Your hosts, Sebastian Hassinger and Kevin Rowney, interview brilliant research scientists, software developers, engineers and others actively exploring the poss...
Generative Quantum Eigensolver with Alán Aspuru-Guzik
Welcome back to The New Quantum Era, a podcast by Sebastian Hassinger and Kevin Rowney. After a brief hiatus, we’re excited to bring you a fascinating conversation with a true pioneer in the field of quantum computing, Alán Aspuru-Guzik. Alán is a professor at the University of Toronto and a leading figure in quantum computing, known for his foundational work on the Variational Quantum Eigensolver (VQE). In this episode, we delve into the evolution of VQE and explore Alán’s latest groundbreaking work on the Generative Quantum Eigensolver (GQE). Expect to hear about the intersection of quantum computing and machine learning, and how these advancements could shape the future of the field.Key Highlights:Origins of VQE: Alan discusses the development of the Variational Quantum Eigensolver, a technique that combines classical and quantum computing to approximate the ground state of chemical systems. This method was a significant step forward in efforts to make practical use of noisy intermediate-scale quantum (NISQ) devices.Challenges and Innovations: The conversation touches on the challenges of variational algorithms, such as the barren plateau problem, and how Alán’s group has been working on innovative solutions to overcome these hurdles.Introduction to GQE: Alán introduces the Generative Quantum Eigensolver, a new approach that leverages generative models like transformers to optimize quantum circuits without relying on quantum gradients. This method aims to make quantum computing more efficient and practical.Future of Quantum Computing: The discussion explores the potential future workflows in quantum computing, where hybrid architectures combining classical and quantum computing will be essential. Alán shares his vision of how GQE could be foundational in this new era.Broader Applications: Beyond chemistry, the GQE technique has potential applications in quantum machine learning and other variational algorithms, making it a versatile tool in the quantum computing toolkit.Mentioned in this episode:A variational eigenvalue solver on a quantum processor: Foundational paper on VQE technique.The generative quantum Eigensolver (GQE) and its application for ground state search: Alan’s latest paper on GQE and its applications.Tequila Framework: An extensible software framework for VQE experiments.The Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation: A paper on learning across potential energy surfaces.Quantum autoencoders for efficient compression of quantum data: Early work on quantum autoencoders for molecular design.Beyond NISQ: The Megaquop Machine: John Preskill’s slides from Q2B SV 2024. I think John is great, but "megaquop" is very "fetch."Myths around quantum computation before full fault tolerance: what no-go theorems rule out and what they don't: A paper discussing myths and truths about quantum computing.Stay tuned for more exciting episodes and deep dives into the world of quantum computing. If you enjoyed this episode, please subscribe, review, and share it on your preferred social media platforms. Thank you for listening!
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Dual-rail superconducting qubits with Rob Schoelkopf
Welcome to another episode of The New Quantum Era, hosted by Sebastian Hassinger and Kevin Rowney. Today, we have the privilege of speaking with Dr. Robert Schoelkopf, Sterling Professor of Applied Physics at Yale, Director of the Yale Quantum Institute, and CTO and co-founder at Quantum Circuits, Inc. Dr. Schoelkopf is a pioneering figure in the field of quantum computing, particularly known for his contributions to the development of the transmon qubit architecture. In this episode, we delve into the history and future of quantum computing, focusing on the latest advancements in error correction and the innovative dual rail qubit architecture.Key Highlights:Historical Context and Contributions: Dr. Schoelkopf discusses the early days of quantum computing at Yale, including the development of the transmon qubit architecture, which has been foundational for superconducting qubits.Introduction to Dual Rail Qubits: Explanation of the dual rail qubit architecture, which promises significant improvements in error detection and correction, potentially reducing the overhead required for fault-tolerant quantum computing.Error Correction Strategies: Insights into how the dual rail qubit architecture simplifies the detection and correction of errors, making quantum error correction more efficient and scalable.Modular Approach to Quantum Computing: Discussion on the modular design of quantum systems, which allows for easier scaling and maintenance, and the potential for interconnecting quantum modules via microwave photons.Future Prospects and Real-World Applications: Dr. Schoelkopf shares his vision for the future of quantum computing, including the commercial deployment of Quantum Circuits, Inc's new quantum devices and the ongoing collaboration between theoretical and experimental approaches to advance the field.Mentioned in this Episode:Yale Quantum InstituteQuantum Circuits Inc. announces Aqumen SeekerJoin us as we explore these groundbreaking advancements and their implications for the future of quantum computing.
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Integrating Quantum Computers and Classical Supercomputers with Martin Schultz
In this episode of The New Quantum Era, Sebastian talks with Martin Schultz, Professor at TU Munich and board member of the Leibniz Supercomputing Center (LRZ) about the critical need to integrate quantum computers with classical supercomputing resources to build practical quantum solutions. They discuss the Munich Quantum Valley initiative, focusing on the challenges and advancements in merging quantum and classical computing.Main Topics Discussed:The Genesis of Munich Quantum Valley: The Munich Quantum Valley is a collaborative project funded by the Bavarian government to advance quantum research and development. The project quickly realized the need for software infrastructure to bridge the gap between quantum hardware and real-world applications.Building a Hybrid Quantum-Classical Computing Infrastructure: LRZ is developing a software stack and web portal to streamline the interaction between their HPC system and various quantum computers, including superconducting and ion trap systems. This approach enables researchers to leverage the strengths of both classical and quantum computing resources seamlessly.Hierarchical Scheduling for Efficient Resource Allocation: LRZ is designing a multi-tiered scheduling system to optimize resource allocation in the hybrid environment. This system considers factors like job requirements, resource availability, and the specific characteristics of different quantum computing technologies to ensure efficient execution of quantum workloads.Open-Source Collaboration and Standardization: LRZ aims to make its software stack open-source, recognizing the importance of collaboration and standardization in the quantum computing community. They are actively working with vendors to define standard interfaces for integrating quantum computers with HPC systems.Addressing the Unknown in Quantum Computing: The field of quantum computing is evolving rapidly, and LRZ acknowledges the need for adaptable solutions. Their architectural design prioritizes flexibility, allowing for future pivots and the incorporation of new quantum computing models and intermediate representations as they emerge.Munich Quantum ValleyIEEE Quantum
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Innovative Near-Term Quantum Algorithms with Toby Cubitt
Welcome to The New Quantum Era, a podcast hosted by Sebastian Hassinger and Kevin Rowney. In this episode, we have an insightful conversation with Dr. Toby Cubitt, a pioneer in quantum computing, a professor at UCL, and a co-founder of Phasecraft. Dr. Cubitt shares his deep understanding of the current state of quantum computing, the challenges it faces, and the promising future it holds. He also discusses the unique approach Phasecraft is taking to bridge the gap between theoretical algorithms and practical, commercially viable applications on near-term quantum hardware.Key Highlights:The Dual Focus of Phasecraft: Dr. Cubitt explains how Phasecraft is dedicated to algorithms and applications, avoiding traditional consultancy to drive technology forward through deep partnerships and collaborative development.Realistic Perspective on Quantum Computing: Despite the hype cycles, Dr. Cubitt maintains a consistent, cautiously optimistic outlook on the progress toward quantum advantage, emphasizing the complexity and long-term nature of the field.Commercial Viability and Algorithm Development: The discussion covers Phasecraft’s strategic focus on material science and chemistry simulations as early applications of quantum computing, leveraging the unique strengths of quantum algorithms to tackle real-world problems.Innovative Algorithmic Approaches: Dr. Cubitt details Phasecraft’s advancements in quantum algorithms, including new methods for time dynamics simulation and hybrid quantum-classical algorithms like Quantum enhanced DFT, which combine classical and quantum computing strengths.Future Milestones: The conversation touches on the anticipated breakthroughs in the next few years, aiming for quantum advantage and the significant implications for both scientific research and commercial applications.Papers Mentioned in this episode:Observing ground-state properties of the Fermi-Hubbard model using a scalable algorithm on a quantum computerTowards near-term quantum simulation of materialsEnhancing density functional theory using the variational quantum eigensolverDissipative ground state preparation and the Dissipative Quantum EigensolverOther sites:PhasecraftDr. Toby Cubitt’s personal site
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Quantum Machine Learning with Jessica Pointing
In this episode of The New Quantum Era podcast, hosts Sebastian Hassinger and Kevin Roney interview Jessica Pointing, a PhD student at Oxford studying quantum machine learning.Classical Machine Learning ContextDeep learning has made significant progress, as evidenced by the rapid adoption of ChatGPTNeural networks have a bias towards simple functions, which enables them to generalize well on unseen data despite being highly expressiveThis “simplicity bias” may explain the success of deep learning, defying the traditional bias-variance tradeoffQuantum Neural Networks (QNNs)QNNs are inspired by classical neural networks but have some key differencesThe encoding method used to input classical data into a QNN significantly impacts its inductive biasBasic encoding methods like basis encoding result in a QNN with no useful bias, essentially making it a random learnerAmplitude encoding can introduce a simplicity bias in QNNs, but at the cost of reduced expressivityAmplitude encoding cannot express certain basic functions like XOR/parityThere appears to be a tradeoff between having a good inductive bias and having high expressivity in current QNN frameworksImplications and Future DirectionsCurrent QNN frameworks are unlikely to serve as general purpose learning algorithms that outperform classical neural networksFuture research could explore:Discovering new encoding methods that achieve both good inductive bias and high expressivityIdentifying specific high-value use cases and tailoring QNNs to those problemsDeveloping entirely new QNN architectures and strategiesEvaluating quantum advantage claims requires scrutiny, as current empirical results often rely on comparisons to weak classical baselines or very small-scale experimentsIn summary, this insightful interview with Jessica Pointing highlights the current challenges and open questions in quantum machine learning, providing a framework for critically evaluating progress in the field. While the path to quantum advantage in machine learning remains uncertain, ongoing research continues to expand our understanding of the possibilities and limitations of QNNs.Paper cited in the episode:Do Quantum Neural Networks have Simplicity Bias?
Your hosts, Sebastian Hassinger and Kevin Rowney, interview brilliant research scientists, software developers, engineers and others actively exploring the possibilities of our new quantum era. We will cover topics in quantum computing, networking and sensing, focusing on hardware, algorithms and general theory. The show aims for accessibility - neither of us are physicists! - and we'll try to provide context for the terminology and glimpses at the fascinating history of this new field as it evolves in real time.
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