The New Quantum Era - innovation in quantum computing, science and technology

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.