Sebastian interviews Professor Lin Lin during the System One ribbon cutting event at Rensselaer Polytechnic Institute in Troy, NY. Professor Lin Lin's journey from computational mathematics to quantum chemistry has been driven by his fascination with modeling nature through computation. As a student at Peking University, he was intrigued by the concept of first principles modeling, which aims to simulate chemical systems using minimal information such as atomic species and positions. Lin Lin pursued this interest during his PhD at Princeton University, working with mathematicians and chemists to develop better algorithms for density functional theory (DFT). DFT reformulates the high-dimensional quantum chemistry problem into a more tractable three-dimensional one, albeit with approximations. While DFT works well for about 95% of cases, it struggles with large systems and the remaining "strongly correlated" 5%. Lin Lin and his collaborators radically reformulated DFT to enable calculations on much larger systems, leading to his faculty position at UC Berkeley in 2014.In 2018, a watershed year marked by his tenure, Lin Lin decided to tackle the challenging 5% of strongly correlated quantum chemistry problems. Two emerging approaches showed promise: artificial intelligence (AI) and quantum computing. Both AI and quantum computing are well-suited for handling high-dimensional problems, albeit in fundamentally different ways. Lin Lin aimed to leverage both approaches, collaborating on the development of deep molecular dynamics using AI to efficiently parameterize interatomic potentials. On the quantum computing side, his group worked to reformulate quantum chemistry for quantum computers. Despite the challenges posed by the COVID-19 pandemic, Lin Lin and his collaborators have made significant strides in combining AI and quantum computing to push the boundaries of computational chemistry simulations, bridging the fields of mathematics, chemistry, AI, and quantum computing in an exciting new frontier.Thanks again to Professor Lin and everyone at RPI for hosting me and providing such an amazing opportunity to interview so many brilliant researchers.

Sebastian is joined by Olivia Lanes, Global Lead for Education and Learning, IBM Quantum to discuss quantum education, IBM's efforts to provide resources for workforce development, the importance of diversity and equality in STEM, and her own personal journey from experimental physics to community building and content creation. Recorded on the RPI campus during the launch event of their IBM System One quantum computer. Key Topics:- Olivia's background in experimental quantum physics and transition to education at IBM Quantum- Lowering barriers to entry in quantum computing education through IBM's Quantum Experience platform, Qiskit open source framework, and online learning resources- The importance of reaching students early, especially women and people of color, to build a diverse quantum workforce pipeline- Quantum computing as an interdisciplinary field requiring expertise across physics, computer science, engineering, and other domains- The need to identify real-world problems and use cases that quantum computing can uniquely address- Balancing the hype around quantum computing's potential with setting realistic expectations - International collaboration and providing global access to quantum education and technologies- The unique opportunity of having an IBM quantum computer on the RPI campus to inspire students and enable cutting-edge researchResources Mentioned: - IBM Quantum learning platform - "Introduction to Classical and Quantum Computing" by Tom Wong- Qiskit YouTube channelIn summary, this episode explores the current state of quantum computing education, the importance of making it accessible to a broad and diverse group of students from an early age, and how academia and industry can partner to build the quantum workforce of the future. Olivia provides an insider's perspective on IBM Quantum's efforts in this space.

For this episode, Sebastian is on his own, as Kevin is taking a break. Sebastian accepted a gracious invite to the ribbon cutting event at Rensselaer Polytechnic Institute in Troy, NY, where the university was launching their on-campus IBM System One -- the first commercial quantum computer on a university campus!This week, the episode is a recording a live event hosted by Sebastian. The panel of RPI faculty and staff talk about their decision to deploy a quantum computer in their own computing center -- a former chapel from the 1930s! - what they hope the RPI community will do with the device, and the role of academic partnership with private industry at this stage of the development of the technology. Joining Sebastian on the panel were:James Hendler, Professor and Director of Future of Computing InstituteJackie Stampalia, Director, Client Information Services, DotCIOOsama Raisuddin, Research Scientist, RPILucy Zhang, Professor, Mechanical, Aerospace, and Nuclear Engineering

Dr. Martin Savage is a professor of nuclear theory and quantum informatics at the University of Washington. His research explores using quantum computing to investigate high energy physics and quantum chromodynamics.Dr. Savage transitioned from experimental nuclear physics to theoretical particle physics in his early career. Around 2017-2018, limitations in classical computing for certain nuclear physics problems led him to explore quantum computing.In December 2022, Dr. Savage's team used 112 qubits on IBM's Heron quantum processor to simulate hadron dynamics in the Schwinger Model. This groundbreaking calculation required 14,000 CNOT gates at a depth of 370. Error mitigation techniques, translational invariance in the system, and running the simulation over the December holidays when the quantum hardware was available enabled this large-scale calculation.While replacing particle accelerator experiments is not the goal, quantum computers could eventually complement experiments by simulating environments not possible in a lab, like the interior of a neutron star. Quantum information science is increasingly important in the pedagogy of particle physics. Advances in quantum computing hardware and error mitigation are steadily enabling more complex simulations.The incubator for quantum simulation at University of Washington brings together researchers across disciplines to collaborate on using quantum computers to advance nuclear and particle physics.Links:Dr. Savage's home pageThe InQubator for Quantum SimulationQuantum Simulations of Hadron Dynamics in the Schwinger Model using 112 QubitsIBM's blog post which contains some details regarding the Heron process and the 100x100 challenge.

In this episode, Sebastian and Kevin interview Professor Yufei Ding, an associate professor at UC San Diego, who specializes in the intersection of theoretical physics and computer science. They discuss Dr. Ding's research on system architecture in quantum computing and the potential impact of AI on the field. Dr. Ding's work aims to replicate the critical stages of classical computing development in the context of quantum computing. The conversation explores the challenges and opportunities in combining computer science, theoretical and experimental quantum computing, and the potential applications of quantum computing in machine learning.TakeawaysYufei Ding's research focuses on system architecture in quantum computing, aiming to replicate the critical stages of classical computing development in the context of quantum computing.The combination of computer science, theoretical and experimental quantum computing is a unique approach that offers new insights and possibilities.AI and machine learning have the potential to greatly impact quantum computing, and finding a generically applicable quantum advantage in machine learning could have a transformative effect.The development of a simulation framework for exploring different system architectures in quantum computing is crucial for advancing the field and identifying viable outcomes.Chapters00:00 Introduction and Background02:12 Yufei Ding's System Architecture03:08 AI and Quantum Computing04:19 Conclusion

In this special solo episode recorded at Q2B Paris 2024, Sebastian talks with Houlong Zhuang, assistant professor at Arizona State University, about his work in material science. Dr. Zhuang discusses his research on using quantum computing and machine learning to simulate high entropy alloy materials. The goal is to efficiently predict material properties and discover new material compositions.Density functional theory (DFT) is a commonly used classical computational method for materials simulations. However, it struggles with strongly correlated electronic states. Quantum computers have the potential to efficiently simulate these challenging quantum interactions.The research uses classical machine learning models trained on experimental data to narrow down the vast combinatorial space of possible high entropy alloy compositions to a smaller set of promising candidates. This is an important screening step.Quantum machine learning and quantum simulation are then proposed to further refine the predictions and simulate the quantum interactions in the materials more accurately than classical DFT. This may enable prediction of properties like stability and elastic constants.Key challenges include the high dimensionality of the material composition space and the noise/errors in current quantum hardware. Hybrid quantum-classical algorithms leveraging the strengths of both are a promising near-term approach.Ultimately, the vision is to enable inverse design - using the models to discover tailored material compositions with desired properties, potentially reducing experimental trial-and-error. This requires highly accurate, explainable models.In the near-term, quantum advantage may be realized for specific local properties or excited states leveraging locality of interactions. Fully fault-tolerant quantum computers are likely needed for complete replacement of classical DFT.Continued development of techniques like compact mappings, efficient quantum circuit compilations, active learning, and quantum embeddings of local strongly correlated regions will be key to advancing practical quantum simulation of realistic materials.In summary, strategically combining machine learning, quantum computing, and domain knowledge of materials is a promising path to accelerating materials discovery, but significant research challenges remain to be overcome through improved algorithms and hardware. A hybrid paradigm will likely be optimal in the coming years.Some of Dr. Zhuang's papers include: Quantum machine-learning phase prediction of high-entropy alloysSudoku-inspired high-Shannon-entropy alloysMachine-learning phase prediction of high-entropy alloys

No guest this episode! Instead, Kevin and Sebastian have a conversation looking back on the events of 2023 in quantum computing, wiht a particular focus on three trends: some waning of enthusiasm in the private sector, a surge of investments from the public sector as national and regional governments invest in the quantum computing value chain and the shift from a focus on NISQ to logical qubits. Qureca's overview of public sector quantum initiatives in 2023Preskill's NISQ paper from 2018 (yes, I was off by a few years!)The paper that introduced the idea of VQE: A variational eigenvalue solver on a quantum processor by Peruzzo et alA variation on VQE that still has some promise An adaptive variational algorithm for exact molecular simulations on a quantum computer by Grimsley et alMitiq, a quantum error mitigation framework from Unitary FundPeter Shor's first of its kind quantum error correction in the paper Scheme for reducing decoherence in quantum computer memoryQuantinuum demonstrates color codes to implement a logical qubit on their ion trap machine, H-1Toric codes introduced in Fault-tolerant quantum computation by anyons by Alexei KitaevSurface codes and topological qubits introduced in Topological quantum memory by Eric Dennis, Alexei Kitaev, Andrew Landahl, and John PreskillThe threshold theorem is laid out in Fault-Tolerant Quantum Computation With Constant Error Rate by Dorit Aharonov and Michael Ben-OrThe GKP variation on the surface code appears in Encoding a qubit in an oscillator by Daniel Gottesman, Alexei Kitaev, John PreskillA new LDPC based chip architecture is described in High-threshold and low-overhead fault-tolerant quantum memory by Sergey Bravyi, Andrew W. Cross, Jay M. Gambetta, Dmitri Maslov, Patrick Rall, Theodore J. YoderNeutral atoms are used to create 48 logical qubits in Logical quantum processor based on reconfigurable atom arrays by Vuletic's and Lukin's groups at MIT and Harvard respectivelyIf you have an idea for a guest or topic, please email us.Also, John Preskill has agreed to return to answer questions from our audience so please send any question you'd like Professor Preskill to answer our way at info@the-new-quantum-era.com

Kevin and Sebastian are joined by Dr. Vladan Vuletic, the Lester Wolfe Professor of Physics at the Center for Ultracold Atoms and Research in the Department of Physics at the Massachusetts Institute of TechnologyAt the end of 2023, the quantum computing community was startled and amazed by the results from a bombshell paper published in Nature on December 6th, titled Logical quantum processor based on reconfigurable atom arrays  in which Dr. Vuletic's group collaborated with Dr Mikhail Lukin's group at Harvard to create 48 logical qubits from an array of 280 atoms. Scott Aaronson does a good job of breaking down the results on his blog, but the upshot is that this is the largest number of logical qubits created, and a very large leap ahead for the field. 00:00 Introduction and Background01:07 Path to Quantum Computing03:30 Rydberg Atoms and Quantum Gates08:56 Transversal Gates and Logical Qubits15:12 Implementation and Commercial Potential23:59 Future Outlook and Quantum Simulations30:51 Scaling and Applications32:22 Improving Quantum Gate Fidelity33:19 Advancing Field of View Systems33:48 Closing the Feedback Loop on Error Correction35:29 Quantum Error Correction as a Remarkable Breakthrough36:13 Cross-Fertilization of Quantum Error Correction Ideas

SummaryIn this episode, Sebastian and Kevin are joined by Chiara Decaroli, a quantum physicist and venture capitalist. Chiara shares her unique journey into the field of quantum, starting from a small village in Italy to earning her PhD in quantum physics. She explains the history of ion trapping and how it led to the development of quantum computing. Chiara also discusses the strengths and weaknesses of trapped ion systems and the challenges of investing in early-stage quantum startups. In this conversation, Chiara Decaroli discusses the challenges of assessing quantum technologies and the deep expertise required in the field. She also shares her experience in gaining familiarity with different quantum modalities and the importance of multidisciplinarity in the quantum field. Chiara highlights the skills needed in the quantum industry, emphasizing the need for deep knowledge in physics and specialized segments. She also discusses the importance of cross-disciplinary education and the potential impact of quantum technologies.TakeawaysChiara's path to quantum started from a small village in Italy and led her to earn a PhD in quantum physics at ETH Zurich.Ion trapping is a key technology in quantum computing, and it has a rich history dating back to the 1930s.Trapped ions can be manipulated using laser beams to perform single and two-qubit gates.Trapped ion systems have the advantage of perfect qubits but face challenges in scalability and speed of operations.Investing in quantum startups requires a deep understanding of the field and the ability to navigate the early-stage landscape. Assessing quantum technologies requires deep expertise and a scientific background.Gaining familiarity with different quantum modalities requires extensive reading and talking to experts in the field.The quantum field is highly multidisciplinary, requiring expertise in physics, engineering, software development, and specialized domains.Cross-disciplinary education is important in the quantum field to foster innovation and solve complex problems.The potential impact of quantum technologies is immense, but it is challenging to predict the exact applications and advancements.Chapters00:00 Introduction and Background01:01 Chiara's Path to Quantum08:13 History of Ion Trapping19:47 Implementing Gates with Trapped Ions27:24 Strengths and Weaknesses of Trapped Ion Systems35:49 Venture Capital in Quantum37:55 The Challenges of Assessing Quantum Technologies39:12 Gaining Familiarity with Different Quantum Modalities40:27 The Multidisciplinary Nature of Quantum Technologies41:22 Skills Needed in the Quantum Field42:58 The Importance of Cross-Disciplinary Education44:27 The Potential Impact of Quantum Technologies

In this episode of The New Quantum Era, Kevin Rowney and Sebastian Hassinger are joined by Dr. Ieva Čepaitė to delve into the nuanced world of quantum physics and computation. Dr. Čepaitė discusses her journey into quantum computing and her work on counterdiabatic methods used to optimize the control of many body quantum states. She provides an overview of the landscape of new algorithms available within the field. She points out the importance of understanding the hardware to implement a quantum algorithm effectively. The focus then shifts to a discussion on adiabatic and counterdiabatic systems, providing a detailed understanding of both methods. The conversation concludes with a speculative take on future breakthroughs that could emerge with respect to quantum algorithms.00:31 Introduction and Overview of the Interview02:43 Dr. Čepaitė's Journey into Quantum Computing05:23 Dr. Čepaitė's Diverse Experience in Quantum Computing09:37 The Challenges and Opportunities in Quantum Computing11:50 Understanding Adiabatic and Counterdiabatic Systems15:15 The Potential of Counterdiabatic Techniques in Quantum Computing25:49 The Future of Quantum Algorithms32:55 The Role of Quantum Machine Learning35:48 Closing Remarks and Reflections

In this interview, independent quantum information science researcher and consultant, Dr. Cassandra Grenade, shares their journey from triple majoring in physics, math, and computer science to their current consulting work with their firm, Dual Space Solutions. She discusses the concept behind the Quantum Intermediate Representation project (QIR), a tool which represents quantum programs and allows language designers to work independently of specific quantum processor details. Cassandra explains how QIR can solve the 'N to M' problem, where multiple language designs must interface with multiple quantum hardware architectures, thereby preventing the need for creating numerous unique compilers. Further, she dives into the evolution and future of quantum computing, highlighting the need for an industry-wide shift in understanding a quantum computer as more than just a circuit-based entity.00:02 Introduction and Guest Background00:22 Cassandra's Journey into Quantum Computing01:40 The Birth of Dual Space Solutions05:35 The Importance of Interdisciplinary Approach in Quantum Computing08:14 The Challenges and Solutions in Quantum Computing10:42 The Role of Quantum Intermediate Representation (QIR)15:56 The Impact of QIR on Quantum Computing19:01 The Future of Quantum Computing with QIR

Misty Wahl of the Unitary Fund joins us for this episode to talk about quantum error mitigation strategies like zero noise extrapolation (ZNE) and probabilistic error reduction using the Mitiq open source framework. Misty is a lead contributor the the Mitiq project as well as an author on a number of recent papers on the topic. We'll discuss the current state of the art, potential future strategies that leverage machine learning and quantum error correction, and how the Mitiq framework makes it easier to code up and compare mitigation strategies on a wide variety of qubits and SDKs. You can find a sampling of Misty's reasearch papers and talk on her personal website, mistywahl.comError mitigation in quantum computing with Misty Wall. 0:02Misty Wahl, technical staff at Unitary Fund, discusses Mitiq project for error mitigation in quantum computers.Misty discusses the growth of quantum computing as a field, with a focus on the Unitary Fund and its role in developing error mitigation techniques.Non-traditional background in quantum computing. 3:31Misty Wahl shares her non-traditional background in mechanical engineering and project management, transitioning to quantum software development and research through self-study and online courses.Misty joined Mitiq as a full-time technical staff member in March 2022, contributing to quantum error mitigation and software development through their experience with unitary hack.Unitary Hack is a unique event hosted by Unitary Fund, where participants can tag issues in their GitHub repos and community can choose to solve them, providing valuable experience and connections in the quantum computing field.Quantum error mitigation techniques and software frameworks. 8:31Misty Wahl describes her experience with the Mitiq frameworkMisty explains how zero noise extrapolation worksMisty Wahl: By intentionally adding noise to quantum computations, researchers can extrapolate to the zero noise limit and estimate the optimal value of an expectation value.Quantum error mitigation techniques. 21:57Misty believes that error mitigation will be crucial in the transition to fault-tolerant quantum computers, and will be used to enhance results at every step.Misty presents a technique combining quantum error mitigation and quantum error correction to scale the distance of the surface code and improve error rate.Quantum computing, open source, and research funding. 28:56Unitary Fund is building an open-source quantum community through community calls on Discord, with the goal of fostering collaboration and advancing quantum computing.Unitary Fund is a 501(c)(3) nonprofit that funds research and development projects in AI, blockchain, and more through government grants and corporate sponsorships.

In this episode, Kevin and Sebastian are joined by Alex Keesling, CEO of QuEra Computing, for a discussion about his work with neutral atom arrays for simulation and computation. Alex describes his very early introduction to quantum information science as a high school student in Mexico, which kicked off a defining fascination with the field. At MIT as an undergraduate he started working with photonic systems, and as a PdD student with Misha Lukin at Harvard he played an instrumental role in the "atom array" project that eventually was spun out as QuEra. Today, QuEra's Aquila device has 256 atoms in its array that can be used as for analog Hamiltonian simulations, and is accessible on the cloud via AWS' Braket service. Alex explains in detail how these devices work, what physics breakthroughs they rely on for their operation, and where they may be going in the future with work underway on digital gates for universal computation. Additionally Alex takes us through some of the incredible scientific results these devices have already made possible, and discusses what the future of both scientific and commercial applications might hold. The QuEra team published a deep dive into their Aquila device and its capabilities in a paper called Aquila: QuEra's 256-qubit neutral-atom quantum computer.

In this episode of The New Quantum Era, hosts Sebastian Hassinger and Kevin Rowney interview Daniel Stick, a researcher at Sandia National Lab. They discuss the fascinating world of ion traps, a novel approach to quantum computing architecture. Stick explains the concept of suspending atoms inside a radio frequency Paul trap and utilizing laser pulses to manipulate their qubit states. The conversation also delves into the advantages and limitations of ion traps compared to other architectures. Stick shares exciting advancements in their technology, including the enchilada trap, developed as part of the Quantum Systems Accelerator project. Tune in to learn more about the cutting-edge research happening in the field of quantum computing.[00:07:14] Large scale ion trap. [00:10:29] Entangling gates. [00:14:14] Major innovations in magneto optical systems. [00:17:30] The Name "Enchilada" [00:21:16] Combining chains for collective gates. [00:27:02] Sympathetic cooling and decoherence. [00:30:16] Unique CMOS application. [00:33:08] CMOS compatible photonics. [00:38:04] More breakthroughs on accuracy. [00:41:39] Scaling quantum computing systems. [00:45:00] Private industry and technology scaling. [00:51:36] Ion trap technology progress. [00:54:39] Spreading the word and building community.00:01:15 - "So these architectures have, I think, powerful advantages versus other architectures."00:18:30 - "So that was the name."00:23:34 - "That's correct. That's that is one of the selling points for trapped ion quantum computing is that there is no threshold temperature at which you make the qubit go from behaving really well to behaving, you know, above which things would operate really poorly."00:35:37 - "That is the grand vision that you've got this chip sitting inside of a chamber, and a bunch of digital signals go in and out of it."00:38:40 - "What's a few exponents between friends anyway?"00:41:39 - "That is one of the things that we have to think about is our gates are just, I don't know, 100 times to a thousand times slower than superconducting quantum computing systems or solid state quantum computing systems and ways to get around that have to leverage other kind of other attempts that are not limited by the physical speeds that are possible with an ion trap."00:48:43 - "Do you have a paperclip, Kevin? That's all you need."

Title: Operating at the Quantum Limit with Dr. Dana Anderson“In 25 to 30 years, quantum is going to be in the kitchen, sitting next to the toaster.” — Dr. Dana AndersonDescription: Welcome to another episode of The New Quantum Era Podcast hosted by Kevin Rowney and Sebastian Hassinger. Today, they are joined by Dr. Dana Anderson to talk about quantum computation, simulation, and sensing technologies using ultracold neutral atoms. Dr. Anderson is Chief Strategy Officer of Infleqtion, which was founded in 2007 as ColdQuanta and recently changed its name after acquiring Super.tech. Dr. Anderson is an applied physicist trained in quantum optics with extensive experience in optical neural networks, signal processing, precision measurement, and what he calls the field of “atomtronics.”Key Takeaways:[3:34] Dr. Anderson shares how he found his passion in physics and his entry point to quantum information science in general.[5:13] How do lasers make atoms cold?[7:13] Does Dr. Anderson think that what was learned from building atomic clocks and quantum devices has accelerated the development and maturation of the technologies behind the neutral atom arrays?[10:44] Dr. Anderson talks about the optical lattice.[12:41] Dr. Anderson addresses the early dawn of the transistor and the parallels with what he calls our age of atomtronics.[14:00] Does Dr. Anderson think components on the optical side continue to shrink?[15:17] Dr. Anderson explains how he uses machine learning to train an interferometer.[17:44] Would machine learning assist in qubit control?[25:05] What kind of new sensing technologies will emerge into the market?[27:31] Dr. Anderson shares NASA developments regarding climate change.[29:31] There will be a home-use application for quantum (and it will be boring, according to Dr. Anderson).[31:48] Dr. Anderson discusses the benefits of meeting quantum and machine learning.[36:06] Dr. Anderson helps us understand how the Infleqtion platform and quantum computation could emerge as a set of practical outcomes.[45:02] Sebastian and Dr. Anderson discuss Infleqtion’s acquisition of Super.tech and what they have been working on.[47:18] What does Dr. Anderson see on the horizon for the next 12 to 24 months for neutral atoms?Mentioned in this episode:Visit The New Quantum Era PodcastThe Nobel Prize in physics for Bose Einstein Condensates Learn more about InfleqtionNASA Cold Atom Lab Tweetables and Quotes:“Every atom is a qubit, and every atom is just like every other atom, and it is as perfect as it could be.“ — Dr. Dana Anderson“Roughly speaking, the way to think about everything Infleqtion can be boiled down to atomtronics.” — Dr. Dana Anderson“If you are not operating at a quantum limit, you are not competitive .” — Dr. Dana Anderson

If anyone needs no introduction on a podcast about quantum computing, it's John Preskill. His paper "Quantum Computing in the NISQ era and beyond," published in 2018, is the source of the acronym "NISQ," for Noisy, Intermediate Scale Quantum" computers -- basically everything we are going to build until we get to effective error correction. It's been cited almost 6000 times since, and remains essential reading to this day.John is a particle physicist and professor at Caltech whose central interests are actually cosmology, quantum matter, and quantum gravity -- he sees quantum computing as a powerful means to gain more understanding of the fundamental behavior of our universe. We discuss the information paradox of black holes, quantum error correction, some history of the field, and the work he's doing with his PhD student Robert (Hsin-Yuan) Huang using machine learning to estimate various properties of quantum systems. How did you become interested in quantum information? 5:13The discovery of Shor’s algorithm. 10:11Quantum error correction. 15:51Black holes and it from qubit. 21:19Is there a parallel between error correcting codes and holographic projection of three dimensions? 27:27The difference between theory and experiment in quantum matter. 38:56Scientific applications of quantum computing. 55:58Notable links:The Physics of Quantum Information, adapted from John's talk at the Solvay Conference on the Physics of InformationQuantum Computing 40 Years Later, an update to John's NISQ paper on the occasion of the 40th anniversary of the conference at Endicott, the Physics of Computation.Lecture notes for John's class on quantum computing at Caltech, PH229Predicting many properties of a quantum system from very few measurements, one of the papers Robert Huang has published with John, appearing in Nature PhysicsTweetables and Quotes:“The idea that you can solve problems efficiently that you'd never be able to solve because it's a quantum world and not a world governed by classical physics, I thought that was one of the coolest ideas I'd ever encountered.” — John Preskill“There's something different about quantum information than ordinary information. You can't look at it without disturbing it.” — John Preskill“Ideas which were being developed without fundamental physics, necessarily in mind, like quantum error correction, have turned out to be very relevant in other areas of physics.” — John Preskill“Thinking about quantum error correction in the context of gravitation led us to construct new types of codes which weren't previously known. “ — John Preskill“With quantum computers and quantum simulators, we can start to investigate new types of matter, new phases, which are far from equilibrium.“ — John Preskill.

Welcome to another episode of The New Quantum Era Podcast hosted by Kevin Rowney and Sebastian Hassinger. Today, they are joined by another distinguished researcher, Dr. Harry Buhrman. Dr. Buhrman is a professor at the University of Amsterdam, he's a director at the CWI, and he's the director at Qusoft as well. He's got a long and illustrious career in quantum information. Today, Dr. Buhrman takes us through some of his earlier work and some of his areas of interest, and he also discloses details of his recent paper which was going to be called Ultra Fast Quantum Circuits for Quantum State Preparation, but was posted to the arXiv as State preparation by shallow circuits using feed forward, which provides fascinating results with respect to the core architecture divided into four layers and time complexity around that framework.Key Takeaways:[4:45] Sebastian introduces Dr. Harry Buhrman.[5:31] How did Dr. Buhrman become interested in Quantum Computing?[9:31] Dr. Buhrman remembers the first time he heard about the complexity class known as fast quantum polynomial time, or BQP.[11:35]  Dr. Buhrman and Richard Cleve started working on communication complexity.[14:14] Dr. Buhrman discusses the opportunity that arose after Shor’s algorithm.[14:53] Dr. Buhrman has also written biology papers explaining how he became involved in this field.[18:05] Is quantum computation and quantum algorithms the main focus now regarding Dr. Buhrman’s areas of study?[20:06] Software and hardware are codependent, so codesigning is needed.[20:58]. What are the big unsolved problems in the areas of time complexity and hierarchy for quantum? [24:50] Does Dr. Buhrman think it's possible that there could be a future where some of the classical time complexity problems could be powerfully informed by quantum information science and Quantum Time complexity discovery?[27:32] Does Dr. Buhrman think that, over time, the distinction between classical information theory and quantum information theory will erode?[28:50] Dr. Burhman talks about his Team's most recent paper.[33:55]  Dr. Buhrman’s group is using tmid-circuit measurement and classical fan out to extend the amount of computation time [35:04] How does this approach differ from VQE or QAOA?[38:35] About Dr. Buhrman’s current paper, is he thinking through algorithms that may be able to be implemented in at least toy problems sort of scale to try this theory out and implementation?{39:22] Sebastian talks about  QubiC, an open-source Lawrence Berkeley National Lab project.[41:14]  Dr. Buhrman recognizes he is very much amazed by the fact that when he started in this field in the mid-late 90s, it was considered very esoteric and beautiful but probably wouldn't lead to anything practical.[43:49] Dr. Buhrman assures that there is a chance that those intractable problems for classical computing also remain intractable for quantum computers.[44:24] What's the next big frontier for Dr. Buhrman and his team?[47:03] Dr. Buhrman explains Quantum Position Verification used for implementing secure communication protocols.[50:56] Sebastian comments on the hilarious and interesting titles for papers Dr. Buhrman comes up with.[53:10] Kevin and Sebastian share the highlights of an incredible conversation with Dr. Buhrman.Mentioned in this episode:Visit The New Quantum Era PodcastQuantum entanglement and communication complexityThe first peptides: the evolutionary transition between prebiotic amino acids and early proteinsA Qubit, a Coin, and an Advice String Walk Into a Relational ProblemSix hypotheses in search of a theoremTweetables and Quotes:“ Biological processes are quantum mechanical, and sometimes you need the quantum mechanical description to understand them, and indeed, quantum computers could be of great help in simulating them and understanding them better than we currently do.“ — Dr. Harry Buhrman“There's a huge gap between what we can do and what we can prove is true.“ — Dr. Harry Buhrman“Our problems have become bigger but also more interesting, I would say.“ — Dr. Harry Buhrman“We're not the first ones to see that having mid-computation measurements plus classical feed forwards actually is very useful and can help you solve problems or generate states that if you don't have this  are impossible  to make.” — Dr. Harry Buhrman“Big companies are very interested in QC not only for building quantum computers but also figuring out whether it is useful from a software point of view. ” — Dr. Harry Buhrman

Welcome to another episode of The New Quantum Era Podcast hosted by Kevin Rowney and Sebastian Hassinger. Today, they are joined by an outstanding European researcher: Professor Leo Kouwenhoven.Leo is a professor in Applied Physics specialized in the field of Quantum NanoScience at TU Delft. Leo got his Ph.D. in Mesoscopic Physics at Delft. He was a postdoc researcher at the University of California at Berkeley and a visiting professor at Harvard. Highlights in Leo’s career include the discovery of conductance quantization in quantum point contacts, Coulomb blockade in quantum dots, artificial atoms, the Kondo effect in quantum dots, Spin qubits, induced superconductivity in nanowires and nanotubes, spin-orbit qubits in nanowires and nanotubes and Majoranas in nanowires. Leo and his group found evidence of Majoranas detailed in a paper from 2012. He lead the Microsoft hardware R&D effort, working on topological qubits using Majorana zero modes from 2016 to 2022. His current focus at Delft is on topological effects in solid-state devices, such as the emergence of Majoranas and topological qubits.Key Takeaways:[2:53] Kevin and Sebastian share their appreciation about how quantum computing was represented in the episode Joan is Awful of the TV show Black Mirror. [6:04] Leo shares how he got interested in the field of quantum computing.[9:40] Leo discusses how much he knew about the work done in theoretical quantum computing in the mid to late 90s.[14:37] The advantage of superconducting qubits is that you have a large number of electrons in the circuit you are manipulating.[15:34] Measurability can be easier but “it always comes with a price”.[17:05] Leo admits the coherence was insufficient, and he shares how they tried to improve it.[19:15] What is the feature of silicon that makes it valuable for Quantum Computing?[22:12] Leo shares the benefits of a hybrid system (combining super connectivity and semi-connectors).[23:10] Leo discusses how he became interested in Majoranas.[27:30] Leo addresses the main research agenda destination regarding Majoranas.[28:22] Was the Majoranas fundamental particle found?[33:21] The potential for theory and application is so huge. What's Leo’s sense about the prospects for these avenues of inquiry research?[36:25] Leo explains the non-abelian property that Majoranas zero modes have.[40:18] Leo addresses the two groups of gate operations needed for universal computing.[41:22] Leo gives his opinion regarding the timeframe for the appearance of commercially viable outcomes in this domain. [47:16] Sebastian reflects on the maturation of the neutral atom systems, considering them as the first realization of Feynman's vision from 1981 regarding the fact that in order to simulate a natural system, there is a need for a quantum computer to do it.[48:08] Can we build machines that can help us simulate the dynamics of quantum systems that might help us understand more what the challenges are in Majorana Qubit? [51:01] Does Leo think there's any value in Majorana braiding simulations to try to understand the dynamics of the system or overcome the challenges?[53:50] There is room for optimism in Quantum Computing.[56:24] Leo talks about the dream of topological Majoranas qubit.  [58:16] Kevin and Sebastian share the highlights of an insightful conversation with Leo Kouwenhoven. Mentioned in this episode:Visit The New Quantum Era PodcastBlack Mirror: Joan is AwfulLearn more about Leo KouwenhovenSignatures of Majorana fermions in hybrid superconductor-semiconductor nanowire devicesTweetables and Quotes:“The advantage of the superconducting qubits is that you have a large number of electrons in the circuit you are manipulating, which can make measurability easier, but it always comes with a price.”— Leo Kouwenhoven“I read that making qubits was too much engineering when it should be something more fundamental… so now we think qubits are fundamental?!” — Leo Kouwenhoven“Problems are there to be solved; they only exist to be solved. People in classical electronics also solved all their problems, so why can’t we? ” — Leo Kouwenhoven

Description: Welcome to another episode of The New Quantum Era Podcast hosted by Kevin Rowney and Sebastian Hassinger. Today, they are joined by Scott Aaronson, who is a leading authority in the space of Quantum Computing, a fascinating person with a long list of relevant achievements. Scott is also the author of an outstanding blog called Shtetl-Optimize and a book named Quantum Computing Since Democritus.Scott helped design Google Quantum Supremacy, but his work exceeds it; he is involved in Complexity Theory and Computer Science and is just extremely good at connecting, explaining, and digging deeper into concepts.Key Takeaways:[3:38] How did Scott get into quantum computing?[11:35] Scott talks about the moment when the question arose: Does nature work this way?[14:28] Scott shares when he realized he wanted to dig deeper into Quantum Computing.[15:56] Scott remembers when he proved the limitation of quantum algorithms for a variation of Grover's search problem.[18:43] Scott realized that his competitive advantage was the ability to explain how things work.[20:01] Scott explains the collision problem.[21:33] Scott defines the birthday paradox.[23:24] Scott discusses the dividing line between serious and non-serious quantum computing research.[24:11]  What's Scott’s relative level of faith and optimism that the areas of topological quantum computing and measurement-based quantum computation are going to produce?[28:33] Scott talks about what he thinks will be the source of the first practical quantum speed-up. [31:55] Scott didn’t imagine that being a complexity theorist would become exponential.[36:14] Is Scott optimistic about quantum walks? [40:11] Has Scott returned to his machine learning and AI roots but is now trying to explain the concepts? [42:03] Scott was asked: ‘What is it going to take to get you to stop wasting your life on quantum computing?’[44:50] Scott talks about the future need to prevent  AI misuse. and his role in Open AI[47:41] Scott emphasizes the need for an external source that can point out your errors.[50:13] Scott shares his thoughts about the possible risks and misuses of GPT.[51:40] Scott made GPT to take a Quantum Computing exam; what did surprise him about the answers? It did much better on conceptual questions than on calculation questions[55:55] What kind of validation will we be able to give GPT?[56:22] Scott explains how RLHF (Reinforced Learning from Human Feedback) works.[59:28] Does Scott feel that there's room for optimism that educators can have a decent tool to hunt down this kind of plagiarism?[1:02:08] Is there anything that Scott is excited about seeing implemented on 1000 gate-based qubits with a decent amount of error mitigation? [1:04:05] Scott shares his interest in designing better quantum supremacy experiments.[1:07:43] Could these quantum supremacy experiments (based on random circuit sampling) already deliver a scalable advantage? [1:10:58] Kevin and Sebastian share the highlights of a fun and enlightening conversation with Scott Aaronson.Mentioned in this episode:Visit The New Quantum Era PodcastCheck Shtetl-OptimizeQuantum Computing Since Democritus, Scott AaronsonLearn more about the Adiabatic Algorithm result by Hastings and the Quantum Walk Algorithm result by Childs et Al.Tweetables and Quotes:“The dividing line between serious and nonserious quantum computing research is, are you asking the question of, ‘Can you actually be the best that a classical computer could do at the same desk? “ — Scott Aaronson“My first big result in quantum computing that got me into the field was to prove that Prasad Hoyer tap algorithm for the collision problem was optimal.”  — Scott Aaronson“ Quantum Walks are  a way of achieving Grover type speed ups at a wider range of problems than you would have expected.” — Scott Aaronson“AI safety is now a subject where you can get feedback.”  — Scott Aaronson“We don't have any theorems that would explain the recent successes of deep learning, the best way we can explain why is that none of the theorems rule it out.” — Scott Aaronson

Welcome to another episode of The New Quantum Era Podcast hosted by Kevin Rowney and Sebastian Hassinger.In this episode, we are joined by Dorit Aharonov, a professor at the Hebrew University of Jerusalem and one of the pioneers of quantum computing. She's also the Chief Science Officer at QEDMA, a quantum startup based in Israel. Dorit is one of the major movers and shakers of quantum error correction and co-author of the important Threshold Theorem for quantum error correction. Kevin, Sebastian, and Dorit talk about her recent work on the theoretical foundations of random circuit sampling.Key Takeaways:[4:22] Dorit shares her path into quantum information and computing.[8:27]  Dorit explains the threshold theorem in an easy-to-understand manner.[16:35] The velocity of error correction versus the generation of errors in the computation could depend on physical implementation, or the algorithm. Maybe even both.[18:53] A more powerful assertion Dorit makes is that there's a deeper connection between the phases of matter and the transition between solid and liquid and these quantum error correction thresholds.[19:51] A lot of the foundations of classical error correction were laid down in the mid-40s in Von Neumann's work when the IAS system was being built. Dorit still sees the echoes of that.[22:35] We might be witnessing a growing momentum around the powerful expression of new quantum error correction technologies.[25:28] Dorit talks about the difference between error mitigation and error correction.[26:55] Dorit explains the idea of the reset gate.[30:22] It might be safe to say that challenges are primarily engineering in nature and that we have enough science to enable that engineering to get to fault tolerance.[31:50] Dorit discusses a possible timeline for this engineering to get to fault tolerance.[34:07] Is Dorit an NISQ optimist or a pessimist when it comes to real-world applications?[39:21] Dorit addresses the difference between practical and asymptotic quantum advantage.[41:30] Dorit shares what the paper on random circuit sampling shows.[45:25] Dorit explains why the machine learning algorithms that were dequantized are  treacherous.[49:56] Dorit shows optimism regarding the possibility of seeing evidence of a quantum event.[52:25] Dorit admits to finding constructive interference between working in the industry and working on theoretical questions.[53:50] Is there something Dorit is excited about in the next year or two that will be another step forward?[56:50] Dorit talks about concrete examples of experiments and sensors that might be arriving thanks to quantum computing advancements.[1:00:35] Sebastian and Kevin share the highlights of a fantastic conversation with Dorit.Mentioned in this episode:Visit The New Quantum EraThe New Quantum Era PodcastLimitations of Noisy Reversible Computation Dorit Aharonov, Michael Ben-Or, Russell Impagliazzo, Norm NisanThe Complexity of NISQ, Sitan Chen, Jordan Cotler, Hsin-Yuan, and  Jerry LiA polynomial-time classical algorithm for noisy random circuit sampling Dorit Aharonov, Xun Gao, Zueph Landau, Yunchao Liu, Umesh Vazirani QEDMATweetables and Quotes:“Nobody actually believed that it was possible to correct errors that occur on quantum states because of the lack of reversibility. ” —  Dorit Aharonov“it's a physics phenomenon… below a certain threshold, we can think of this as if the system is capable of some completely different behavior, like ice and water. It's just like a phase transition -- below that, there would be macroscopic entanglement and … ability to control large scale quantum correlations. And above it, this would not be possible.”  — Dorit Aharonov