Quantum computing holds the promise of a revolution in computer science. Since a quantum machine operates at an atomic level, its memory could be immense compared to silicon-based technology. Furthermore, because of an effect known as quantum parallelism, some quantum algorithms can run exponentially faster than their classical counterparts. Although the theory of quantum computation is more advanced than the physical implementation, advances in nanotechnology continually make this field more and more commercially viable. Participants in this tutorial will learn the fundamentals of quantum computing, including a surprisingly small number of prerequisites from math and physics, several quantum algorithms, and a simulator to test algorithms.
Quantum computing holds the promise of a revolution in computer science. Since a quantum machine operates at an atomic level, its memory could be immense compared to silicon-based technology. Furthermore, because of an effect known as quantum parallelism, some quantum algorithms can run exponentially faster than their classical counterparts. Although the theory of quantum computation is more advanced than the physical implementation, advances in nanotechnology continually make this field more and more commercially viable. Participants in this tutorial will learn the fundamentals of quantum computing, including a surprisingly small number of prerequisites from math and physics, several quantum algorithms, and a simulator to test algorithms.
Quantum computers offer the potential to extend our abilities to tackle computational problems in fields such as number theory, encryption, search and scientific computation.Up to a superpolynomial speedup has been reported for quantum algorithms in these areas.Motivated by the promise of faster computations, the development of quantum machines has caught the attention of both academics and industry researchers.Quantum machines are now at sizes where implementations of quantum algorithms or their components are now becoming possible.In order to implement quantum algorithms on quantum machines, resource efficient circuits and functional blocks must be designed.This tutorial will cover the following:
Quantum computing represents one of the most profound technological and intellectual advances of the twenty-first century. Rooted in the principles of quantum mechanics and enriched by developments in computer science, mathematics, and engineering, quantum computing offers fundamentally new ways of processing information. By exploiting phenomena such as superposition, entanglement, and quantum interference, quantum computers promise to solve certain classes of problems that remain intractable for even the most powerful classical machines.
Quantum Computing (QC) has gained immense popularity as a potential solution to deal with the ever-increasing size of data and associated challenges leveraging the concept of quantum random access memory (QRAM). QC promises-quadratic or exponential increases in computational time with quantum parallelism and thus offer a huge leap forward in the computation of Machine Learning algorithms. This paper analyzes speed up performance of QC when applied to machine learning algorithms, known as Quantum Machine Learning (QML). We applied QML methods such as Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) to detect Software Supply Chain (SSC) attacks. Due to the access limitations of real quantum computers, the QML methods were implemented on open-source quantum simulators such as IBM Qiskit and TensorFlow Quantum. We evaluated the performance of QML in terms of processing speed and accuracy and finally, compared with its classical counterparts. Interestingly, the experimental results differ to the speed up promises of QC by demonstrating higher computational time and lower accuracy in comparison to the classical approaches for SSC attacks.
Over the last decade, quantum simulation and computation has progressed from a purely scientific testbed of quantum mechanics to a mature field that is starting to offer new insights into materials, quantum effects, and algorithms that are intractable with even the most state-of-the-art classical computation. Simulating certain quantum Hamiltonians that are theorized to be at the heart of high-Tc superconductivity, topological quantum systems such as the fractional quantum Hall effect or fundamental questions of thermalization quickly exceed the available memory of even the most advanced supercomputers – and in fact all memory in the world – due to the exponential growth of the Hilbert space with system size. As postulated by Feynman himself, the computational system best suited for solving quantum problems is to use a quantum system itself, as it can intrinsically represent the entanglement that is the key resource making quantum computing challenging but also promising. Apart from simulating problems in physics and chemistry, there is also tremendous excitement for quantum computers to provide speedups for general algorithms in optimization at a rate that is exponentially faster than existing classical programming. So far only a small class of algorithms has been actually proven to provide such a speedup, most prominently Shor's algorithm which can factor large prime numbers, and several other search and oracle algorithms. In the last two decades, many experiments using ultracold atoms, ions, or superconducting circuits have focused on analog quantum simulation. Here, a model Hamiltonian that is believed to capture all the relevant physics of the system of study is experimentally realized using different building blocks and interactions but replicating condensed matter effects at an increased scale. This has led to a number of bespoke experiments such as BEC/Fermigass machines, quantum gas microscopes and more exotic setups that are purpose built to study a particular effect. More recently, there has been a shift to develop more universal simulation platforms and optical tweezer arrays have emerged as a leading solution due to their high degree of reconfigurability. Through tremendous improvements in the degree of control and measurement of single qubits, error correction has become a viable route to build a true universal digital quantum computer. Even as we transition into the digital age of quantum simulation, certain classes of problems with additional physics constraints such as Fermionic statistics, gauge fields and lattice gauge theories still incur too large of an overhead to be encoded on these digital simulators. In this thesis, we use a highly degenerate twisted cavity to simulate the effect of a magnetic field on a two-dimensional system of interacting photons to enable realization of states of the quantum Hall effect. We design, build, and commission a new lens based cavity apparatus realizing a lowest Landau level for a mesoscopic number of modes. We demonstrate the integration of highly excited Rydberg atoms to mediate strong interactions and demonstrate photon blockade as well as mean field interactions. As a second application of cavities to quantum information leveraging our newfound comfort with intra-cavity optics, and experience with aberration modeling in resonators, we demonstrate a new class of small waist optical resonators enabled by lens-based cavities and then apply our understanding of optical aberrations in cavities to the development of a new class of cavity array. This Cavity Array Microscope (CAM) realizes a large number of cavities (up to currently 600) using only a fixed number of free space optics and is both scalable with constant overhead as well as compatible with Rydberg atoms. This platform provides a route to highly parallel quantum repeater operation or entanglement generation between atom arrays in the future.
“Quantum computing” marks a ground breaking change in the field of computing by using quantum mechanics, like superposition and entanglement, to process data. Quantum computing represents a contemporary method of computation grounded in the principles of quantum mechanics and its astonishing phenomena. It is an exquisite fusion of physics, mathematics, computer science, and information theory. It offers significant computational capability, reduced energy usage, and exponential speeds compared to traditional computers by manipulating the behaviour of tiny physical entities, such as microscopic particles including atoms, electrons, photons, and so on. It reveals the potential of quantum computers that can affect our lives from different perspectives such as cyber security, traffic management, healthcare, artificial intelligence, and numerous others.
Quantum computing, based on quantum theory, holds great promise as an advanced computational paradigm for achieving fast computations. Quantum algorithms are expected to surpass their classical counterparts in terms of computational complexity for certain tasks, including machine learning. In this paper, we design, implement, and evaluate three hybrid quantum k-Means algorithms, exploiting different degrees of parallelism. Indeed, each algorithm incrementally leverages quantum parallelism to reduce the complexity of the cluster assignment step up to a constant cost. In particular, we exploit quantum phenomena to speed up the computation of distances. The core idea is that the computation of distances between records and centroids can be executed simultaneously, thus saving time, especially for big datasets. We show that our hybrid quantum k-Means algorithms are theoretically faster than the classical algorithm, while experiments suggest that it is possible to obtain comparable clustering results.