Skip the Hype. These 5 Books Actually Teach You Quantum. Qiskit 2026-09-17 | Skip the Hype. These 5 Books Actually Teach You Quantum.
Entanglement and Bell’s Inequality | Qiskit in the Classroom Qiskit 2024-12-18 | In this lesson, we’ll step through the logic of Bell’s Inequality and how it can be used to disprove local hidden variable alternatives to quantum theory. Then, we’ll show you how you can run your own Bell experiment using the qubits in a real quantum computer.Bell's 1964 paper: journals.aps.org/ppf/abstract/10.1103/PhysicsPhysiqueFizika.1.1952022 Nobel Prize in Physics information: nobelprize.org/uploads/2023/10/advanced-physicsprize2022-4.pdfBell's inequality Qiskit module: learning-api.quantum.ibm.com/assets/9567907c-74ab-4185-bdfa-2f67df4f3325#qiskit #learnquantum #ibmquantum
So what is an Ansatz? Qiskit 2024-12-16 | So what is an Ansatz? An ansatz is an educated guess about the value or form of an unknown function and is used to help derive the real solution of an equation In quantum computing ansatz is usually a parameterized circuit often used in variational algorithms such as VQE.This ansatz is used as a starting point or trial state which is then iteratively updated as more information is calculated.Picking a good ansatz is important when running quantum experiments as a well chosen ansatz can drastically improve the accuracy of your results.
Understanding Quantum Uncertainty | Qiskit in the Classroom Qiskit 2024-12-11 | In this lesson, we'll dig into Heisenberg's Uncertainty Principle and how it applies to the qubits in a quantum computer. Then, we'll make some measurements on a real qubit to verify that the uncertainty principle holds.Link to download the module: learning-api.quantum.ibm.com/assets/691e9439-cf33-4883-85f5-04eceb7dc525#qiskit #ibmquantum #learnquantum
Hamiltonian: The first step in almost any problem. Qiskit 2024-12-10 | In quantum mechanics, a hamiltonian is a mathematical description of the total energy of a system, incorporating both kinetic and potential energy. The system being described could be anything: a molecule, a single qubit, or a bouncy ball.A hamiltonian can take different forms, it might incorporate different types of energy contributions, or be written in different coordinates or dimensions, which often makes them look very different. In quantum mechanics, knowing the Hamiltonian of your system is very important, because it will allow you to solve the Schrodinger Equation; the heart of quantum mechanics. The Schrodinger Equation calculates how the system will evolve or change in time, which tells us basically everything useful we would want to know. That is why the first step of almost any problem or experiment boils down to calculating or identifying the Hamiltonian.
Superposition Explained. Qiskit 2024-12-04 | Superposition is a word you hear often in the world of quantum computing. But what is it? And why is it so important?youtu.be/TZ-sUHK8vVQ #qiskit #ibmquantum #learnquantum
What is a Superposition? | Qiskit in the Classroom Qiskit 2024-12-04 | In this lesson, we'll try to better understand quantum superposition by comparing our measurements of a qubit in a superposition to that of a classical coin flip. In the episode, we'll explore the differences in behavior between our "quantum coin" and a classical coin.Link to download the module: learning-api.quantum.ibm.com/assets/30776b0f-bbfd-4b89-bea6-1fb1a697d7a5#qiskit #learnquantum #ibmquantum
Quantum Spin and the Stern-Gerlach Experiment | Qiskit in the Classroom Qiskit 2024-11-27 | This lesson discusses the famous historical experiment by Otto Stern and Walther Gerlach that proved the quantization of angular momentum. We show you how the experiment worked and how to run your own Stern-Gerlach-type experiment with Qiskit and an IBM Quantum computer.Translation of original 1922 Stern and Gerlach paper: arxiv.org/abs/2301.113432016 review article on the Stern-Gerlach experiment: arxiv.org/abs/1609.09311Link to module:Stern Gerlach (student) learning-api.quantum.ibm.com/assets/d626ae4b-7336-4ec1-8756-89ec5983e63e#qiskit #learnquantum #ibmquantum
Error Mitigation Deep Dive Part 3 Building Confidence - Mariana Bernagozzi - QDC24 Qiskit 2024-11-26 | An in-depth look at error mitigation workflows, including the use of noise models and debugging tools.#ibm #ibmquantum #qiskit
Error Mitigation Deep Dive Part 2 - Sam Ferracin - QDC24 Qiskit 2024-11-26 | An in-depth look at error mitigation workflows, including the use of noise models and debugging tools.#ibm #ibmquantum #qiskit
Error Mitigation Deep Dive Part 1 Leveraging Layers - Ian Hincks - QDC24 Qiskit 2024-11-26 | An in-depth look at error mitigation workflows, including the use of noise models and debugging tools.#ibm #ibmquantum #qiskit
Patterns for Performance - Andrew Wack - QDC24 Qiskit 2024-11-26 | An overview of the best error mitigation techniques using IBM Quantum software tools, as well as an overview of performance metrics for getting the best possible results from hardware.#ibm #ibmquantum #qiskit
Performance Benchmarks - Blake Johnson - QDC24 Qiskit 2024-11-26 | An overview of the best error mitigation techniques using IBM Quantum software tools, as well as an overview of performance metrics for getting the best possible results from hardware.#ibm #ibmquantum #qiskit
Error Mitigation Extracting Accurate Results from Noisy Quantum Computers - Abhinav Kandala - QDC24 Qiskit 2024-11-26 | An in-depth look at error mitigation workflows, including the use of noise models and debugging tools.#ibm #ibmquantum #qiskit
Error Suppression Passes in Qiskit Runtime - Petar Jurcevic - QDC24 Qiskit 2024-11-26 | An in-depth look at error mitigation workflows, including the use of noise models and debugging tools.#ibm #ibmquantum #qiskit
Intro to the Qiskit Transpiler Service - David Kremer - QDC24 Qiskit 2024-11-26 | An overview of the foundational concepts of transpilation, why it is important for quantum computing work, and which IBM Quantum software tools to use.#ibm #ibmquantum #qiskit
Compiler Tool Comparison - Elena Peña Tapia - QDC24 Qiskit 2024-11-26 | Follow along for a full demonstration on how to build a custom pass manager for an optimal transpilation workflow.
Intro to Transpilation - Matthew Treinish - QDC24 Qiskit 2024-11-26 | An overview of the foundational concepts of transpilation, why it is important for quantum computing work, and which IBM Quantum software tools to use.#ibm #ibmquantum #qiskit
IBM Quantum 2024 State of the Union: From utility to algorithm discovery Qiskit 2024-11-26 | Last year, IBM Quantum announced that the field had entered the era of quantum utility. Now, it's time to use utility-scale quantum computers for algorithm discovery. This talk covers the remarkable performance breakthroughs and software and hardware innovations that will allow users to explore uncharted computational territory with quantum computers, plus progress as we push forward along our development and innovation roadmaps.#ibm #ibmquantum #qiskit
Correcting Quantum Errors | Understanding Quantum Information and Computation - Lesson 13 Qiskit 2024-11-13 | Building reliable large-scale quantum computers is a monumental challenge, due in large part to the fact that quantum information is extremely fragile. Fortunately, we have a key tool to help in this endeavor: quantum error correction. This lesson takes a first look at quantum error correction, including the first quantum error correcting code discovered — the 9-qubit Shor code — and the foundational concept in quantum error correction known as the discretization of errors.0:00 – Introduction2:27 – Overview3:28 – The need for error correction5:32 – Classical repetition codes9:10 – Repetition code for qubits14:02 – Phase-flip errors15:13 – Correcting phase-flip errors18:03 – 9-qubit Shor code19:34 – Correcting bit-flip errors21:31 – Errors and CNOTs23:06 – Correcting phase-flip errors26:05 – Correcting bit- and phase-flips28:20 – Random errors32:06 – Unitary errors37:16 – Arbitrary errors39:39 – ConclusionFind the written content for this lesson on IBM Quantum Learning: learning.quantum.ibm.com/course/foundations-of-quantum-error-correction/correcting-quantum-errors#qiskit #ibmquantum #learnquantum
Which Problems Are Quantum Computers Good For? Quantum Computing in Practice Qiskit 2024-11-01 | In the fourth episode of Quantum Computing in Practice, Olivia discusses how we decide which types of problems to tackle with today’s quantum computers. In the first part, she teaches broad guidelines for selecting problems based on computational complexity theory and more practical considerations. Then, she walks us through three example use-cases based on recent work done in the community.Associated chapter: learning.quantum.ibm.com/course/quantum-computing-in-practice/what-problems-are-quantum-computers-good-forPapers discussed:Quantum Simulations of Hadron Dynamics in the Schwinger Model using 112 Qubits:arXiv: 2401.08044Bias-field digitized counterdiabatic quantum optimization:arXiv:2405.13898mRNA secondary structure prediction using utility-scale quantum computers:arXiv:2405.20328v1
Introduction to Coding with Qiskit Qiskit 2024-10-28 | Welcome back to Coding with Qiskit, where IBM research scientist Dr. Derek Wang will get you up to speed with the latest developments in quantum computers for your own work. The foundation and focus will be on Qiskit 1.x, an open source and freely available software development kit that allows you to program useful quantum computational workflows, all the way from building quantum circuits and designing quantum algorithms, to submitting them to real quantum computers and orchestrating large-scale, complex work loads. With 1.0, we expect Qiskit to become much more stable, efficient and serve as the developer-ready software backbone for quantum computational workflow.
Quantum Internship at IBM #qiskit #ibmquantum #quantum Qiskit 2024-10-23 | Head to this link to learn more about IBM quantum internships:linkedin.com/pulse/intern-ibm-quantum-2025-ibm-quantum-wycme
Quantum Computing in Practice with Dr. Olivia Lanes Qiskit 2024-10-17 | Many videos on quantum computing focus on their theoretical concepts or future potential, but in this new series, Dr. Olivia Lanes will show you how to use a quantum computer that exists right now to its fullest potential.#quantumcomputing #quantum
Understanding Quantum Information & Computation Qiskit 2024-10-16 | The goal for this series is simple: to create a university level quantum course and to make it available worldwide, while explaining how quantum information and computation actually works, at a detailed mathematical level.There's no expectation you already know anything about quantum computing or quantum information, or have a background in quantum mechanics or physics. But you will want to be comfortable with linear algebra, complex numbers, and basic mathematical concepts.Understanding Quantum Information and Computation is meant for anyone who wants to learn about quantum computing from a computer scientists perspective, and will begin by covering the basics of quantum information in Unit 1. In unit 2, we will dive into Quantum Algorithms, Unit 3 we'll dive deeper into the mathematics of quantum information, and Unit 4 will explore how to understand and mitigate noise. My name is John Watrous, and I hope you'll join me for Lesson 1 of Understanding Quantum Information and Computation.
Unlocking Qiskit Functions: A Deep Dive with Tushar Mittal and Sanket Panda | Quantum Crosstalk Qiskit 2024-10-16 | Qiskit Functions simplify and accelerate utility-scale algorithm discovery and application development, by abstracting away parts of the quantum software development workflow. In this way, Qiskit Functions free up time normally spent hand-writing code and fine-tuning experimentsIn this interview, Olivia speaks to IBM Quantum product managers Sanket Panda and Tushar Mittal, to better understand what the Qiskit functions are, how users of different types can integrate them, and how to get started with them right now.ibm.com/quantum/blog/qiskit-functions-catalogdocs.quantum.ibm.com/guides/functions#ibm #quantum #qiskit
Adaptive Quantum Simulation Algorithms: State of the Art and Outlook | Qiskit Quantum Seminar Qiskit 2024-10-12 | Adaptive quantum simulation algorithms: state of the art and outlookQiskit Quantum Seminar with Sophia EconomouAbstract: Variational quantum algorithms (VQAs) constitute a class of hybrid quantum-classical simulation algorithms that are envisioned for quantum simulation and optimization, possibly in the near term. For VQAs to be useful, it is important to reduce the size of the ansatz and the number of required measurements. I will present our work addressing these challenges with ADAPT-VQE, an adaptive, problem tailored approach to ansatz construction. I will also discuss a pulse-based alternative for variational algorithms.Bio:Sophia E. Economou is an American physicist who is a professor and the T. Marshall Hahn Chair in Physics at Virginia Tech. She directs the Virginia Tech Center for Quantum Information Science and Engineering.
Quantum Nanomechanics with Trapped Ion Motion | Qiskit Quantum Seminar with Daniel Slichter Qiskit 2024-09-28 | Quantum nanomechanics with trapped ion motionEpisode 176Abstract: Trapped atomic ions can host highly coherent, readily-controllable qubits and qudits in their internal degrees of freedom; these are widely used for quantum computing, sensing, and networking applications. However, trapped ions also possess quantized motional degrees of freedom. These single-ion or few-ion nanomechanical quantum harmonic oscillators can be cooled very near their ground states and manipulated by externally applied fields or by coupling to the ions’ internal states. Trapped ion motional modes are most commonly used in an auxiliary role to couple the internal states of multiple ions, but there is increasing interest in exploring them as stand-alone, coherent quantum harmonic oscillators. I will describe recent work in our group on several projects that use trapped ion motion as a quantum nanomechanical oscillator. We have created highly squeezed states of trapped ion motion, enabling us to perform quantum-enhanced sensing and to speed up quantum dynamics involving the motion. We have also demonstrated various forms of coherent coupling, state exchange, and entanglement between multiple motional modes. This coupling can enable repeated non-destructive measurement of motional states by avoiding the motional state decoherence associated with photon recoil during fluorescence readout. Finally, we are using the motion of a single trapped ion as a quantum sensor to understand and mitigate sources of electric field noise from material surfaces, using a special trap that enables the study of interchangeable samples.Bio:Daniel Slichter is a physicist in the Ion Storage Group at NIST in Boulder, Colorado. His research focuses on quantum information experiments with trapped atomic ions, with an emphasis on developing new paradigms for scalable trapped ion quantum computing and creating long-distance quantum networks with trapped ion memory and computation nodes. He received his A.B. in physics (2004) from Harvard University, and his M.A. (2007) and Ph.D. (2011) in physics from the University of California, Berkeley. His Ph.D. research was in the field of superconducting quantum information, where he demonstrated the first continuous high-fidelity measurement of a superconducting qubit, and studied quantum feedback, measurement backaction, and near-quantum-limited parametric amplification.
Purifications and Fidelity | Understanding Quantum Information and Computation - Lesson 12 Qiskit 2024-09-26 | This lesson explores the concept of a purification in quantum information, where an arbitrary quantum state is represented by a pure state of a larger system that leaves the original state when the rest of the larger system is discarded. This is an incredibly useful concept that allows us to reason interesting things about density matrices by essentially viewing them as parts of pure states, which are simpler in some respects. The lesson also introduces fidelity, a measure of similarity between two quantum states, which plays a key role in quantum computing. Along the way we'll also discuss Schmidt decompositions, the unitary equivalence of purifications, and Uhlmann's theorem, which links fidelity to purifications.0:00 — Introduction1:29 — Overview2:23 — Purifications4:02 — Existence of purifications7:33 — Schmidt decompositions12:54 — Unitary equivalence of purifications16:21 — Example: superdense coding18:04 — Cryptographic implications20:47 — HJW theorem25:48 — Definition of fidelity31:32 — Properties of fidelity35:24 — Gentle measurement lemma39:18 — Uhlmann’s theorem43:45 — ConclusionFind the written content for this lesson on IBM Quantum Learning: learning.quantum.ibm.com/course/general-formulation-of-quantum-information/purifications-and-fidelity#ibmquantum #qiskit #learnquantum
Effect of non–unital noise on random circuit sampling | Qiskit Quantum Seminar with Soumik Ghosh Qiskit 2024-09-21 | Episode 175In this work, drawing inspiration from the type of noise present in real hardware, we study the output distribution of random quantum circuits under practical non–unital noise sources with constant noise rates. We show that even in the presence of unital sources like the depolarizing channel, the distribution, under the combined noisechannel, never resembles a maximally entropic distribution at any depth. To show this, we prove that the output distribution of such circuits never anticoncentrates — meaning it is never too ”flat” — regardless of the depth of the circuit. This is in stark contrast to the behavior of noiseless random quantum circuits or those with only unital noise, both of which anticoncentrate at sufficiently large depths. As consequences, our results have interesting algorithmic implications on both the hardness and easiness of noisy random circuit sampling, since anticoncentration is a critical property exploited by both state-of-the-art classical hardness and easiness results.Based on: journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.5.030317I am a fourth year Ph.D. student in Computer Science at the University of Chicago, supervised by William Fefferman. Previously, from 2018 to 2020, I was a Master’s student at the University of Waterloo, under the supervision of John Watrous. I was also a part of the Institute for Quantum Computing (IQC). In another life, I was an undergraduate at Jadavpur University, India in the Department of Electronics and Telecommunication Engineering, from 2014 to 2018.
Your Guide to Utility Scale QAOA: Quantum Computing in Practice Qiskit 2024-09-18 | In this third episode of Quantum Computing in Practice, Olivia walks you through a step-by-step tutorial on the Maxcut problem, which will be solved using the QAOA algorithm. The first part of the video is focused on the basics and setting up the problem, then we will discuss how to solve this problem for 100+ qubits using IBM Quantum processors.Follow along with the tutorial here: learning.quantum.ibm.com/tutorial/quantum-approximate-optimization-algorithm#ibmquantum #learnquantum #qiskit
Qiskit Performance with Dr. Blake Johnson | Quantum Crosstalk Qiskit 2024-09-11 | Why is Qiskit the most performant quantum SDK? In this episode of Crosstalk, Olivia and Abby sit down with Dr. Blake Johnson, an IBM distinguished engineer and the head of IBM Quantum Engine to discuss Qiskit performance. We go through questions like, 'what is performant software?' and 'how did Qiskit get so much faster this past year??' We also discuss how Qiskit performs relative to other competitor SDKs :eyes:If you are interested in Qiskit, quantum programming, or benchmarking, this is the conversation you've been waiting forThese links might be helpful:Install Qiskit: docs.quantum.ibm.com/guides/install-qiskitSoftware Latest Updates: docs.quantum.ibm.com/guides/latest-updates
Quantum Computation with Fermions, Bosons, and Qubits with Eleanor Crane Qiskit 2024-09-07 | Quantum Computation with Fermions, Bosons, and Qubits with Eleanor CraneEpisode 174Abstract: Finding a scalable and universal framework for quantum simulation of strongly correlated fermions and bosons is important in fields ranging from material science to high-energy physics. While digital qubit-only quantum computers in principle offer such universality, the overhead encountered in mapping fermions and bosons to qubits renders this endeavour extremely challenging to implement in practice. In this talk, I will explain an approach to simulating bosonic matter, fermionic matter, and Abelian gauge fields in (2+1)D, which uses hybrid digital qubit-boson (or 'oscillator-qubit') and qubit-fermion operations, avoiding this overhead altogether.I will show how our compilation strategies for hybrid oscillator-qubit computation can be used to study dynamics as well as ground states, and develop measurement techniques of non-local observables, and mention the influence of hardware errors in circuit QED coupled to high-Q cavities. I will also show that it is possible to implement fully fault-tolerant operations using logical fermions comprised of physical fermions as can be found in neutral atoms.Illustrating the advantage of our hybrid qubit-oscillator approach over all-qubit hardware, the end-to-end comparison of the gate complexity for the Z2 gauge-invariant bosonic hopping term finds an improvement of the asymptotic scaling from $\mathcal{O}(\log(S)^2)$ to $\mathcal{O}(1)$ in our framework, as well as a constant factor improvement of better than $10^3$, and the $U(1)$ plaquette term benefits from an improvement from $\mathcal{O}(\log(S))$ to $\mathcal{O}(1)$. Illustrating the advantage of our hybrid qubit-fermion approach over all-qubit hardware, the fermionic fast Fourier transform, a widely-used subroutine in quantum algorithms for materials, finds an improvement from $\mathcal{O}(N\log(N))$ to $\mathcal{O}(\log(N))$ in circuit depth as well as from $\mathcal{O}(N^2)$ to $\mathcal{O}(N)$ in Clifford gate complexity. Our work establishes hybrid qubit-oscillator quantum simulation and qubit-fermion fault-tolerant quantum computation as viable and advantageous methods for the study of the quantum aspects of nature.Bio: Eleanor Crane is a postdoctoral scholar at MIT investigating quantum algorithms using qubits as well as native particles which appear in nature: fermions and bosons. This provides advantages, especially when the goal of the computation is to simulate nature! Previously, she worked part-time for the companies Quantinuum and IBM, and had a joint position between QuICS, JQI and NIST. She did her PhD at University College London working both theoretically and experimentally on donors in silicon. Because she grew up in a superposition of England and France, she strives to bring people and cultures together, and always enjoys an adventure, whatever the nature.
Certifying Almost All Quantum States with Few Single-Qubit Measurements | Qiskit Quantum Seminar Qiskit 2024-08-31 | Episode 173Certifying that an n-qubit state synthesized in the lab is close to the target state is a fundamental task in quantum information science. However, existing rigorous protocols either require deep quantum circuits or exponentially many single-qubit measurements. In this work, we prove that almost all n-qubit target states, including those with exponential circuit complexity, can be certified from only O(n^2) single-qubit measurements. This result is established by a new technique that relates certification to the mixing time of a random walk. Our protocol has applications for benchmarking quantum systems, for optimizing quantum circuits to generate a desired target state, and for learning and verifying neural networks, tensor networks, and various other representations of quantum states using only single-qubit measurements. We show that such verified representations can be used to efficiently predict highly non-local properties that would otherwise require an exponential number of measurements. We demonstrate these applications in numerical experiments with up to 120 qubits, and observe advantage over existing methods such as cross-entropy benchmarking (XEB).Hsin-Yuan Huang (Robert) is a Senior Research Scientist at Google Quantum AI and a Visiting Scientist at MIT. In 2025, he will join California Institute of Technology as an Assistant Professor of Theoretical Physics. He completed his Ph.D. under the mentorship of John Preskill and Thomas Vidick.His research harnesses learning theory to unveil novel insights in physics, information science, and quantum computation. His seminal contributions include classical shadow tomography for learning large-scale quantum systems, provably efficient machine learning algorithms for solving quantum many-body problems, and quantum advantages in learning from experiments. His findings have been published in Nature, Science, Nature Physics, Nature Review Physics, Nature Communications, Physical Review Letters, PRX Quantum, as well as premier conferences in theoretical computer science, such as IEEE Symposium on Foundations of Computer Science (FOCS), and ACM Symposium on Theory of Computing (STOC). Over the past five years, he has been invited to give more than 120 research talks.His doctoral dissertation, "Learning in the Quantum Universe," was honored with the Milton and Francis Clauser Doctoral Prize. This award, bestowed annually to a single doctoral dissertation across all disciplines at Caltech, recognizes work demonstrating the highest degree of originality and potential for opening new avenues of human thought and endeavor. He was also awarded the Ben P. C. Chou Doctoral Prize in Information Science and Technology, a Google Ph.D. fellowship, the Boeing Quantum Creator Prize, the MediaTek Research Young Scholarship, and the Kortschak Scholarship. Starting in 2025, Dr. Hsin-Yuan Huang will hold the position of William H. Hurt Scholar, an endowed early career professorship at California Institute of Technology.
What Its Like to Intern at IBM Quantum | Quantum Crosstalk Qiskit 2024-08-28 | Welcome back to Quantum Crosstalk! In this episode, Dr. Olivia Lanes sits down with former intern turned full time IBM employee Ari Noori and current IBM intern Abigail McClain Gomez to talk about their experiences as well as how to get the most out of an internship. They'll also talk about whether to pursue a Masters or PhD, the challenges of networking and how to do it, and Olivia briefly interviews a surprise special guest at the end! #ibmquantum #qiskit #learnquantum
Exploring Quantum Machine Learning with Meltem Tolunay: Qiskit Summer School 2024 Qiskit 2024-08-24 | In this lecture, we discuss quantum machine learning, with a specific focus on near-term algorithms. After introducing classical and quantum machine learning, we show how a variational quantum circuit can be used as a classifier. In the second half of the lecture, we look at quantum kernels and support vector machines, and conclude the lecture with a discussion on quantum neural networks.--The Qiskit Global Summer School is a two-week intensive summer program designed to empower the quantum researchers and developers of tomorrow with the know-how to explore the world of quantum computing, as well as refresh and sharpen the industry professional’s skills. This fifth-annual summer school will prepare participants for the imminent era of quantum utility. Using hands on examples, the syllabi will guide students through areas of near-term practical importance.Labs and solutions can be found here github.com/qiskit-community/qgss-2024/tree/main
Robust and Efficient Quantum Property Learning with Shallow Shadows | Qiskit Quantum Seminar Qiskit 2024-08-24 | Robust and Efficient Quantum Property Learning with Shallow Shadows with Hong-Ye HuEpisode 172Abstract: Extracting information efficiently from quantum systems is a major component of quantum information processing tasks. Randomized measurements, or classical shadows, enable predicting many properties of arbitrary quantum states using few measurements. While random single qubit measurements are experimentally friendly and suitable for learning low-weight Pauli observables, they perform poorly for nonlocal observables. Prepending a shallow random quantum circuit before measurements maintains this experimental friendliness, but also has favorable sample complexities for observables beyond low-weight Paulis, including high-weight Paulis and global low-rank properties such as fidelity. However, in realistic scenarios, quantum noise accumulated with each additional layer of the shallow circuit biases the results. To address these challenges, we propose the robust shallow shadows protocol. Our protocol uses Bayesian inference to learn the experimentally relevant noise model and mitigate it in postprocessing. and we prove the chosen noise model can mitigate a wide range of quantum noise, including coherent and incoherent errors. The mitigation introduces a bias-variance trade-off: correcting for noise-induced bias comes at the cost of a larger estimator variance. Despite this increased variance, as we demonstrate on a superconducting quantum processor, our protocol correctly recovers state properties such as expectation values, fidelity, and entanglement entropy, while maintaining a lower sample complexity compared to the random single qubit measurement scheme. This combined theoretical and experimental analysis positions the robust shallow shadow protocol as a scalable, robust, and sample-efficient protocol for characterizing quantum states on current quantum computing platforms. Last but not the lease, I will talk about potential applications of robust shallow shadow on learning low energy spectrum of many-body Hamiltonians.Reference:[1]. Hong-Ye Hu, Andi Gu, Swarnadeep Majumder, Hang Ren, Yipei Zhang, Derek S. Wang, Yi-Zhuang You, Zlatko Minev, Susanne F. Yelin, Alireza Seif. Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows. arXiv: 2402.17911[2]. Yizhi Shen, Alex Buzali, Hong-Ye Hu, Katherine Klymko, Daan Camps, Susanne F. Yelin, Roel Van Beeumen. Efficient Measurement-Driven Eigenenergy Estimation with Classical Shadows. (In preparation)Bio: Hong-Ye Hu is currently a Harvard Quantum Initiative Fellow with a focus on the intersection of quantum information science and machine learning. His research interest spans several areas, including the machine learning of quantum systems, quantum simulation, quantum optimal controls, and quantum error mitigation and correction. Hong-Ye earned his PhD from the University of California, San Diego, in 2022. Before that, he completed his Bachelor of Science degree at Peking University in 2016. In addition to his academic pursuits, Hong-Ye has actively collaborated with leading industry players such as IBM Quantum, QuEra Computing, NASA Quantum AI Lab, and Nvidia, contributing his expertise to push the boundaries of quantum computing technology.
Hamiltonian Dynamics: Application and Simulation with Mario Motta - Qiskit Summer School 2024 Qiskit 2024-08-22 | The goal of this lecture is to give an overview of the simulation of Hamiltonian dynamics on a quantum computer. We will explore physical applications of Hamiltonian dynamics and quantum computing algorithms for its simulation. We will then explore the use of Hamiltonian dynamics as a subroutine in quantum algorithms to approximate the Hamiltonian eigenstates of a quantum system.--The Qiskit Global Summer School is a two-week intensive summer program designed to empower the quantum researchers and developers of tomorrow with the know-how to explore the world of quantum computing, as well as refresh and sharpen the industry professional’s skills. This fifth-annual summer school will prepare participants for the imminent era of quantum utility. Using hands on examples, the syllabi will guide students through areas of near-term practical importance.Labs and solutions can be found here github.com/qiskit-community/qgss-2024/tree/main
Solving Optimization Problems with Quantum Algorithms with Daniel Egger: Qiskit Summer School 2024 Qiskit 2024-08-21 | In this course we will cover combinatorial optimization problems and quantum approaches to solve them. In particular, we will cover adiabatic quantum computing and how to derive the quantum approximate optimization algorithm also known as QAOA from it. We will end with a short discussion on some of the aspects to consider when executing such algorithms on superconducting qubits.--The Qiskit Global Summer School is a two-week intensive summer program designed to empower the quantum researchers and developers of tomorrow with the know-how to explore the world of quantum computing, as well as refresh and sharpen the industry professional’s skills. This fifth-annual summer school will prepare participants for the imminent era of quantum utility. Using hands on examples, the syllabi will guide students through areas of near-term practical importance.Labs and solutions can be found here github.com/qiskit-community/qgss-2024/tree/main
Mapping Computational Problems onto Quantum Circuits with Kevin Sung: Qiskit Summer School 2024 Qiskit 2024-08-20 | This lecture explains how computational problems are mapped onto quantum circuits and observables for two application areas. The first part of the lecture discusses combinatorial optimization, using the max cut problem as an example. The second part discusses quantum chemistry, explaining how systems of electrons are mapped onto qubits using the second quantization formalism.--The Qiskit Global Summer School is a two-week intensive summer program designed to empower the quantum researchers and developers of tomorrow with the know-how to explore the world of quantum computing, as well as refresh and sharpen the industry professional’s skills. This fifth-annual summer school will prepare participants for the imminent era of quantum utility. Using hands on examples, the syllabi will guide students through areas of near-term practical importance.Labs and solutions can be found here github.com/qiskit-community/qgss-2024/tree/main
Maximizing Performance on Noisy Quantum Devices with Pedro Rivero: Qiskit Summer School 2024 Qiskit 2024-08-19 | Although quantum computing promises many applications in the fault tolerant regime, today's devices are noisy and we need strategies to extract the best performance out of them. In this lecture we will give an overview of how said strategies are classified and can be used to mitigate and suppress errors in state of the art quantum hardware. In particular, we will look at this task from an algorithm engineering lens, focusing on practical application of these techniques using Qiskit Runtime, and learning only the necessary theory to make informed decisions about when to use the different methodologies.--The Qiskit Global Summer School is a two-week intensive summer program designed to empower the quantum researchers and developers of tomorrow with the know-how to explore the world of quantum computing, as well as refresh and sharpen the industry professional’s skills. This fifth-annual summer school will prepare participants for the imminent era of quantum utility. Using hands on examples, the syllabi will guide students through areas of near-term practical importance.Labs and solutions can be found here github.com/qiskit-community/qgss-2024/tree/main
Ergodicity breaking Transition in East Circuits | Qiskit Quantum Seminar with Tomaž Prosen Qiskit 2024-08-17 | Ergodicity breaking transition in East circuits: From dynamical localizaiton to quantum Ruelle resonances Episode 171I will discuss a family of clean quantum circuits with constrained chiral dynamics, the so-called East circuits. For small enough coupling parameter of the model and incommensurate Trotter time-step, analytical-perturbative as well as tensor-network-based-numerical arguments suggest existence of dynamical localization, i.e. asymptotic exponential localization of time dependent initially localized excitation.For sufficiently large coupling parameter, the dynamics appears ergodic and quantum chaotic with uniform asymptotic exponential relaxation of all local observables. The latter is captured by the concept of quantum Ruelle-Pollicott resonances formulated in terms of the leading nontrivial eigenvalue of an open quantum circuit with absorbing boundary condition (implemented in terms of a reset channel). The corresponding eigenvector exhibits a clean self-similar fractal structure with characteristic power law scaling. Tomaž Prosen has been a full professor of theoretical physics at University of Ljubljana since 2008, where he leads a group on nonequilibrium quantum and statistical physics. He obtained his Ph.D. in 1995 at the same university. His main current research interest is in fundamental problems in dynamical systems, quantum many-body dynamics, quantum chaos, nonequilibrium statistical mechanics, and transport theory.
Understanding Noise in Near-Term Quantum Computers with Haimeng Zhang: Qiskit Summer School 2024 Qiskit 2024-08-15 | When working with near-term quantum computers, it is essential to understand noise and its effect on algorithm implementation. In this lecture, we will explore different types of noise relevant to quantum computers with superconducting qubit architecture. We will cover the noise mechanism, how to mathematically represent noise, and how to characterize noise at scale.--The Qiskit Global Summer School is a two-week intensive summer program designed to empower the quantum researchers and developers of tomorrow with the know-how to explore the world of quantum computing, as well as refresh and sharpen the industry professional’s skills. This fifth-annual summer school will prepare participants for the imminent era of quantum utility. Using hands on examples, the syllabi will guide students through areas of near-term practical importance.Labs and solutions can be found here github.com/qiskit-community/qgss-2024/tree/main
Transforming Quantum Circuits using Qiskits Transpiler with Matthew Treinish: Qiskit Summer School Qiskit 2024-08-14 | Qiskit provides users with a powerful and performant optimizing compiler for quantum circuits with its transpiler. This lecture will describe the operation of Qiskit's transpiler and detail how compilation works in Qiskit. It will cover the specific circuit transformations and analysis performed during compilation.--The Qiskit Global Summer School is a two-week intensive summer program designed to empower the quantum researchers and developers of tomorrow with the know-how to explore the world of quantum computing, as well as refresh and sharpen the industry professional’s skills. This fifth-annual summer school will prepare participants for the imminent era of quantum utility. Using hands on examples, the syllabi will guide students through areas of near-term practical importance.Labs and solutions can be found here github.com/qiskit-community/qgss-2024/tree/main
Start Writing Quantum Code with Abby Mitchell: Qiskit Summer School 2024 Qiskit 2024-08-13 | This lecture is a broad overview of Qiskit's quantum computing software capabilities, including the latest updates for the Qiskit SDK v1.x. From building circuits, to understanding the basics of transpilation, to running programs on real quantum devices - this lecture will cover key concepts you'll need to know to get started writing quantum code!--The Qiskit Global Summer School is a two-week intensive summer program designed to empower the quantum researchers and developers of tomorrow with the know-how to explore the world of quantum computing, as well as refresh and sharpen the industry professional’s skills. This fifth-annual summer school will prepare participants for the imminent era of quantum utility. Using hands on examples, the syllabi will guide students through areas of near-term practical importance.Labs and solutions can be found here github.com/qiskit-community/qgss-2024/tree/main
An Introduction to Qiskit Runtime Primitives Version 2 with Chris Wood: Qiskit Summer School 2024 Qiskit 2024-08-12 | The release of Qiskit 1.0 also introduced version 2 of the Sampler and Estimator primitives interfaces for executing quantum programs. In this lecture we will give an overview of working with these new primitives, and highlight important changes to the input specification of programs as primitive unified blocs (pubs), and the primitive result data formats. We will also give an introduction advanced capabilities available to the Qiskit runtime V2 primitives including Pauli twirling and error mitigation, and how these can be enabled through user configurable primitives options.--The Qiskit Global Summer School is a two-week intensive summer program designed to empower the quantum researchers and developers of tomorrow with the know-how to explore the world of quantum computing, as well as refresh and sharpen the industry professional’s skills. This fifth-annual summer school will prepare participants for the imminent era of quantum utility. Using hands on examples, the syllabi will guide students through areas of near-term practical importance.Labs and solutions can be found here github.com/qiskit-community/qgss-2024/tree/main
Qiskit Quantum Seminar: Chemistry Beyond Exact Solutions on a Quantum-Centric Supercomputer Qiskit 2024-08-10 | Chemistry Beyond Exact Solutions on a Quantum-Centric Supercomputer Episode 170 A universal quantum computer can be used as a simulator capable of predicting properties of diverse quantum systems. Electronic structure problems in chemistry offer practical use cases around the hundred-qubit mark. This appears promising since current quantum processors have reached these sizes. However, mapping these use cases onto quantum computers yields deep circuits, and for for pre-fault-tolerant quantum processors, the large number of measurements to estimate molecular energies leads to prohibitive runtimes. As a result, realistic chemistry is out of reach of current quantum computers in isolation. A natural question is whether classical distributed computation can relieve quantum processors from parsing all but a core, intrinsically quantum component of a chemistry workflow. In this seminar, I will discuss the incorporation of quantum computations of chemistry in a quantum-centric supercomputing architecture, using up to 6400 nodes of the supercomputer Fugaku to assist a Heron superconducting quantum processor. We simulate the N2 triple bond breaking in a correlation-consistent cc-pVDZ basis set, and the active-space electronic structure of [2Fe–2S] and [4Fe–4S] clusters, using 58, 45 and 77 qubits respectively, with quantum circuits of up to 10570 (3590 2-qubit) quantum gates. Javier is a Research Scientist at IBM. His research focuses on the use of quantum computers and machine learning techniques to simulate quantum many-body systems, and in particular electronic-structure problems. He obtained his PhD in Physics from New York University in 2023, where he developed machine learning techniques to tackle the quantum many-body problem. Prior to that, he obtained his B.S. in Physics from the Universidad Autónoma de Madrid in 2017.
Can We Scale Error Correction in Quantum Computing? - Quantum Paper Review Qiskit 2024-08-07 | On this episode of Crosstalk, Abby sits down with IBM error correction scientists Ted Yoder and Sergey Bravyy (IBM Fellow) to discuss their most recent publication, “High threshold and low overhead fault tolerant quantum memory.”This landmark paper discusses low-density parity-check (LDPC) codes for error correction on quantum computers. Error correction is a notoriously difficult problem, and Sergey and Ted explain how this work makes scalability much easier and paves the way for real-world implementation.Read more about error correcting codes for near-term quantum computers here: ibm.com/quantum/blog/error-correction-codesLink to Nature paper: nature.com/articles/s41586-024-07107-7Join the Discussion:Share your thoughts and questions about this groundbreaking research in the comments section below. Engage with the scientific community and explore the possibilities of quantum computing together!Stay Updated:If you're fascinated by the world of quantum computing and scientific exploration, don't forget to like, subscribe, and hit the notification bell to stay updated on our latest episodes and quantum discoveries.Thank You for Watching!#QuantumComputing #ResearchReview #Qiskit #IBMQuantum #Science #Innovation
General measurements | Understanding Quantum Information & Computation - Lesson 11 Qiskit 2024-07-31 | Measurements provide an interface between quantum and classical information; when a system is measured, classical information about its state is extracted (and the state is generally changed in the process). This lesson describes measurements in full generality, including different ways that general measurements can be described in mathematical terms. It also describes quantum state discrimination and quantum state tomography, which are important notions connected with measurements.Find the written content for this lesson on IBM Quantum Learning: learning.quantum.ibm.com/course/general-formulation-of-quantum-information/general-measurements0:00 — Introduction1:19 — Overview2:35 — Descriptions of measurements5:02 — Measurements as matrices10:18 — Examples13:59 — Measurements as channels17:24 — Partial measurements22:06 — Naimark's theorem23:07 — Proof of Naimark's theorem28:07 — Non-destructive measurements31:08 — State discrimination & tomography33:55 — Discriminating pairs of states38:38 — Discriminating 3 or more states40:50 — Quantum state tomography42:13 — Qubit tomography45:19 — ConclusionLink to the full Understanding Quantum Information & Computation Series: youtube.com/playlist?list=PLOFEBzvs-VvqKKMXX4vbi4EB1uaErFMSO&si=EbuCeifokHs_RSJ5#ibmquantum #qiskit #learnquantum
2024 Qiskit Global Summer School Closing Ceremony & Panel Qiskit 2024-07-26 | Olivia Lanes, Zlatko Minev, Pedro Rivero, Derek Wang, Abhinav Kandala & Jen Glick
