A Continuous Variable Born Machine

Generative Modelling has become a promising use case for near term quantum computers. In particular, due to the fundamentally probabilistic nature of quantum mechanics, quantum computers naturally model and learn probability distributions, perhaps more efficiently than can be achieved classically. The Born machine is an example of such a model, easily implemented on near term quantum computers. However, in its original form, the Born machine only naturally represents discrete distributions. Since probability distributions of a continuous nature are commonplace in the world, it is essential to have a model which can efficiently represent them. Some proposals have been made in the literature to supplement the discrete Born machine with extra features to more easily learn continuous distributions, however, all invariably increase the resources required to some extent. In this work, we present the continuous variable Born machine, built on the alternative architecture of continuous variable quantum computing, which is much more suitable for modelling such distributions in a resource-minimal way. We provide numerical results indicating the models ability to learn both quantum and classical continuous distributions, including in the presence of noise.Comment: 17 pages, 5 figure

Machine learning applications for noisy intermediate-scale quantum computers

Quantum machine learning (QML) has proven to be a fruitful area in which to search for applications of quantum computers. This is particularly true for those available in the near term, so called noisy intermediate-scale quantum (NISQ) devices. In this thesis, we develop and study QML algorithms in three application areas. We focus our attention towards heuristic algorithms of a variational (meaning hybrid quantum-classical) nature, using parameterised quantum circuits as the underlying quantum machine learning model. The variational nature of these models makes them especially suited for NISQ computers. We order these applications in terms of the increasing complexity of the data presented to them. Firstly, we study a variational quantum classifier in supervised machine learning, and focus on how (classical) data, feature vectors, may be encoded in such models in a way that is robust to the inherent noise on NISQ computers. We provide a framework for studying the robustness of these classification models, prove theoretical results relative to some common noise channels, and demonstrate extensive numerical results reinforcing these findings. Secondly, we move to a variational generative model called the Born machine, where the data becomes a (classical or quantum) probability distribution. Now, the problem falls into the category of unsupervised machine learning. Here, we develop new training methods for the Born machine which outperform the previous state of the art, discuss the possibility of quantum advantage in generative modelling, and perform a systematic comparison of the Born machine relative to a classical competitor, the restricted Boltzmann machine. We also demonstrate the largest scale implementation (28 qubits) of such a model on real quantum hardware to date, using the Rigetti superconducting platform. Finally, for our third QML application, the data becomes purely quantum in nature. We focus on the problem of approximately cloning quantum states, an important primitive in the foundations of quantum mechanics. For this, we develop a variational quantum algorithm which can learn to clone such states, and show how this algorithm can be used to improve quantum cloning fidelities on NISQ hardware. Interestingly, this application can be viewed as either supervised or unsupervised in nature. Furthermore, we demonstrate how this can algorithm can be used to discover novel implementable attacks on quantum cryptographic protocols, focusing on quantum coin flipping and key distribution as examples. For the algorithm, we derive differentiable cost functions, prove theoretical guarantees such as faithfulness, and incorporate state of the art methods such as quantum architecture search

Augmented control of hands free voice prostheses

Laryngectomy patients often use an electrolarynx to facilitate speech following a tracheotomy. Devices of this type provide the most intelligible means of communication for tracheotomy patients. However, the electro-larynx has inherent drawbacks such as the buzzing monotonic sound emitted, the need for a free hand to operate the device, and the difficulty experienced by many tracheotomy patients in adapting to use it. The most effective means of addressing the shortcomings of existing electro-larynges is to provide the user with a hands-free facility. This allows the user to perform other manual tasks whilst speaking, or simply to communicate more effectively through body language. Hands-free devices do exist but require a considerable amount of patient training as they involve the use of the shoulder muscles to control pitch. Furthermore, they are not suitable for all patients as the hands-free is suitable only for users with a certain type of tracheotomy. Goldstein et al in 2004 [1] produced a working prototype of a hands-free device that employed electromyographic signals to activate the device. However, it was quite cumbersome in design and failed to alleviate the monotonous sound produced. The goal of this research is to research the implementation of a hands-free electrolarynx, using various activation methods including electromyographic signals to vary parameters of the output signal. Once a satisfactory system of initiation has been devised and tested, a method of pitch variation shall be developed

Progress toward practical quantum cryptanalysis by variational quantum cloning

Cryptanalysis of quantum cryptographic systems generally involves finding optimal adversarial attack strategies on the underlying protocols. The core principle of modeling quantum attacks often reduces to the ability of the adversary to clone unknown quantum states and to extract thereby meaningful secret information. Explicit optimal attack strategies typically require high computational resources due to large circuit depths or, in many cases, are unknown. Here we introduce variational quantum cloning (VarQlone), a cryptanalysis algorithm based on quantum machine learning, which allows an adversary to obtain optimal approximate cloning strategies with short depth quantum circuits, trained using hybrid classical-quantum techniques. The algorithm contains operationally meaningful cost functions with theoretical guarantees, quantum circuit structure learning and gradient-descent-based optimization. Our approach enables the end-to-end discovery of hardware-efficient quantum circuits to clone specific families of quantum states, which we demonstrate in an implementation on the Rigetti Aspen quantum hardware. We connect these results to quantum cryptographic primitives and derive explicit attacks facilitated by VarQlone. We expect that quantum machine learning will serve as a resource for improving attacks on current and future quantum cryptographic protocols

Web-based sensor streaming wearable for respiratory monitoring applications.

This paper presents a system for remote monitoring of respiration of individuals that can detect respiration rate, mode of breathing and identify coughing events. It comprises a series of polymer fabric-sensors incorporated into a sports vest, a wearable data acquisition platform and a novel rich internet application (RIA) which together enable remote real-time monitoring of untethered wearable systems for respiratory rehabilitation. This system will, for the first time, allow therapists to monitor and guide the respiratory efforts of patients in real-time through a web browser. Changes in abdomen expansion and contraction associated with respiration are detected by the fabric sensors and transmitted wirelessly via a Bluetooth-based solution to a standard computer. The respiratory signals are visualized locally through the RIA and subsequently published to a sensor streaming cloud-based server. A web-based signal streaming protocol makes the signals available as real-time streams to authorized subscribers over standard browsers. We demonstrate real-time streaming of a six-sensor shirt rendered remotely at 40 samples/s per sensor with perceptually acceptable latency (<0.5s) over realistic network conditions

Intelligibility of Electrolarynx Speech using a Novel Hands-Free Actuator

During voiced speech, the larynx provides quasi-periodic acoustic excitation of the vocal tract. In most electrolarynxes, mechanical vibrations are produced by a linear electromechanical actuator, the armature of which percusses against a metal or plastic plate at a frequency within the range of glottal excitation. In this paper, the intelligibility of speech produced using a novel hands-free actuator is compared to speech produced using a conventional electrolarynx. Two able-bodied speakers (one male, one female) performed a closed response test containing 28 monosyllabic words, once using a conventional electrolarynx and a second time using the novel design. The resulting audio recordings were randomized and replayed to ten listeners who recorded each word that they heard. The results show that the speech produced using the hands-free actuator was substantially more intelligible to the majority of listeners than that produced using the conventional electrolarynx. The new actuator has properties (size, weight, shape, cost) which lends itself as a suitable candidate for possible hands-free operation. This is one of the research ideals for the group and this test methodology presented as a means of testing intelligibility. This paper outlines the procedure for the possible testing of intelligibility of electrolarynx designs

TennisSense: a multi-sensory approach to performance analysis in tennis

The TennisSense Project, that is run in collaboration with Tennis Ireland, aims to create the infrastructure required to digitally capture physical, tactical and physiological data from tennis players in order to assist in their coaching and improved performance. This study examined the potential for using Wireless Inertial Monitoring Units (WIMU) to model the biomechanical aspects of the tennis stroke and for developing coaching tools that utilise this information. There is significant evidence in the current literature that the ability to accurately capture and model the accelerations, angular velocities and orientations involved in the tennis stroke could facilitate a major step forward in the application of biomechanics to tennis coachin