QIC Journal✍️Quantum #algorithmic differentiation #by Giuseppe Colucci and Francesco Giacosa
🔗10.26421/QIC21.1-2-5 (#arXiv:2006.13370)
EnablaJoin Prof. Bruno Bertini from the University of Birmingham for an exciting lecture on using quantum circuits to describe the dynamics of many-body quantum systems. Discover how brickwork quantum circuits compare to time-independent Hamiltonian dynamics and dive into solvable circuits like random unitary and dual unitary circuits. This talk also explores entanglement dynamics and their connection to local interactions.
Watch the #OpenAccess lecture on Enabla and engage in online discussions to enhance your understanding. Don't miss this opportunity to connect with peers and the lecturer!
🔗 Watch here: https://enabla.com/pub/1167/about
#QuantumPhysics #QuantumCircuits #ManyBodyPhysics #EntanglementDynamics
If you're interested in Anderson localization or random quantum circuits, don't miss the opportunity to discuss it with Prof. John Chalker from the University of Oxford, Department of Physics online under his new Enabla lecture here: https://enabla.com/pub/942/about
Prof. Chalker's introductory lecture covers topics such as the random matrix theory of Gaussian Wigner-Dyson ensembles, Anderson localisation with one-parameter scaling, and random quantum circuits. The focus is on the spectral form factor, entanglement entropy, and out-of-time-ordered correlators.
#CondensedMatterPhysics #QuantumPhysics #RandomMatrices #AndersonLocalization #OpenAccess #QuantumCircuits
Meet the new Enabla #OpenAccess lecture by Prof. Sergey Denisov from the Oslo Metropolitan University, where he discusses the theoretical and experimental aspects of parameterized circuits and their ability to simulate random unitaries, offering a deep dive into NISQ implementations and their potential for sampling random channels.
Have a question? Ask online through our website, and Sergey will help you understand the material better! A must-watch for anyone involved in quantum computing and random matrix theory!
🔗 Watch the full lecture here: https://enabla.com/pub/1122/about
Abstract: We consider the spectral properties of random quantum channels, both theoretically and experimentally, discuss parameterized circuits in their ability to simulate random unitaries, and present results confirming the ability of NISQ implementations of these circuits to sample certain ensembles of random channels
#ComputerScience #QuantumComputing #RandomMatrixTheory #QuantumCircuits #NISQ #OpenScience
Curious about quantum computing's real-world applications? Check out the MPI-PKS talk by Prof.Smith from the University of Nottingham, and learn with hands-on examples using the IBM Cloud quantum computers and @qiskit python library🧑💻
🔗 https://enabla.com/set/92/pub/642/about
The lecture covers the basics of quantum mechanics necessary to understand quantum circuits and explores two applications in many-body physics: finding ground states and simulating non-equilibrium dynamics. Perfect for beginners and experts alike. Don't miss out!
All #Enabla lectures are #free and #OpenAccess. If you like what we're doing, please support us by liking, sharing, following this account, leaving some comments under this post & asking questions on Enabla. Any of these actions help us a lot; thank you!🙏
#QuantumComputing #IBMQuantum #Qiskit #QuantumCircuits #NISQ #ManyBodyPhysics
Jens EisertCan the output distribution of #quantumcircuits be efficiently learned? In our work, we show that learning the output distributions of brickwork random quantum circuits is average-case hard in the statistical query model.
On the average-case complexity of learning output distributions of quantum circuits
In this work, we show that learning the output distributions of brickwork random quantum circuits is average-case hard in the statistical query model. This learning model is widely used as an abstract computational model for most generic learning algorithms. In particular, for brickwork random quantum circuits on $n$ qubits of depth $d$, we show three main results: - At super logarithmic circuit depth $d=\omega(\log(n))$, any learning algorithm requires super polynomially many queries to achieve a constant probability of success over the randomly drawn instance. - There exists a $d=O(n)$, such that any learning algorithm requires $\Omega(2^n)$ queries to achieve a $O(2^{-n})$ probability of success over the randomly drawn instance. - At infinite circuit depth $d\to\infty$, any learning algorithm requires $2^{2^{\Omega(n)}}$ many queries to achieve a $2^{-2^{\Omega(n)}}$ probability of success over the randomly drawn instance. As an auxiliary result of independent interest, we show that the output distribution of a brickwork random quantum circuit is constantly far from any fixed distribution in total variation distance with probability $1-O(2^{-n})$, which confirms a variant of a conjecture by Aaronson and Chen.
|Ðr⟩⊕|JθΣħμαħ⟩⊗|Hεατħ⟩“Measurement-induced phase transitions on dynamical quantum trees” by Xiaozhou Feng (heard about its posting from Brian Skinner on Twitter):
