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 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.