The New Quantum Era Sebastian Hassinger & Kevin Rowney

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 research
Resources Mentioned: - IBM Quantum learning platform - "Introduction to Classical and Quantum Computing" by Tom Wong- Qiskit YouTube channel
In 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.
Takeaways
Yufei 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.Chapters
00:00 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