arXiv subjects
Publications and source records attributed to M Girish Chandra.
In this paper, we consider different Quantum Image Representation Methods to encode images into quantum states and then use a Quantum Machine Learning pipeline to classify the images. We provide encouraging results on classifying benchmark datasets of grayscale and colour images using two different classifiers. We also test multi-class classification performance.
Utilizing present and futuristic Quantum Computers to solve difficult problems in different domains has become one of the main endeavors at this moment. Of course, in arriving at the requisite solution both quantum and classical computers work in conjunction. With the continued popularity of Low Density Parity Check (LDPC) codes and hence their decoding, this paper looks into the latter as a Quadratic Unconstrained Binary Optimization (QUBO) and utilized D-Wave 2000Q Quantum Annealer to solve it. The outputs from the Annealer are classically post-processed using simple minimum distance decoding to further improve the performance. We evaluated and compared this implementation against the decoding performance obtained using Simulated Annealing (SA) and belief propagation (BP) decoding with classical computers. The results show that implementations of annealing (both simulated and quantum) are superior to BP decoding and suggest that the advantage becomes more prominent as block lengths increase. Reduced Bit Error Rate (BER) and Frame Error Rate (FER) are observed for simulated annealing and quantum annealing, at useful SNR range - a trend that persists for various codeword lengths.
Enormous activity in the Quantum Computing area has resulted in considering them to solve different difficult problems, including those of applied nature, together with classical computers. An attempt is made in this work to nail down a pipeline consisting of both quantum and classical processing blocks for the task of image classification and segmentation in a systematic fashion. Its efficacy and utility are brought out by applying it to Surface Crack segmentation. Being a sophisticated software engineering task, the functionalities are orchestrated through our in-house Cognitive Model Management framework.
Of late, we are witnessing spectacular developments in Quantum Information Processing with the availability of Noisy Intermediate-Scale Quantum devices of different architectures and various software development kits to work on quantum algorithms. Different problems, which are hard to solve by classical computation, but can be sped up (significantly in some cases) are also being populated. Leveraging these aspects, this paper examines unsupervised graph clustering by quantum algorithms or, more precisely, quantum-assisted algorithms. By carefully examining the two cluster Max-Cut problem within the framework of quantum Ising model, an extension has been worked out for max 3-cut with the identification of an appropriate Hamiltonian. Representative results, after carrying out extensive numerical evaluations, have been provided including a suggestion for possible futuristic implementation with qutrit devices. Further, extrapolation to more than 3 classes, which can be handled by qudits, of both annealer and gate-circuit varieties, has also been touched upon with some preliminary observations; quantum-assisted solving of Quadratic Unconstrained D-ary Optimisation is arrived at within this context. As an additional novelty, a qudit circuit to solve max-d cut through Quantum Approximate Optimization algorithm is systematically constructed.
An attempt has been made in this paper to modify Grover's Algorithm to find the binary string solutions approximating a target cost value. In that direction, new Controlled Oracle and the Local Diffusion Operator are suggested, apart from incorporating suitable ancilla qubits. A possible strategy to estimate eigenvalues and eigenstates of a given cost Hamiltonian, extending the reasoning of the methodology, is also pointed out. Typical results and relevant discussions are captured to support the propositions.
Quantum computers are projected to handle the Gibbs sampling and the related inference on Markov networks effectively. Apart from noting the background information useful for those starting the explorations in this important thread of Quantum Machine Learning, we capture some results and observations obtained through extensive simulations with two popular paradigms of sampling based on Quantum Annealing and Quantum Approximate Optimization Algorithm.
Mobile Augmented Reality (MAR) is becoming an important cyber-physical system application given the ubiquitous availability of mobile phones. With the need to operate in unprepared environments, accurate and robust registration and tracking has become an important research problem to solve. In fact, when MAR is used for tele-interactive applications involving large distances, say from an accident site to insurance office, tracking at both the ends is desirable and further it is essential to appropriately fuse inertial and vision sensors data. In this paper, we present results and discuss some insights gained in marker-less tracking during the development of a prototype pertaining to an example use case related to breakdown or damage assessment of a vehicle. The novelty of this paper is in bringing together different components and modules with appropriate enhancements towards a complete working system.