In this episode, your host Sebastian Hassinger sits down with Andrew Houck to explore the latest advancements and collaborative strategies in quantum computing. Houck shares insights from his leadership roles at both Princeton and the Center for Co-Design of Quantum Advantage (C2QA), focusing on how interdisciplinary efforts are pushing the boundaries of coherence times, materials science, and scalable quantum architectures. The conversation covers the importance of co-design across the quantum stack, the challenges and surprises in improving qubit performance, and the vision for the next era of quantum research.

KEY TOPICS DISCUSSED

• Mission of C2QA:
  The central goal is to build the components necessary to move beyond the NISQ (Noisy Intermediate-Scale Quantum) era into fault-tolerant quantum computing. This requires integrating expertise in materials, devices, software, error correction, and architecture to ensure compatibility and progress at every level.
• Materials Breakthroughs:
  Houck discusses the surprising impact of using tantalum in superconducting qubits, which has significantly reduced surface losses compared to other metals. He explains the ongoing quest to identify and mitigate sources of decoherence, such as two-level systems (TLSs) and interface defects.
• Co-Design Philosophy:
  The episode delves into two types of co-design:
  • Vertical co-design: Aligning advances in materials, devices, error correction, and architecture to optimize the full quantum computing stack.
  • Cross-platform co-design: Bridging ideas and techniques across different qubit modalities and even across disciplines, such as applying methods from quantum sensing to quantum computing.
• Error Correction Innovations:
  Houck highlights breakthroughs like using GKP states for error correction, which have achieved performance beyond the break-even point, thanks to improvements in materials and device design.
• Bosonic Modes and Custom Architectures:
  The conversation touches on leveraging native bosonic modes in hardware to simulate field theories more efficiently, potentially saving vast computational resources. Houck discusses the trade-offs between general-purpose and custom quantum circuits in the current era of limited qubit counts.
• Modular Quantum Computing:
  As quantum systems scale, the focus is shifting to modular architectures. Houck outlines the challenges of connecting modules—such as chip-to-chip coupling and optimizing connectivity for error correction and algorithms.
• Institutional Collaboration:
  Houck contrasts the long-term, foundational investment at Princeton with the national, multi-institutional mission of C2QA. He emphasizes the unique strengths universities, industry, and national labs each bring to quantum research, and the importance of fostering collaboration across these sectors.
• Looking Ahead:
  The next phase for C2QA will incorporate advances in neutral atom quantum computing and diamond-based quantum sensing, while ramping down some networking efforts. Houck also reflects on the broader scientific and practical motivations driving quantum information science, and the fundamental questions that large-scale quantum systems may help answer.

NOTABLE QUOTES

“There’s a quasi-infinite number of ways that you can mess up coherence… If you’re really only using one number, you’ll never know.”

“Some of the best ideas we have are taking approaches from one field and bringing them to another. That’s what we call cross-platform co-design.”

“A million-qubit quantum computer is basically a cat… as you build these systems up, you can start to really ask: do we actually understand quantum mechanics as it turns into these macroscopically large objects?”

RESOURCES & MENTIONS

• Center for Co-Design of Quantum Advantage (C2QA)
• Princeton Quantum Initiative

For more episodes and updates, subscribe to The New Quantum Era.

In this episode of The New Quantum Era, Sebastian is joined by Dr. Emily Edwards, a co-founder of the Q12 initiative, an NSF-funded effort aimed at enhancing quantum science education from middle school through early undergraduate levels. Emily brings her expertise in organizing and motivating educators, as well as her passion for science communication. In this episode, we delve into the unique challenges of teaching quantum science and explore effective strategies to make this abstract field more accessible to learners of all ages.

Key Points

• Challenges in Quantum Communication and Education: Emily discusses the public perception of quantum science, often influenced by pop culture, and the importance of demystifying the subject to make it more approachable.
• Strategies for Formal and Informal Learning: The conversation highlights different techniques for teaching quantum science in formal settings, like schools, and informal settings, such as science museums or YouTube. Emily emphasizes the importance of foundational knowledge and incremental learning.
• Role of Technology in Quantum Education: Emily talks about using scanning electron microscopes and other technologies to make the invisible world of quantum science visible, thus igniting public interest and imagination similar to stargazing.
• Importance of Science Communication Workshops: Emily shares her experience in leading science communication workshops, aiming to improve the accuracy and effectiveness of science content created by the public.
• Public and Private Sector Collaboration: The discussion touches on the need for a blend of federal and private funding to sustain and scale quantum education initiatives. Emily stresses the importance of industry involvement to emphasize the urgency and importance of scientific literacy for the future workforce.

In this episode of The New Quantum Era, your host Sebastian Hassinger talks with Dr. Daniel Lidar. Dr. Lidar is a pioneering researcher in quantum computing with over 25 years of experience, currently a professor at the University of Southern California. His work spans quantum algorithms, error correction, and quantum advantage, with significant contributions to understanding quantum annealing and noise suppression techniques. Lidar has been instrumental in exploring practical quantum computing applications since the mid-1990s.

Key Topics Discussed:

• Dr. Lidar discussed how his experiments have demonstrated computational advantages on D-Wave and IBM quantum devices using innovative error suppression methods like dynamical decoupling
• We discuss Dr. Lidar's involvement in the exploration of the mechanics of quantum annealing, particularly with D-Wave devices, and its potential for solving optimization problems
• Daniel provides a detailed view of emerging approaches to error suppression, including logical dynamical decoupling (LDD) and its experimental validation
• Finally we touch on Quantum Elements, his new company focused on developing more accurate open-system quantum simulation software to improve quantum hardware performance

In this episode, Sebastian Hassinger welcomes back James Wootton, now Chief Science Officer at Moth Quantum, for a fascinating conversation about quantum computing's role in creative applications. This is a return visit from James, having appeared on episode 2, this time to talk about his exciting new role. Previously at IBM Quantum, James has been a pioneer in exploring unconventional applications of quantum computing, particularly in gaming, art, and creative industries.

Key Topics

Origins of James's Quantum Journey

• Started in Arosa, Switzerland (coincidentally where Schrödinger developed his wave equation)
• Initially skeptical about commercial applications of his quantum error correction research
• Created "Decodoku" (a play on "decoder" and "Sudoku"), a puzzle game to gamify quantum error correction in 2016
• The same year IBM put a 5 qubit machine on the cloud, creating a paradigm shift in accessibility

Quantum Gaming Innovations

• Developed what may be the first quantum computing game
• Created "Hello Quantum," a mobile educational game
• Developed "Quantum Blur," a tool that encodes images in quantum states, allowing users to see how quantum gates affect images
• Used quantum computing for procedural generation in games, including terrain generation for Minecraft-like environments

Quantum Art and Creativity

• Collaborated with a classical painter who has used Quantum Blur as his main artistic tool for five years
• Explored using quantum computing for music generation
• Investigated language generation using the DiscoCat framework

Moth Quantum

• James joined Moth Quantum as Chief Science Officer
• The company focuses on bringing quantum computing to creative industries
• Their approach recognizes that in creative fields, "usefulness" can mean bringing something unique rather than just superior performance
• Aims to build expertise with current quantum technologies to be ready when fault tolerance enables quantum advantage
• At the beginning of May, 2025, Moth collaborated with musical artist ILA on a project called "Infinite Remix," using quantum computing in the creation of an exciting new musical creation tool. 

Introduction:
In this milestone 50th episode of The New Quantum Era, your host Sebastian Hassinger welcomes Dr. Anna Grassellino, a leading figure in quantum information science and the director of the Superconducting Quantum Materials and Systems Center at Fermilab, or SQMS. Dr. Grassellino discusses the center’s mission to advance quantum computing and quantum sensing through innovations in superconducting materials and devices. The conversation explores the intersection of quantum hardware development, high energy physics applications, and the collaborative efforts driving progress in the field. We recorded our conversation at the APS 2025 Global Summit with assistance from the American Physical Society and from Quantum Machines, Inc. 

Main Topics Discussed:

• The vision and mission of the Superconducting Quantum Materials and Systems (SQMS) Center, including its role in the Department of Energy’s National Quantum Initiative and its focus on developing quantum systems with superior performance for scientific and technological applications.
• Advances in superconducting quantum hardware, particularly the use of high-quality superconducting radio frequency (SRF) cavities and their integration with two-dimensional superconducting circuits to enhance qubit coherence and scalability.
• Key technical challenges in scaling up quantum systems, such as mitigating decoherence, improving materials, and developing large-scale cryogenic platforms for quantum experiments.
• The importance of interdisciplinary collaboration between quantum engineers, materials scientists, and high energy physicists to achieve breakthroughs in quantum technology.
• Future directions for the SQMS Center, including the pursuit of quantum advantage in high energy physics algorithms, quantum sensing, and the development of robust error correction strategies.

Notable Papers from Fermi’s SQMS Center:

• Quantum computing hardware for HEP algorithms and sensing (arXiv:2204.08605) – Overview of SQMS’s approach to quantum hardware for high energy physics applications, including architectures and error correction.
• A large millikelvin platform at Fermilab for quantum computing applications (arXiv:2108.10816) – Description of the design and goals of a large-scale cryogenic platform for hosting advanced quantum devices at millikelvin temperatures.
• Searches for New Particles, Dark Matter, and Gravitational Waves 
• Additional recent preprints and publications from SQMS can be found on the SQMS Center’s publications page, including work on nonlinear quantum mechanics bounds, materials for quantum devices, and quantum error correction strategies.

Introduction

In this episode of The New Quantum Era podcast, host Sebastian Hassinger delves into an insightful conversation with Yonatan Cohen, CTO and co-founder of Quantum Machines. As a pioneer in quantum control systems, Quantum Machines is at the forefront of tackling the critical challenges of scaling quantum computing, and they also provided support for my interviews conducted at the American Physical Society’s Global Summit 2025. APS itself also graciously provided support for these episodes. 

Yonatan shares exciting updates from their latest demos at the APS conference, discusses their unique approach to quantum control, and explores how integrating classical and quantum computing is paving the way for more efficient and scalable solutions.

Key Points

• Scaling Quantum Control Systems: Yonatan discusses the challenges of scaling up quantum control systems, emphasizing the need to make systems more compact, reduce power consumption, and lower costs per qubit while maintaining high analog specifications.
• Integration of Classical Compute with Quantum Systems: The conversation highlights Quantum Machines’ collaborative work with NVIDIA on DGX Quantum, a platform that integrates classical and quantum computing to enhance computational power and low-latency data transfer.
• AI for Quantum Calibration and Error Correction: Yonatan explains the role of AI and machine learning in speeding up the calibration process of quantum computers and improving qubit control, potentially transforming how frequently and effectively quantum systems can be calibrated.
• Versatility Across Different Quantum Modalities: Quantum Machines’ control systems are adaptable to various quantum computing modalities such as superconducting qubits, NV centers, and atomic qubits, providing a flexible toolkit for researchers.
• The Role of the Israeli Quantum Computing Center: Yonatan describes Quantum Machines’ involvement in building and operating the Israeli Quantum Computing Center, providing researchers with hands-on access to cutting-edge quantum control technologies.

Welcome to episode 48 of The New Quantum Era podcast! Another episode recorded at the APS Global Summit in March, today's special guest is true quantum pioneer, John Martinis, co-founder and CTO of QoLab, a superconducting qubit company seeking to build a million qubit device. In this enlightening conversation, we explore the strategic shifts, collaborative efforts, and technological innovations that are pushing the boundaries of quantum computing closer to building scalable, million-qubit systems. This episode was made with support form The American Physical Society and Quantum Machines, Inc. (BTW I know I said episode 49 in the intro to this episode, I noticed it too late to fix without a further delay in posting the interview!)

Key Highlights

• Emerging from Stealth Mode & Million-Qubit System Paper:
  • Discussion on QoLab’s transition from stealth mode and their comprehensive paper on building scalable million-qubit systems.
  • Focus on a systematic approach covering the entire stack.
• Collaboration with Semiconductor Companies:
  • Unique business model emphasizing collaboration with semiconductor companies to leverage external expertise.
  • Comparison with bigger players like Google, who can fund the entire stack internally.
• Innovative Technological Approaches:
  • Integration of wafer-scale technology and advanced semiconductor manufacturing processes.
  • Emphasis on adjustable qubits and adjustable couplers for optimizing control and scalability.
• Scaling Challenges and Solutions:
  • Strategies for achieving scale, including using large dilution refrigerators and exploring optical communication for modular design.
  • Plans to address error correction and wiring challenges using brute force scaling and advanced materials.
• Future Vision and Speeding Up Development:
  • QoLab’s goal to significantly accelerate the timeline toward achieving a million-qubit system.
  • Insight into collaborations with HP Enterprises, NVIDIA, Quantum Machines, and others to combine expertise in hardware and software.
• Research Papers Mentioned in this Episode:
  • Position paper on building scalable million-qubit systems 

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 2024
• Quantum 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 Highlights

• Introduction 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 Concepts

• Quantum Memory: Storage for quantum information that maintains coherence
• QRAM (Quantum Random Access Memory): Architecture that allows quantum superpositions of addresses to access corresponding data
• Coherence Time: How long a qubit can maintain its quantum state
• Bucket Brigade: Method for routing quantum information through a tree structure
• Dual-Rail Qubits: Encoding quantum information in the presence of a photon in one of two resonators

References

• Weiss, D.K., Puri, S., Girvin, S.M. (2024). “Quantum random access memory architectures using superconducting cavities.” arXiv:2310.08288
• Xu, S., Hann, C.T., Foxman, B., Girvin, S.M., Ding, Y. (2023). “Systems Architecture for Quantum Random Access Memory.” arXiv:2306.03242
• Brock, 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 group
• Future 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 operations

This 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 states
• Comparison of QITE to Variational Quantum Eigensolver (VQE) approaches
• Challenges in variational methods, including barren plateaus and expressivity concerns
• Trade-offs between circuit depth, fidelity, and practical implementation on current hardware
• Potential for scientific value from NISQ-era devices in physics and chemistry applications
• The 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.
• Algorithmiq
• QCI’s forthcoming quantum computing device, Aqumen Seeker
• Tensor 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 Institute
• Quantum Circuits Inc. announces Aqumen Seeker

Join 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 Valley
IEEE 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 computer
• Towards near-term quantum simulation of materials
• Enhancing density functional theory using the variational quantum eigensolver
• Dissipative ground state preparation and the Dissipative Quantum Eigensolver

Other sites:

• Phasecraft
• Dr. 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 Context

• Deep learning has made significant progress, as evidenced by the rapid adoption of ChatGPT
• Neural networks have a bias towards simple functions, which enables them to generalize well on unseen data despite being highly expressive
• This “simplicity bias” may explain the success of deep learning, defying the traditional bias-variance tradeoff

Quantum Neural Networks (QNNs)

• QNNs are inspired by classical neural networks but have some key differences
• The encoding method used to input classical data into a QNN significantly impacts its inductive bias
• Basic encoding methods like basis encoding result in a QNN with no useful bias, essentially making it a random learner
• Amplitude encoding can introduce a simplicity bias in QNNs, but at the cost of reduced expressivity
  • Amplitude encoding cannot express certain basic functions like XOR/parity
• There appears to be a tradeoff between having a good inductive bias and having high expressivity in current QNN frameworks

Implications and Future Directions

• Current QNN frameworks are unlikely to serve as general purpose learning algorithms that outperform classical neural networks
• Future research could explore:
  • Discovering new encoding methods that achieve both good inductive bias and high expressivity
  • Identifying specific high-value use cases and tailoring QNNs to those problems
  • Developing entirely new QNN architectures and strategies
• Evaluating quantum advantage claims requires scrutiny, as current empirical results often rely on comparisons to weak classical baselines or very small-scale experiments

In 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 optics
• Transition to quantum machine learning and quantum computing applications

Quantum Machine Learning Challenges

• Limited to simulating small systems (6-10 qubits) due to lack of working quantum computers
• Barren plateau problem: the more quantum and entangled the system, the worse the problem
• Moved towards analog systems and away from universal quantum computers

Quantum Reservoir Computing

• Subclass of recurrent neural networks where connections between nodes are fixed
• Learning occurs through a filter function on the outputs
• Suitable for analog quantum systems like ensembles of atoms with interactions
• Advantages: redundancy in learning, quantum effects (interference, non-commuting bases, true randomness)
• Potential for fault tolerance and automatic error correction

Quantum Chemistry Application

• Goal: leverage classical chemistry knowledge and identify problems hard for classical computers
• Collaboration with quantum chemists Anna Krylov (USC) and Martin Head-Gordon (UC Berkeley)
• Focused on effective input-output between classical and quantum computers
• Simulating a biochemical catalyst molecule with high spin correlation using a combination of analog time evolution and logical gates
• Demonstrating higher fidelity simulation at low energy scales compared to classical methods

Future Directions

• Exploring fault-tolerant and robust approaches as an alternative to full error correction
• Optimizing pulses tailored for specific quantum chemistry calculations
• Investigating dynamics of chemical reactions
• Calculating potential energy surfaces for molecules
• Implementing multi-qubit analog ideas on the Rydberg atom array machine at Harvard
• Dr. 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.

Welcome back to The New Quantum Era, the podcast where we explore the cutting-edge developments in quantum computing. In today’s episode, hosts Sebastian Hassinger and Kevin Rowe are joined by Dr. Julien Camirand Lemyre, the CEO and co-founder of Nord Quantique. Nord Quantique is a startup spun out from the University of Sherbrooke in Quebec, Canada, and is making significant strides in the field of quantum error correction using innovative superconducting qubit designs. In this conversation, Dr. Camirand Lemyre shares insights into their groundbreaking research and the innovative approaches they are taking to improve quantum computing systems.

Listeners can expect to learn about:

• Dr. Camirand Lemyre’s journey into quantum computing and the founding of Nord Quantique.
• The unique approach Nord Quantique is taking with Bosonic code qubits and how they differ from traditional fermionic qubits.
• The recent research paper by Nord Quantique that demonstrates autonomous quantum error correction, a significant step forward in the field.
• The potential impact of these advancements on reducing the overhead of error correction in quantum systems.
• Future directions and next steps for Nord Quantique, including further optimization and development of their quantum technology.

Highlights:

• Julien Camirand Lemyre’s Background: Dr. Camirand Lemyre shares his academic journey and how it led to the founding of Nord Quantique.
• Bosonic Qubits: An exploration of how Nord Quantique is leveraging Bosonic qubits for better quantum error correction.
• Autonomous Quantum Error Correction: Discussion on the recent research paper and its implications for the field of quantum computing.
• Technological Innovations: Insights into the specific technological advancements and controls Nord Quantique is developing.
• Future Plans: Dr. Camirand Lemyre shares what’s next for Nord Quantique and their ongoing research efforts.

Mentioned in this episode:

• Nord Quantique: Website
• University of Sherbrooke: Website
• Institut Quantique: Website
• Q-Ctrl: Website

Tune in to hear about these exciting developments and what they mean for the future of quantum computing!

Welcome to another episode of The New Quantum Era! Today, we have a fascinating conversation with Professor Jens Eisert, a veteran in the field of quantum information science. Jens takes us through his journey from his PhD days, delving into the role of entanglement in quantum computing and communication, to leading a team that bridges theoretical and practical aspects of quantum technology. In this episode, we explore the fine line between classical and quantum worlds, the potential and limitations of near-term quantum devices, and the role of theoretical frameworks in advancing quantum technologies. Here are some key highlights from our conversation:

• Theoretical Limits and Practical Applications: Jens discusses his team's work on establishing theoretical limits and guidelines for what can be achieved with current quantum hardware, focusing on both long-term and near-term goals.
• Benchmarking and Certification: The importance of randomized benchmarking techniques is highlighted, including their role in diagnosing and improving quantum devices. Jens elaborates on how these techniques can provide detailed diagnostic information and their limitations in scalability.
• Error Mitigation and Non-Unit Noise: Insights into the impact of non-unit noise on quantum circuits and the limitations of error mitigation techniques, particularly concerning their scalability.
• Quantum Simulation and Near-Term Devices: Jens shares his cautious optimism about the potential for near-term quantum devices to achieve practical applications, particularly in the field of quantum simulation.
• Innovative and Foundational Research: The conversation touches on Jens' interest in both pioneering new fields and concluding existing ones. He shares intriguing research on the emergence of temperature in quantum systems and its potential implications for quantum algorithms.