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.

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!