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