Lectures
Explore our collection of quantum computing lectures, ranging from introductory topics to advanced research. Learn from experts across academia and industry.
Explore our collection of quantum computing lectures, ranging from introductory topics to advanced research. Learn from experts across academia and industry.
Major advances across all the design stack of Quantum computing – algorithm, software, and hardware – has brought us to a realm where, it is impossible to ignore the effect of Quantum computing in the world around us.
This lecture discusses how integrated hardware–software co-design can address key challenges in scalable quantum computing.
Quantum recursive programming has recently been introduced for describing sophisticated and complicated quantum algorithms in a compact and elegant way.
Quantum computing lecture from the QuCS series.
Quantum error correction has become a crucial and popular topic, especially given its essential role in ensuring the scalability and reliability of quantum computers.
Quantum computing lecture from the QuCS series.
Compiling a quantum algorithm to run on a quantum computer consisting of a 2D array of qubits with only nearest neighbor interactions is a complex problem.
Quantum computing lecture from the QuCS series.
Quantum computing lecture from the QuCS series.
Quantum computing lecture from the QuCS series.
Quantum computing has made significant progress in recent years.
The goal of numerical quantum control is to automate the gate design process, with the promise of rapid and flexible gate design for arbitrary systems.
Solving optimization problems is a key task for which quantum computers could possibly provide a speedup over the best known classical algorithms.
Quantum computing lecture from the QuCS series.
We introduce ODEgen, a new method for evaluating analytic gradients of quantum pulse programs and contrast it with the stochastic parameter-shift rule.
The full promise of quantum computation will only be realized if quantum devices scale.
Over the past decade, quantum computing has experienced remarkable growth and development, opening up new possibilities for simulating molecular properties.
The second quantum revolution, the transition from quantum theory to quantum engineering, is leading us toward practical quantum computing.
Quantum dynamics in real-world scenarios seldom stand alone; they occur under the continuous influence of surrounding environments.
This talk will provide the full picture of design automation for quantum computing - from designing quantum algorithms and quantum circuits to quantum computing using physical quantum hardware.
High-fidelity entanglement is a prerequisite for almost any quantum information processing task.
Quantum computing lecture from the QuCS series.
Quantum computing lecture from the QuCS series.
Quantum computing lecture from the QuCS series.
Combinatorial optimization has been one of most promising use cases of the near-term quantum computers.
Zheng (Eddy) Zhang is an Associate Professor at Rutgers University.
Quantum computing lecture from the QuCS series.
At the heart of the next-level quantum technology like quantum computing lies the problem of actively controlling the dynamics of a quantum system.
Several prominent quantum computing algorithms—including Grover’s search algorithm and Shor’s algorithm for finding the prime factorization of an integer—employ subcircuits termed ‘oracles’ that embed a specific instance of a mathematical function into a corresponding bijective function that is then realized as a quantum circuit representation.
Accurate information processing is crucial both in technology and in nature.
The field of quantum computing has rapidly developed around a cloud model, in which users receive a remote handle to a quantum system, compile (transpile) quantum programs (circuits) for the target system, and then remotely invoke an execution on the system from which they then fetch the results of.
Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate algorithm for solving combinatorial optimization problems on quantum computers.
State-of-the-art noisy digital quantum computers can only execute short-depth quantum circuits.
Quantum compiler plays a critical role in practical quantum compilation, particularly in the Noise-Intermediate-Scale-Quantum (NISQ) era.
The development of machine learning (ML) and quantum computing (QC) hardware has generated a lot of interest in creating quantum machine learning (QML) applications.
Benefited from the technology development of controlling quantum particles and constructing quantum hardware, quantum computation has attracted more and more attention in recent years.
In recent years, there has been a significant breakthrough in the development of superconducting quantum computers, with IBM’s 433-qubit quantum computer being a prime example of the progress made in addressing scalability issues.
Quantum computing and quantum communication provide potential speed-up and enhanced security compared with their classical counterparts.
Quantum computing and quantum communication provide potential speed-up and enhanced security compared with their classical counterparts.
Finance has been identified as the first industry sector to benefit from quantum computing, due to its abundance of use cases with exponential complexity and the fact that, in finance, time is of the essence, which makes the case for solutions to be computed with high accuracy in real time.
Clique cover and graph coloring are complementary problems which have many applications in wireless communications, especially in satellite communications (SatCom).
In recent years, quantum computers have attracted extensive research interest due to their potential capability of solving problems which are not easily solvable using classical computers.
In superconducting quantum computer, quantum gates are compiled down to a sequence of microwave pulses.
In this talk, I present abstractions that help classical developers reason about the quantum world, with the goal of designing expressive and sound tools for quantum programming.
Recent quantum supremacy experiments demonstrated with boson sampling garnered significant attention, while efforts to perfect approximate classical simulation techniques challenge supremacy claims on different fronts.
Several quantum software stacks (QSS) have been developed in response to rapid hardware advances in quantum computing.
Quantum compilers are essential in the quantum software stack but are error-prone.
Recent experimental results suggest that continuous-time analog quantum simulation would be advantageous over gate-based digital quantum simulation in the Noisy Intermediate-Size Quantum (NISQ) machine era.
Quantum machine learning (QML) is a trailblazing research subject that integrates quantum computing and machine learning.
The influence of noise in quantum dynamics is one of the main factors preventing Noisy Intermediate-Scale Quantum (NISQ) devices from performing useful quantum computations.
Quantum computing presents fascinating new opportunities for various applications, including machine learning, simulation, and optimization.
A quantum compiler is one essential and critical component in a quantum computing system to deploy and optimize the quantum programs onto the underlying physical quantum hardware platforms.
Verifying if a remote server has sufficient quantum resources to demonstrate quantum advantage is a fascinating question in complexity theory as well as a practical challenge.
The field of quantum computing has observed extraordinary advances in the last decade, including the design and engineering of quantum computers with more than a hundred qubits.
Quantum processing units (QPUs) have to satisfy highly demanding quantity and quality requirements on their qubits to produce accurate results for problems at useful scales.
The prevalence of quantum crosstalk in current quantum devices poses challenges to achieving high-fidelity quantum logic operations and reliable quantum processing.
The most challenging stage in compilation for near-term quantum computing is qubit mapping, also called layout synthesis, where qubits in quantum programs are mapped to physical qubits.
The growth of the need for quantum computers in many domains such as machine learning, numerical scientific simulation, and finance has urged quantum computers to produce more stable and less error-prone results.
As Quantum Computer device research continues to advance rapidly, there are also advances at the other levels of the computer system stack that involve these devices.
Quantum information technologies are expected to enable transformative technologies with wide-ranging global impact.
Partial differential equations (PDEs) have long been the center of interest to system modeling in many disciplines of science and engineering, such as computational physics, fluid mechanics, and quantitative finance.
Quantum entanglement enables important computing applications such as quantum key distribution.
We propose the Quantum Data Center (QDC), an architecture combining Quantum Random Access Memory (QRAM) and quantum networks.
Shadow tomography is a fundamental problem in quantum computing, whose goal is to efficiently learn an unknown d-dimensional quantum state using projective measurements.
Today’s quantum computers are in the Noisy Intermediate-Scale Quantum era and prone to errors.
Quantum computing is becoming a reality, but automated methods and software tools for this technology are just beginning.
A lecture by Jinglei Cheng on quantum computing.
A lecture by Zhixin Song on quantum computing.
I will talk about recent developments in noise mitigation techniques for quantum computers. In the Noisy Intermediate-Scale Quantum (NISQ) era, qubits have short lifetimes and quantum gates are prone to errors. This talk will provide an overview of software and algorithmic approaches to mitigate quantum noise.