled state. Parameters used for hyper-entanglement will include photon polarization, energy-time, orbital angular momentum (OAM), radial quantum number, etc. as well as up to 12 parameters characterizing the wavefront properties. These parameters arise from using Lie algebra techniques to solve the paraxial wave equation. Eigenfunctions of operators associated with each 2D paraxial wave equation will be determined each being a function of up to six parameters. The resulting wavefronts will be shown in some cases to be propagation invariant and have the self-healing property. The free parameters of the derived wavefront will offer a programmable wavefront that will permit better imaging around obstacles and reduced propagation loss. Unlike the results found in the literature the free parameters will permit photonic trajectories to be programmed for better performance. Parabolic trajectories, i.e. ones with quadratic terms will emerge as well as those with cubic or higher degree terms. This will further facilitate imaging around obstacles. The same wavefront or combinations of wavefronts can be applied to multiple signal photons to combine hyper-entanglement with multiphoton entanglement offering additional performance improvements. Wavefront engineering can be applied to a single ancilla or multiple ancilla photons. Enhanced superdense coding will be considered. Measures of effectiveness (MOEs) such as the SNR, measurement time, resolution measures, and Holevo bound will be derived or provided.

Unlike purely classical communication, unconditionally secure key distribution is possible if Alice and Bob are both equipped with quantum hardware. The degree to which a protocol needs to be quantum is not only an interesting theoretical question, but also important for practical implementations. Indeed, one may wish to construct cheaper devices, or compensate for device malfunction. In this sense, studying limited resource QKD protocols is an important problem. One direction to studying this is the semi-quantum model introduced by Boyer et al. in 2007 (PRL 99 140501). Several provably secure semi-quantum protocols were put forth. However, most of these protocols were proven secure in the perfect qubit scenario and not necessarily against practical attacks. Only recently, starting with seminal work of Boyer, Katz, Liss, and Mor in (PRA 96 062335) has research in the field of semi-quantum cryptography considered practical devices and imperfections, such as multi photon sources and imperfect detectors. In this work, we present a new SQKD protocol based on an Extended B92 protocol which is able to counter certain practical attacks. Furthermore, the techniques we use may see broad application to other limited-resource (S)QKD protocols.

This talk will explain the structure of Khovanov homology for knots and links and will present a quantum algorithm for its computation that is based on the use of combinatorial Hodge theory. By using combinatorial Hodge theory we can formulate the problem of computing the homology in terms of the determination of the dimension of the eigenvalue one subspace of a unitary operator that is associated with the boundary and coboundary operators of the Khovanov complex. We then use the quantum phase estimation algorithm to find the dimension of this subspace. This is a first foray into the problems of producing a quantum algorithm for the significant knot and link invariant Khovanov homology. There are other deeper problems related to the structure of the chain complex and we shall discuss these difficulties. This paper represents joint work with Sam Lomonaco and Nadya Shirakova.

Spectral graph theory is a branch of mathematics that studies the relationships between the eigenvectors and eigenvalues of Laplacian and adjacency matrices and their associated graphs. The Variational Quantum Eigen- solver (VQE) algorithm was proposed as a hybrid quantum/classical algorithm that is used to quickly determine the ground state of a Hamiltonian, and more generally, the lowest eigenvalue of a matrix M ∈ R^nxn. There are many interesting problems associated with the spectral decompositions of associated matrices, such as partitioning, embedding, and the determination of other properties. In this paper, we will expand upon the VQE algorithm to analyze the spectra of directed and undirected graphs. We evaluate runtime and accuracy comparisons (empirically and theoretically) between different choices of ansatz parameters, graph sizes, graph densities, and matrix types, and demonstrate the effectiveness of our approach on Rigetti's QCS platform on graphs of up to 64 vertices, finding eigenvalues of adjacency and Laplacian matrices. We finally make direct comparisons to classical performance with the Quantum Virtual Machine (QVM) in the appendix, observing a superpolynomial runtime improvement of our algorithm when run using a quantum computer.*

Discrete quantum walk is one of the de facto models of quantum computation and as an efficient tool to develop quantum search algorithms. Although the theoretical model of quantum walks is straightforward, there are many complex scenarios such as coherence decay and/or decoherence in the implementations. It is hard to test experimentally if quantum walk works, or it just decays into a version of classic random walk. We propose a quantum central limit theorem (QCLT) for discrete quantum walks and conduct the statistical hypothesis testing for the standard or decayed walker probability distribution for imperfect quantum walks based on the QCLT. A reliable statistical analysis result is obtained for the imperfect distribution by the experimental quantum walk study.

We define a framework for objective function estimation and maximization of arbitrary computational problems in gatemodel quantum computers. The method significantly reduces the costs of the objective function estimation and provides an estimate of the new state of the quantum computer. The framework integrates an objective function extension procedure, a segmentation algorithm that utilizes the gate parameters of the quantum computer, and a machine-learning unit for the quantum state prediction. The results are particularly convenient for the performance optimization of experimental gate-model quantum computations.