| Home | Registration | Program |
| 10:00am | Introduction to the workshop |
| Raffaele Santagati, Boehringer Ingelheim |
| 10:05am | Optimizing Quantum Algorithms for Next-Generation Quantum Chemistry |
| Artur F. Izmaylov, University of Toronto | |
| Quantum chemistry problem is one of the attractive targets for demonstrating quantum advantage of quantum computing technology. Having strongly correlated systems as the main target, I would like to discuss what new classical computing techniques need to be developed to help quantum computing algorithms to solve the electronic structure problem. Encoding the electronic Hamiltonian in the second quantized form on a quantum computer is not a trivial problem, and its efficiency can become a bottleneck for the entire quantum solution. Dealing with this Hamiltonian can be facilitated by partitioning it into a sum of fragments diagonalizable using rotations from either small Lie groups or the Clifford group. These fragments are convenient for performing various algebraic manipulations required in circuit compiling and quantum measurement. I will illustrate how the Hamiltonian partitioning can be used to improve performance of the Quantum Phase Estimation algorithm. Another challenge that will be discussed is a recent approach to certify that an initial state for QPE has a significant overlap with the eigenstate of interest. |
| 10:50am | Quantum Optimization Without (much) Learning |
| Nathan Wiebe, University of Toronto | |
| Gradient optimization has been a staple of continuous optimization and hybrid quantum algorithms. Despite its ubiquity, evaluating gradients on a quantum computer within fixed relative error can be a significant challenge owing to the restrictions placed by quantum mechanics on learning a gradient. In this talk I will present a new method that can be used to optimize functions on a quantum computer that skips this learning process. We provide analytic bounds that are exponentially better, for some problems, than those attained for gradient descent and show rigorous convergence for locally quadratic objective functions. This work provides evidence that quantum optimization may be much more scalable on quantum computers than previously believed and provides an approach that allows us to mitigate the effects of small gradients by removing the need to learn them at all in the optimization process. |
| 11:30am | Lunch Break |
| 1:00pm | From In Silico to In Vitro: Experimental Validation of Bioactive Molecules Designed by Hybrid Quantum-Classical Machine Learning Model |
| Christoph Gorgulla, St. Jude Children's Research Hospital | |
| The identification of promising hit and lead compounds is a primary bottleneck in drug discovery, defined by the immense challenge of navigating a virtually limitless chemical space. To test if quantum computing offers a tangible advantage in this area, we developed a hybrid quantum-classical generative model and benchmarked it directly against a purely classical counterpart. Both models were tasked with designing novel inhibitors for KRAS, a key cancer target, with the quantum model using a 16-qubit quantum computer from IBM. Initial computational benchmarks demonstrated a superior performance for the quantum model regarding the quality of the generated molecules. This computational advantage translated into tangible experimental success: after synthesizing 15 promising candidates jointly from both models, the two molecules confirmed as the best biological hits, a broad-spectrum inhibitor and a distinct, mutant-selective compound, both originated from the quantum-generative model. To our knowledge, this work is the first to translate molecules designed by a quantum-generative model into experimentally validated biological hits. It demonstrates the practical potential of quantum-assisted computing for therapeutic design. |
| 1:45pm | The Grand Challenge of Developing Quantum Applications |
| Nicholas C. Rubin, Google Quantum AI | |
| In this talk, I will discuss how our team at Google understands and approaches the problems of developing new quantum algorithms and matching them to real-world applications. I will survey several recent results from our team across several domains including optimization, linear and differential equations, and quantum simulation. My talk will also touch on problems of emerging importance such as the design and compilation of algorithms for very small fault-tolerant quantum computers. |
| 2:30pm | Coffee Break |
| 3:00pm | Enabling Scientific Discovery through Quantum and HPC Workflows at NERSC |
| Katherine Klymko | |
| The National Energy Research Scientific Computing Center (NERSC) is taking an active role in shaping the practical landscape of quantum computing. This talk will provide a snapshot of how NERSC is supporting near-term and future quantum applications through strategic programs and partnerships. I will highlight three key areas of focus: (1) the Quantum Computing Access at NERSC (QCAN) initiative, which offers researchers access to both quantum hardware and large-scale classical resources to explore hybrid workflows; (2) collaborative R&D efforts with QuEra Computing that target scientific use cases on neutral atom quantum systems; and (3) NERSC’s development of benchmarking and characterization tools to evaluate real-world quantum performance. I will close with a discussion of promising application domains, technical challenges, and NERSC’s strategy for supporting future computational workloads that leverage both HPC and quantum computing. |
| 3:45pm | Panel Discussion - Quantum Advantage in Industry: Bridging Expectations and Reality |
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| This panel brings together leading voices from academia and industry to engage in a candid discussion on the current state and future trajectory of quantum computing in the chemical and pharmaceutical sectors. Building on insights from the keynote presentations, panelists will explore the gap between theoretical promise and practical implementation, focusing on algorithmic advances, resource requirements, and industrial relevance. The conversation will address critical questions: Where can quantum computing realistically add value today? What are the most promising paths forward? Furthermore, how can academia and industry collaborate more effectively to accelerate progress? Attendees will gain a nuanced understanding of the challenges, opportunities, and timelines for achieving quantum advantage in real-world applications. |