The New Quantum Era

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

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

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?

Sebastian is joined by Susanne Yelin, Professor of Physics in Residence at Harvard University and the University of Connecticut.Susanne's Background:Fellow at the American Physical Society and Optica (formerly the American Optics Society)Background in theoretical AMO (Atomic, Molecular, and Optical) physics and quantum opticsTransition to quantum machine learning and quantum computing applicationsQuantum Machine Learning ChallengesLimited to simulating small systems (6-10 qubits) due to lack of working quantum computersBarren plateau problem: the more quantum and entangled the system, the worse the problemMoved towards analog systems and away from universal quantum computersQuantum Reservoir ComputingSubclass of recurrent neural networks where connections between nodes are fixedLearning occurs through a filter function on the outputsSuitable for analog quantum systems like ensembles of atoms with interactionsAdvantages: redundancy in learning, quantum effects (interference, non-commuting bases, true randomness)Potential for fault tolerance and automatic error correctionQuantum Chemistry ApplicationGoal: leverage classical chemistry knowledge and identify problems hard for classical computersCollaboration with quantum chemists Anna Krylov (USC) and Martin Head-Gordon (UC Berkeley)Focused on effective input-output between classical and quantum computersSimulating a biochemical catalyst molecule with high spin correlation using a combination of analog time evolution and logical gatesDemonstrating higher fidelity simulation at low energy scales compared to classical methodsFuture DirectionsExploring fault-tolerant and robust approaches as an alternative to full error correctionOptimizing pulses tailored for specific quantum chemistry calculationsInvestigating dynamics of chemical reactionsCalculating potential energy surfaces for moleculesImplementing multi-qubit analog ideas on the Rydberg atom array machine at HarvardDr. Yelin's work combines the strengths of analog quantum systems and avoids some limitations of purely digital approaches, aiming to advance quantum chemistry simulations beyond current classical capabilities.