Before revealing the first on-campus quantum computer in the US, Rensselaer Polytechnic Institute invited experts from across the country to discuss the current state of quantum computing. Here are the key points.

Figure 1. IBM Quantum System One at RPI's Voorhees Computing Centre.

Ahead of the ribbon-cutting ceremony for its new IBM System One quantum computer, the first of its kind on a college campus, Rensselaer Polytechnic Institute (RPI) recently hosted a quantum computing day. The event featured several distinguished speakers who collectively offered insights into the current state of the field. As someone who has been covering quantum computing for a significant period, I've observed notable advancements, yet there remain numerous challenges that must be addressed. Figure 1 shows IBM Quantum System One at RPI's Voorhees Computing Centre.

From Quantum Physics to Quantum Computing

Jay M. Gambetta, Vice President of Quantum Computing at IBM, presented an overview of quantum computing's history, advancements, and future challenges. He emphasized that quantum computing utilizes the quantum properties of qubits, like superposition and entanglement, to solve problems beyond classical computers' capabilities. Gambetta discussed the evolution of superconducting qubits, from single-qubit to the 127-qubit IBM Eagle chip in the Quantum System One. He highlighted the need for larger systems and improved error correction to achieve "quantum utility" where quantum computers outperform classical simulations. The panel discussed quantum computing applications in quantum chromodynamics and computational materials science.

Major Challenges and The Importance of Error Correction

Steve M. Girvin, Eugene Higgins Professor of Physics at Yale University, discusses the challenges of creating error-correcting quantum computers. He describes two quantum revolutions: the first, including transistors and lasers, and the current based on new quantum mechanics insights. Girvin cautions against overhyping quantum computing, stressing it's revolutionary but not yet fully mature. Quantum sensors, ultra-sensitive to perturbations, pose challenges for computers, necessitating robust error correction. Girvin highlights algorithmic challenges, fault tolerance, and the complexity of routing quantum information. He notes progress but emphasizes a long journey ahead.[1]

The Road to Quantum Advantage

In a morning session, Austin Minnich from Caltech discussed "mid-circuit measurement" and hybrid circuits to mitigate errors in quantum computing. Kerstin Kleese van Dam of Brookhaven National Lab emphasized the potential of quantum computers in areas like machine learning, highlighting the need for larger systems to achieve quantum advantage. She also raised concerns about energy consumption compared to traditional AI models. Shekhar Garde of RPI moderated, likening the current state of quantum computing to early traditional computing. The panel envisioned hybrid systems and user-friendly quantum libraries within 10 years, aiming to demystify quantum computing and enhance accessibility.

• Cryptography, exemplified by Shor's algorithm for factoring large numbers.
• Unitary quantum processes, involving simulating quantum dynamics under a given Hamiltonian, as envisioned by Richard Feynman.
• Non-unitary quantum processes, which are challenging for classical computers to implement efficiently. Recent quantum algorithms attempt to translate these non-unitary problems into unitary ones.
• Classical processes, involving solving linear or nonlinear equations, which may not be inherently quantum, particularly those with high-dimensional data.

Lin highlighted that while many believe quantum advantage will be achieved in the first two levels due to established methods, there is still much work needed on algorithms for the next two levels. This makes the current period exciting for mathematicians, physicists, chemists, and computer scientists alike.

Lin Lin, a mathematics professor at the University of California, Berkeley, discussed his interest in solving quantum many-body problems and applying quantum computing to areas like numerical analysis and linear algebra. He compared the current state of quantum computing to where classical computing was 60 to 70 years ago, emphasizing the importance of interdisciplinary approaches to handle errors in quantum systems.

During a panel discussion, Ryan Sweke from IBM Research highlighted the different levels of abstraction in classical computing and noted the unique challenge in quantum computing where those working on algorithms need to collaborate closely with hardware developers. Di Fang, Assistant Professor of Mathematics at Duke University, described the current era as a "golden time" for algorithm development, focusing on both theoretical and practical problem-solving.

Brian McDermott, Principal R&D Engineer at the Naval Nuclear Laboratory, discussed the reverse approach of identifying practical problems and working backward to develop quantum hardware and software solutions. The panelists explored the potential impact of quantum algorithms across various fields, from computational fluid dynamics and material science to nuclear engineering and astrophysics.[2]

Education And Workforce Development

Olivia Lanes, Global Lead and Manager for IBM Quantum Learning and Education, emphasized the need for workforce development in the quantum field, citing a projected shortfall of STEM workers and Carl Sagan's warning about society's dependence on technology without understanding it. She highlighted the lack of understanding of both quantum and classical computing, with a significant gap between job openings and qualified candidates. Lanes advocated for upskilling and diversifying expertise within the quantum ecosystem, urging individuals to focus on their interests and build rare skills.

Anastasia Marchenkova from Bleximo Corporation emphasized the importance of bridging the gap between pop science and research to enable broader usage of quantum computing. Richard Plotka of RPI stressed the need for middleware tools to help existing professionals leverage quantum technology and prepare students with foundational knowledge for future career adaptability.

References:

1. https://www.pcmag.com/articles/how-we-facilitate-the-next-big-leap-in-quantum-computing-rpi-ibm
2. https://medium.com/@snehankanmukherjeework/quantum-computing-the-next-giant-leap-in-technology-is-here-59b37e60d3ec

Cite this article:

Gokila G (2024), Here's How We Can Enable the Next Major Advancement in Quantum Computing, AnatechMaz, pp.139