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

In this episode of The New Quantum Era podcast, your host Sebastian Hassinger interviews two of the field's most well-known figures, John Preskill and Rob Schoelkopf, about the transition of quantum computing into a new phase that John is calling "megaquop," which stands for "a million quantum operations." Our conversation delves into what this new phase entails, the challenges and opportunities it presents, and the innovative approaches being explored to make quantum computing perform better and become more useful. This episode was made with the kind support of the American Physical Society and Quantum Circuits, Inc. Here’s what you can expect from this insightful discussion:Introduction of the Megaquop Era: John explains the transition from the NISQ era to the megaquop era, emphasizing the need for quantum error correction and the goal of achieving computations with around a million operations.Quantum Error Correction: Both John and Rob discuss the importance of quantum error correction, the challenges involved, and the innovative approaches being taken, such as dual rail and cat qubits.Superconducting Qubits and Dual Rail Approach: Rob shares insights into Quantum Circuits' work on dual rail superconducting qubits, which aim to make error correction more efficient by detecting erasure errors.Scientific and Practical Implications: The conversation touches on the scientific value of current quantum devices and the potential applications and discoveries that could emerge from the megaquop era.Future Directions and Challenges: The discussion also covers the future of quantum computing, including the need for better connectivity and the challenges of scaling up quantum devices.Mentioned in this Episode:Beyond NISQ: The Megaquop Machine: John Preskill's paper adapting his keynote from Q2B Silicon Valley 2024Quantum Circuits, Inc.: Rob's company, which is working on dual rail superconducting qubits.

In this episode of The New Quantum Era podcast, host Sebastian Hassinger speaks with Steve Girvin, professor of physics at Yale University, about quantum memory - a critical but often overlooked component of quantum computing architecture. This episode was created with support from the American Physical Society and Quantum Circuits, Inc.Episode HighlightsIntroduction to Quantum Memory: Steve explains that quantum memory is essential for quantum computers, similar to how RAM functions in classical computers. It serves as intermediate storage while the CPU works on other data.Coherence Challenges: Quantum bits (qubits) struggle to faithfully hold information for extended periods. Quantum memory faces both bit flips (like classical computers) and phase flips (unique to quantum systems).The Fundamental Theorem: Steve notes there’s “no such thing as too much coherence” in quantum computing - longer coherence times are always beneficial.Quantum Random Access Memory (QRAM): Unlike classical RAM, QRAM can handle quantum superpositions, allowing it to process multiple addresses simultaneously and create entangled states of addresses and their associated data.QRAM Applications: Quantum memory enables state preparation, construction of oracles, and processing of big data in quantum algorithms for machine learning and linear algebra.Tree Architecture: QRAM is structured like an upside-down binary tree with routers at each node. The “bucket brigade” approach guides quantum bits through the tree to retrieve data.Error Resilience: Surprisingly, the error situation in QRAM is less catastrophic than initially feared. With a million leaf nodes and 0.1% error rate per component, only about 1,000 errors would occur, but the shallow circuit depth (only requiring n hops for n address bits) makes the system more resilient.Dual-Rail Approach: Recent work by Danny Weiss demonstrates using dual resonator (dual-rail) qubits where a microwave photon exists in superposition between two boxes, achieving 99.9% fidelity for each hop in the tree.Historical Context: Steve draws parallels to early classical computing memory systems developed by von Neumann at Princeton’s IAS, including mercury delay line memory and early fault tolerance concepts.Future Outlook: While building quantum memory presents significant challenges, Steve remains optimistic about progress, noting that improving base qubit quality first and then scaling is their preferred approach.Key ConceptsQuantum Memory: Storage for quantum information that maintains coherenceQRAM (Quantum Random Access Memory): Architecture that allows quantum superpositions of addresses to access corresponding dataCoherence Time: How long a qubit can maintain its quantum stateBucket Brigade: Method for routing quantum information through a tree structureDual-Rail Qubits: Encoding quantum information in the presence of a photon in one of two resonatorsReferencesWeiss, D.K., Puri, S., Girvin, S.M. (2024). “Quantum random access memory architectures using superconducting cavities.” arXiv:2310.08288Xu, S., Hann, C.T., Foxman, B., Girvin, S.M., Ding, Y. (2023). “Systems Architecture for Quantum Random Access Memory.” arXiv:2306.03242Brock, B., et al. (2024). “Quantum Error Correction of Qudits Beyond Break-even.” arXiv:2409.15065

In this episode of The New Quantum Era, host Sebastian Hassinger interviews Professor Will Oliver from MIT about the advancements in fluxonium qubits. The discussion delves into the unique features of fluxonium qubits compared to traditional transmon qubits, highlighting their potential for high fidelity operations and scalability. Oliver shares insights from recent experiments at MIT, where his team achieved nearly five nines fidelity in single-qubit gates, and discusses how these qubits could be scaled up for larger quantum computing architectures through innovative control systems.Major Points Covered:Fluxonium vs. Transmon Qubits: Fluxonium qubits have a double-well potential, unlike the harmonic oscillator-like potential of transmon qubits. This design allows for high anharmonicity, which is beneficial for reducing leakage to higher energy levels during operations.High Fidelity Operations: The MIT team achieved high fidelity in both single and two-qubit gates using fluxonium qubits. For single qubits, they reached nearly five nines fidelity, and for two-qubit gates, they achieved fidelities around 99.92%.Scalability and Cost Reduction: Fluxonium qubits operate at lower frequencies, which could enable the integration of control electronics at cryogenic temperatures, reducing costs and increasing scalability. This approach is being developed by Atlantic Quantum, a startup spun out of Oliver's research groupFuture Directions: The goal is to implement surface code error correction with fluxonium qubits, which could lead to efficient production of logical qubits due to their high fidelity operationsThis episode brought to you with support from APS and from Quantum Machines, a big thank you to both organizations!

Professor Zoe Holmes from EPFL in Lausanne, Switzerland, discusses her work on quantum imaginary time evolution and variational techniques for near-term quantum computers. With a background from Imperial College London and Oxford, Holmes explores the limits of what can be achieved with NISQ (Noisy Intermediate-Scale Quantum) devices.Key topics covered:Quantum Imaginary Time Evolution (QITE) as a cooling-inspired algorithm for finding ground statesComparison of QITE to Variational Quantum Eigensolver (VQE) approachesChallenges in variational methods, including barren plateaus and expressivity concernsTrade-offs between circuit depth, fidelity, and practical implementation on current hardwarePotential for scientific value from NISQ-era devices in physics and chemistry applicationsThe interplay between classical and quantum methods in advancing our understanding of quantum systems

Welcome to another episode of The New Quantum Era, where we delve into the cutting-edge developments in quantum computing. with your host, Sebastian Hassinger. Today, we have a unique episode featuring representatives from two companies collaborating on groundbreaking quantum algorithms and hardware. Joining us are Sean Weinberg, Director of Quantum Applications at Quantum Circuits Incorporated, and Guillermo Garcia Perez, Chief Science Officer and co-founder at Algorithmiq. Together, they discuss their partnership and the innovative work they are doing to advance quantum computing applications, particularly in the field of chemistry and pharmaceuticals.Key Highlights:Introduction of New Podcast Format: Sebastian explains the new format of the podcast and introduces the guests, Sean Weinberg from Quantum Circuits Inc. and Guillermo Garcia Perez from Algorithmic.Collaboration Overview: Guillermo discusses the partnership between Quantum Circuits Inc. and Algorithmiq, focusing on how Quantum Circuits Inc.'s dual-rail qubits with built-in error detection enhance Algorithmiq’s quantum algorithms.Innovative Algorithms: Guillermo elaborates on their novel approach to ground state simulations using tensor network methods and informationally complete measurements, which improve the accuracy and efficiency of quantum computations.Hardware Insights: Sean provides insights into Quantum Circuits Inc.'s Seeker device, an eight-qubit system that flags 90% of errors, and discusses the future scalability and potential for error correction.Future Directions: Both guests talk about the potential for larger-scale devices and the importance of collaboration between hardware and software companies to advance the field of quantum computing.Mentioned in this Episode:Quantum Circuits Inc.AlgorithmiqQCI’s forthcoming quantum computing device, Aqumen SeekerTensor Network Error Mitigation: A method used by Algorithmic to improve the accuracy of quantum computations.Tune in to hear about the exciting advancements in quantum computing and how these two companies are pushing the boundaries of what’s possible in this new quantum era, and if you like what you hear, check out www.newquantumera.com, where you'll find our full archive of episodes and a preview of the book I'm writing for O'Reilly Media, The New Quantum Era.

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!

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.

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?