Episodi
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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.
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.
Key Highlights:
Observing ground-state properties of the Fermi-Hubbard model using a scalable algorithm on a quantum computerTowards near-term quantum simulation of materialsEnhancing density functional theory using the variational quantum eigensolverDissipative ground state preparation and the Dissipative Quantum Eigensolver
Papers Mentioned in this episode:Other sites:
PhasecraftDr. Toby Cubitt’s personal site -
Episodi mancanti?
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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 ChatGPTNeural networks have a bias towards simple functions, which enables them to generalize well on unseen data despite being highly expressiveThis “simplicity bias” may explain the success of deep learning, defying the traditional bias-variance tradeoffQuantum Neural Networks (QNNs)
QNNs are inspired by classical neural networks but have some key differencesThe encoding method used to input classical data into a QNN significantly impacts its inductive biasBasic encoding methods like basis encoding result in a QNN with no useful bias, essentially making it a random learnerAmplitude encoding can introduce a simplicity bias in QNNs, but at the cost of reduced expressivityAmplitude encoding cannot express certain basic functions like XOR/parityThere appears to be a tradeoff between having a good inductive bias and having high expressivity in current QNN frameworksImplications and Future Directions
Current QNN frameworks are unlikely to serve as general purpose learning algorithms that outperform classical neural networksFuture research could explore:Discovering new encoding methods that achieve both good inductive bias and high expressivityIdentifying specific high-value use cases and tailoring QNNs to those problemsDeveloping entirely new QNN architectures and strategiesEvaluating quantum advantage claims requires scrutiny, as current empirical results often rely on comparisons to weak classical baselines or very small-scale experimentsIn summary, this insightful interview with Jessica Pointing highlights the current challenges and open questions in quantum machine learning, providing a framework for critically evaluating progress in the field. While the path to quantum advantage in machine learning remains uncertain, ongoing research continues to expand our understanding of the possibilities and limitations of QNNs.
Paper cited in the episode:
Do Quantum Neural Networks have Simplicity Bias? -
Sebastian is joined by Susanne Yelin, Professor of Physics in Residence at Harvard University and the University of Connecticut.
Fellow at the American Physical Society and Optica (formerly the American Optics Society)Background in theoretical AMO (Atomic, Molecular, and Optical) physics and quantum opticsTransition to quantum machine learning and quantum computing applications
Susanne's Background:Quantum Machine Learning Challenges
Limited to simulating small systems (6-10 qubits) due to lack of working quantum computersBarren plateau problem: the more quantum and entangled the system, the worse the problemMoved towards analog systems and away from universal quantum computersQuantum Reservoir Computing
Subclass of recurrent neural networks where connections between nodes are fixedLearning occurs through a filter function on the outputsSuitable for analog quantum systems like ensembles of atoms with interactionsAdvantages: redundancy in learning, quantum effects (interference, non-commuting bases, true randomness)Potential for fault tolerance and automatic error correctionQuantum Chemistry Application
Goal: leverage classical chemistry knowledge and identify problems hard for classical computersCollaboration with quantum chemists Anna Krylov (USC) and Martin Head-Gordon (UC Berkeley)Focused on effective input-output between classical and quantum computersSimulating a biochemical catalyst molecule with high spin correlation using a combination of analog time evolution and logical gatesDemonstrating higher fidelity simulation at low energy scales compared to classical methodsFuture Directions
Exploring fault-tolerant and robust approaches as an alternative to full error correctionOptimizing pulses tailored for specific quantum chemistry calculationsInvestigating dynamics of chemical reactionsCalculating potential energy surfaces for moleculesImplementing multi-qubit analog ideas on the Rydberg atom array machine at HarvardDr. 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.
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.
Listeners can expect to learn about:
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.
Highlights:
Nord Quantique: WebsiteUniversity of Sherbrooke: WebsiteInstitut Quantique: WebsiteQ-Ctrl: Website
Mentioned in this episode:
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. -
Welcome to The New Quantum Era podcast! In today’s episode, we dive deep into the fascinating world of quantum computing and the broader tech landscape with Anastasia Marchenkova, who has a unique blend of experiences in startups, academia, and venture capital. Join us as we explore the intersections of technology, business, and education, and uncover the challenges and opportunities that lie ahead in the quantum era.
Highlights from the Interview:
Journey into Quantum Computing: Learn how our Anastasia's early experiences in quantum telecommunications and a serendipitous encounter with a startup led to a pivotal role at Rigetti Computing.Building and Scaling Startups: Insights into the startup ecosystem, including the importance of customer discovery, the challenges of scaling deep tech companies, and the role of non-dilutive funding from sources like DARPA.Interdisciplinary Innovations: Discover how principles from quantum computing are being applied to other cutting-edge fields like thermodynamic computing and AI, and the potential for cross-disciplinary breakthroughs.The Importance of Communication and Networking: Discussion on the critical role of communication skills in science and technology, and how building connections can drive innovation and collaboration.Future Vision and Education: Our guest’s ambitious plans for bridging the gap between deep tech and the broader public through educational initiatives and media, aiming to inspire the next generation of technologists and entrepreneurs.Mentioned in This Episode:
Rigetti Computing: A pioneering quantum computing startup.DARPA (Defense Advanced Research Projects Agency): A key source of non-dilutive funding for deep tech projects.Quantum Benchmark: A company specializing in error characterization and mitigation for quantum computing, acquired by Keysight Technologies.Thermodynamic Computing: An emerging field aimed at reducing energy consumption in AI, with notable contributions from researchers like Patrick Coles, who founded Normal Computing, and Guillaume Verdun, who recently founded Extropic.VC Lab: An incubator program for training emerging venture capitalists. -
In this episode of The New Quantum Era, Kevin and Sebastian are joined by a special guest, Paul Cadden-Zemansky, Associate Professor of Physics at Bard College and Director of the Physics Program. Paul is also on the Executive Committee for the International Year of Quantum at the American Physical Society and has been actively involved in the UN’s recent declaration of 2025 as the International Year of Quantum Science and Technology. With the UN resolution now official, Paul joins us to discuss the significance and plans for this global celebration of quantum mechanics.
The Significance of the International Year of Quantum Science and Technology: Paul explains the origins and importance of the UN’s declaration, marking the 100th anniversary of quantum mechanics and its impact over the past century.Global Collaboration and Outreach: Discussion on the international cooperation involved in getting the resolution passed, including the involvement of various scientific societies and countries, and the emphasis on public awareness and education.Challenges and Strategies for Quantum Communication: Paul shares his thoughts on the difficulties of communicating complex quantum concepts to the public and the strategies to make quantum mechanics more accessible and engaging.Future Plans and Initiatives: Insights into the plans for 2025, including potential events, educational resources, and how individuals and organizations can get involved in promoting quantum science.Innovations in Quantum Visualization: Paul’s work with students on new methods for visualizing complex quantum systems, including the development of tools to help understand two-qubit states.
Listeners can expect an insightful conversation covering the following key points:
UN Declaration of 2025 as the International Year of Quantum Science and TechnologyAmerican Physical Society (APS)Quantum 2025 Website: quantum2025.orgPaul’s Research Paper on Quantum Visualization on ArxivPaul's web-based visualization tool
Mentioned in this episode:
Join us as we delve into the exciting world of quantum mechanics and explore the plans for celebrating its centennial year! -
In this episode of The New Quantum Era, host Sebastian Hassinger comes to you again from Rensselaer Polytechnic Institute, during their launch event in April 2024 for the deployment of an IBM System One quantum computer on their campus. RPI invited me to lead a panel discussion with members of their faculty and IT team, and provided a podcast studio for my use for the remainder of the week, where he recorded a series of interviews. In this episode Sebastian interviews Di Fang, an assistant professor of mathematics at Duke University and member of the Duke Quantum Center. They discuss Dr. Fang's research on the theoretical aspects of quantum computing and quantum simulation, the potential for quantum computers to demonstrate quantum advantage over classical computers, and the need to balance theory with practical applications. Key topics and takeaways from the conversation include:
- Dr. Fang's background as a mathematician and how taking a quantum computing class taught by Umesh Vazirani at UC Berkeley sparked her interest in the field of quantum information science
- The potential for quantum computers to directly simulate quantum systems like molecules, going beyond the approximations required by classical computation
- The importance of both proving theoretical bounds on quantum algorithms and working towards practical resource estimation and hardware implementation to demonstrate real quantum advantage
- The stages of development needed to go from purely theoretical quantum advantage to solving useful real-world problems, and the role of Google's quantum XPRIZE competition in motivating practical applications
- The long-term potential for quantum computing to have a disruptive impact like AI, but the risk of a "quantum winter" if practical results don't materialize, and the need for continued fundamental research by academics alongside industry efforts -
In this episode of The New Quantum Era, we're diving deep into the intersection of quantum computing and chemistry with Jamie Garcia, Technical Program Director for Algorithms and Scientific Partnerships Group with IBM Quantum. Jamie brings a unique perspective, having transitioned from a background in chemistry to the forefront of quantum computing. At the heart of our discussion is the deployment of the IBM Quantum computer at RPI, marking a significant milestone as the first of its kind on a university campus. Jamie shares insights into the challenges and breakthroughs in using quantum computing to push the boundaries of computational chemistry, highlighting the potential to revolutionize how we approach complex chemical reactions and materials science.
Throughout the interview, Jamie discusses the evolution of quantum computing from a theoretical novelty to a practical tool in scientific research, particularly in chemistry. We explore the limitations of classical computational methods in chemistry, such as the reliance on approximations, and how quantum computing offers the promise of more accurate and efficient simulations. Jamie also delves into the concept of "utility" in quantum computing, illustrating how IBM's quantum computers are beginning to perform tasks that challenge classical computing capabilities. The conversation further touches on the significance of quantum computing in education and research, the integration of quantum systems with high-performance computing (HPC) centers, and the future of quantum computing in addressing complex problems in chemistry and beyond.
Jamie's homepage at IBM Research
How Quantum Computing Could Remake Chemistry, an article by Jamie Garcia in Scientific American -
Sebastian interviews Professor Lin Lin during the System One ribbon cutting event at Rensselaer Polytechnic Institute in Troy, NY. Professor Lin Lin's journey from computational mathematics to quantum chemistry has been driven by his fascination with modeling nature through computation. As a student at Peking University, he was intrigued by the concept of first principles modeling, which aims to simulate chemical systems using minimal information such as atomic species and positions. Lin Lin pursued this interest during his PhD at Princeton University, working with mathematicians and chemists to develop better algorithms for density functional theory (DFT). DFT reformulates the high-dimensional quantum chemistry problem into a more tractable three-dimensional one, albeit with approximations. While DFT works well for about 95% of cases, it struggles with large systems and the remaining "strongly correlated" 5%. Lin Lin and his collaborators radically reformulated DFT to enable calculations on much larger systems, leading to his faculty position at UC Berkeley in 2014.
In 2018, a watershed year marked by his tenure, Lin Lin decided to tackle the challenging 5% of strongly correlated quantum chemistry problems. Two emerging approaches showed promise: artificial intelligence (AI) and quantum computing. Both AI and quantum computing are well-suited for handling high-dimensional problems, albeit in fundamentally different ways. Lin Lin aimed to leverage both approaches, collaborating on the development of deep molecular dynamics using AI to efficiently parameterize interatomic potentials. On the quantum computing side, his group worked to reformulate quantum chemistry for quantum computers. Despite the challenges posed by the COVID-19 pandemic, Lin Lin and his collaborators have made significant strides in combining AI and quantum computing to push the boundaries of computational chemistry simulations, bridging the fields of mathematics, chemistry, AI, and quantum computing in an exciting new frontier.
Thanks again to Professor Lin and everyone at RPI for hosting me and providing such an amazing opportunity to interview so many brilliant researchers.
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Sebastian is joined by Olivia Lanes, Global Lead for Education and Learning, IBM Quantum to discuss quantum education, IBM's efforts to provide resources for workforce development, the importance of diversity and equality in STEM, and her own personal journey from experimental physics to community building and content creation. Recorded on the RPI campus during the launch event of their IBM System One quantum computer.
Key Topics:
- Olivia's background in experimental quantum physics and transition to education at IBM Quantum
- Lowering barriers to entry in quantum computing education through IBM's Quantum Experience platform, Qiskit open source framework, and online learning resources
- The importance of reaching students early, especially women and people of color, to build a diverse quantum workforce pipeline
- Quantum computing as an interdisciplinary field requiring expertise across physics, computer science, engineering, and other domains
- The need to identify real-world problems and use cases that quantum computing can uniquely address
- Balancing the hype around quantum computing's potential with setting realistic expectations
- International collaboration and providing global access to quantum education and technologies
- The unique opportunity of having an IBM quantum computer on the RPI campus to inspire students and enable cutting-edge researchResources Mentioned:
- IBM Quantum learning platform
- "Introduction to Classical and Quantum Computing" by Tom Wong
- Qiskit YouTube channelIn summary, this episode explores the current state of quantum computing education, the importance of making it accessible to a broad and diverse group of students from an early age, and how academia and industry can partner to build the quantum workforce of the future. Olivia provides an insider's perspective on IBM Quantum's efforts in this space.
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For this episode, Sebastian is on his own, as Kevin is taking a break. Sebastian accepted a gracious invite to the ribbon cutting event at Rensselaer Polytechnic Institute in Troy, NY, where the university was launching their on-campus IBM System One -- the first commercial quantum computer on a university campus!
James Hendler, Professor and Director of Future of Computing InstituteJackie Stampalia, Director, Client Information Services, DotCIOOsama Raisuddin, Research Scientist, RPILucy Zhang, Professor, Mechanical, Aerospace, and Nuclear Engineering
This week, the episode is a recording a live event hosted by Sebastian. The panel of RPI faculty and staff talk about their decision to deploy a quantum computer in their own computing center -- a former chapel from the 1930s! - what they hope the RPI community will do with the device, and the role of academic partnership with private industry at this stage of the development of the technology.
Joining Sebastian on the panel were: -
Dr. Martin Savage is a professor of nuclear theory and quantum informatics at the University of Washington. His research explores using quantum computing to investigate high energy physics and quantum chromodynamics.Dr. Savage transitioned from experimental nuclear physics to theoretical particle physics in his early career. Around 2017-2018, limitations in classical computing for certain nuclear physics problems led him to explore quantum computing.In December 2022, Dr. Savage's team used 112 qubits on IBM's Heron quantum processor to simulate hadron dynamics in the Schwinger Model. This groundbreaking calculation required 14,000 CNOT gates at a depth of 370. Error mitigation techniques, translational invariance in the system, and running the simulation over the December holidays when the quantum hardware was available enabled this large-scale calculation.While replacing particle accelerator experiments is not the goal, quantum computers could eventually complement experiments by simulating environments not possible in a lab, like the interior of a neutron star. Quantum information science is increasingly important in the pedagogy of particle physics. Advances in quantum computing hardware and error mitigation are steadily enabling more complex simulations.The incubator for quantum simulation at University of Washington brings together researchers across disciplines to collaborate on using quantum computers to advance nuclear and particle physics.
Links:
Dr. Savage's home page
The InQubator for Quantum Simulation
Quantum Simulations of Hadron Dynamics in the Schwinger Model using 112 Qubits
IBM's blog post which contains some details regarding the Heron process and the 100x100 challenge. -
In this episode, Sebastian and Kevin interview Professor Yufei Ding, an associate professor at UC San Diego, who specializes in the intersection of theoretical physics and computer science. They discuss Dr. Ding's research on system architecture in quantum computing and the potential impact of AI on the field. Dr. Ding's work aims to replicate the critical stages of classical computing development in the context of quantum computing. The conversation explores the challenges and opportunities in combining computer science, theoretical and experimental quantum computing, and the potential applications of quantum computing in machine learning.
Takeaways
Yufei Ding's research focuses on system architecture in quantum computing, aiming to replicate the critical stages of classical computing development in the context of quantum computing.The combination of computer science, theoretical and experimental quantum computing is a unique approach that offers new insights and possibilities.AI and machine learning have the potential to greatly impact quantum computing, and finding a generically applicable quantum advantage in machine learning could have a transformative effect.The development of a simulation framework for exploring different system architectures in quantum computing is crucial for advancing the field and identifying viable outcomes.Chapters
00:00 Introduction and Background
02:12 Yufei Ding's System Architecture
03:08 AI and Quantum Computing
04:19 Conclusion -
In this special solo episode recorded at Q2B Paris 2024, Sebastian talks with Houlong Zhuang, assistant professor at Arizona State University, about his work in material science.
Dr. Zhuang discusses his research on using quantum computing and machine learning to simulate high entropy alloy materials. The goal is to efficiently predict material properties and discover new material compositions.Density functional theory (DFT) is a commonly used classical computational method for materials simulations. However, it struggles with strongly correlated electronic states. Quantum computers have the potential to efficiently simulate these challenging quantum interactions.The research uses classical machine learning models trained on experimental data to narrow down the vast combinatorial space of possible high entropy alloy compositions to a smaller set of promising candidates. This is an important screening step.Quantum machine learning and quantum simulation are then proposed to further refine the predictions and simulate the quantum interactions in the materials more accurately than classical DFT. This may enable prediction of properties like stability and elastic constants.Key challenges include the high dimensionality of the material composition space and the noise/errors in current quantum hardware. Hybrid quantum-classical algorithms leveraging the strengths of both are a promising near-term approach.Ultimately, the vision is to enable inverse design - using the models to discover tailored material compositions with desired properties, potentially reducing experimental trial-and-error. This requires highly accurate, explainable models.In the near-term, quantum advantage may be realized for specific local properties or excited states leveraging locality of interactions. Fully fault-tolerant quantum computers are likely needed for complete replacement of classical DFT.Continued development of techniques like compact mappings, efficient quantum circuit compilations, active learning, and quantum embeddings of local strongly correlated regions will be key to advancing practical quantum simulation of realistic materials.In summary, strategically combining machine learning, quantum computing, and domain knowledge of materials is a promising path to accelerating materials discovery, but significant research challenges remain to be overcome through improved algorithms and hardware. A hybrid paradigm will likely be optimal in the coming years.
Some of Dr. Zhuang's papers include:
Quantum machine-learning phase prediction of high-entropy alloys
Sudoku-inspired high-Shannon-entropy alloys
Machine-learning phase prediction of high-entropy alloys -
No guest this episode! Instead, Kevin and Sebastian have a conversation looking back on the events of 2023 in quantum computing, wiht a particular focus on three trends: some waning of enthusiasm in the private sector, a surge of investments from the public sector as national and regional governments invest in the quantum computing value chain and the shift from a focus on NISQ to logical qubits.
Qureca's overview of public sector quantum initiatives in 2023
Preskill's NISQ paper from 2018 (yes, I was off by a few years!)
The paper that introduced the idea of VQE: A variational eigenvalue solver on a quantum processor by Peruzzo et al
A variation on VQE that still has some promise An adaptive variational algorithm for exact molecular simulations on a quantum computer by Grimsley et al
Mitiq, a quantum error mitigation framework from Unitary Fund
Peter Shor's first of its kind quantum error correction in the paper Scheme for reducing decoherence in quantum computer memory
Quantinuum demonstrates color codes to implement a logical qubit on their ion trap machine, H-1
Toric codes introduced in Fault-tolerant quantum computation by anyons by Alexei Kitaev
Surface codes and topological qubits introduced in Topological quantum memory by Eric Dennis, Alexei Kitaev, Andrew Landahl, and John Preskill
The threshold theorem is laid out in Fault-Tolerant Quantum Computation With Constant Error Rate by Dorit Aharonov and Michael Ben-Or
The GKP variation on the surface code appears in Encoding a qubit in an oscillator by Daniel Gottesman, Alexei Kitaev, John Preskill
A new LDPC based chip architecture is described in High-threshold and low-overhead fault-tolerant quantum memory by Sergey Bravyi, Andrew W. Cross, Jay M. Gambetta, Dmitri Maslov, Patrick Rall, Theodore J. Yoder
Neutral atoms are used to create 48 logical qubits in Logical quantum processor based on reconfigurable atom arrays by Vuletic's and Lukin's groups at MIT and Harvard respectivelyIf you have an idea for a guest or topic, please email us.
Also, John Preskill has agreed to return to answer questions from our audience so please send any question you'd like Professor Preskill to answer our way at [email protected] -
Kevin and Sebastian are joined by Dr. Vladan Vuletic, the Lester Wolfe Professor of Physics at the Center for Ultracold Atoms and Research in the Department of Physics at the Massachusetts Institute of Technology
At the end of 2023, the quantum computing community was startled and amazed by the results from a bombshell paper published in Nature on December 6th, titled Logical quantum processor based on reconfigurable atom arrays in which Dr. Vuletic's group collaborated with Dr Mikhail Lukin's group at Harvard to create 48 logical qubits from an array of 280 atoms. Scott Aaronson does a good job of breaking down the results on his blog, but the upshot is that this is the largest number of logical qubits created, and a very large leap ahead for the field.
00:00 Introduction and Background
01:07 Path to Quantum Computing
03:30 Rydberg Atoms and Quantum Gates
08:56 Transversal Gates and Logical Qubits
15:12 Implementation and Commercial Potential
23:59 Future Outlook and Quantum Simulations
30:51 Scaling and Applications
32:22 Improving Quantum Gate Fidelity
33:19 Advancing Field of View Systems
33:48 Closing the Feedback Loop on Error Correction
35:29 Quantum Error Correction as a Remarkable Breakthrough
36:13 Cross-Fertilization of Quantum Error Correction Ideas -
Summary
In this episode, Sebastian and Kevin are joined by Chiara Decaroli, a quantum physicist and venture capitalist. Chiara shares her unique journey into the field of quantum, starting from a small village in Italy to earning her PhD in quantum physics. She explains the history of ion trapping and how it led to the development of quantum computing. Chiara also discusses the strengths and weaknesses of trapped ion systems and the challenges of investing in early-stage quantum startups. In this conversation, Chiara Decaroli discusses the challenges of assessing quantum technologies and the deep expertise required in the field. She also shares her experience in gaining familiarity with different quantum modalities and the importance of multidisciplinarity in the quantum field. Chiara highlights the skills needed in the quantum industry, emphasizing the need for deep knowledge in physics and specialized segments. She also discusses the importance of cross-disciplinary education and the potential impact of quantum technologies.
Takeaways
Chiara's path to quantum started from a small village in Italy and led her to earn a PhD in quantum physics at ETH Zurich.
Ion trapping is a key technology in quantum computing, and it has a rich history dating back to the 1930s.
Trapped ions can be manipulated using laser beams to perform single and two-qubit gates.
Trapped ion systems have the advantage of perfect qubits but face challenges in scalability and speed of operations.
Investing in quantum startups requires a deep understanding of the field and the ability to navigate the early-stage landscape. Assessing quantum technologies requires deep expertise and a scientific background.
Gaining familiarity with different quantum modalities requires extensive reading and talking to experts in the field.
The quantum field is highly multidisciplinary, requiring expertise in physics, engineering, software development, and specialized domains.
Cross-disciplinary education is important in the quantum field to foster innovation and solve complex problems.
The potential impact of quantum technologies is immense, but it is challenging to predict the exact applications and advancements.Chapters
00:00 Introduction and Background
01:01 Chiara's Path to Quantum
08:13 History of Ion Trapping
19:47 Implementing Gates with Trapped Ions
27:24 Strengths and Weaknesses of Trapped Ion Systems
35:49 Venture Capital in Quantum
37:55 The Challenges of Assessing Quantum Technologies
39:12 Gaining Familiarity with Different Quantum Modalities
40:27 The Multidisciplinary Nature of Quantum Technologies
41:22 Skills Needed in the Quantum Field
42:58 The Importance of Cross-Disciplinary Education
44:27 The Potential Impact of Quantum Technologies -
In this episode of The New Quantum Era, Kevin Rowney and Sebastian Hassinger are joined by Dr. Ieva Čepaitė to delve into the nuanced world of quantum physics and computation. Dr. Čepaitė discusses her journey into quantum computing and her work on counterdiabatic methods used to optimize the control of many body quantum states. She provides an overview of the landscape of new algorithms available within the field. She points out the importance of understanding the hardware to implement a quantum algorithm effectively. The focus then shifts to a discussion on adiabatic and counterdiabatic systems, providing a detailed understanding of both methods. The conversation concludes with a speculative take on future breakthroughs that could emerge with respect to quantum algorithms.
00:31 Introduction and Overview of the Interview
02:43 Dr. Čepaitė's Journey into Quantum Computing
05:23 Dr. Čepaitė's Diverse Experience in Quantum Computing
09:37 The Challenges and Opportunities in Quantum Computing
11:50 Understanding Adiabatic and Counterdiabatic Systems
15:15 The Potential of Counterdiabatic Techniques in Quantum Computing
25:49 The Future of Quantum Algorithms
32:55 The Role of Quantum Machine Learning
35:48 Closing Remarks and Reflections - Mostra di più
