Q4Bio - Quantum for Bio

Meet Performer Harvard. ? This team, led by PI?mikhail lukin and co-Pis Joonho lee,? Susanne Yelin, James collins and Norman Yao?from? Harvard University,? Alex K. Shalek from Massachusetts Institute of Technology?and?Shengtao Wang?from?QuEra Computing Inc., is working on developing quantum and classical algorithms to tackle two core challenges encountered in drug design: obtaining high-resolution protein structures, and estimating the binding affinity between proteins and small drug molecules. ? During phase I, the team made advancements in various directions, including classical methods for capturing strong quantum correlations, and quantum algorithms for simulation of NMR and electronic structure. Using co-design to develop algorithms and hardware in tandem, they are further tailoring their approaches for quantum simulators based on neutral atom arrays. The team also developed a bioinformatics pipeline to identify protein targets that are expected to contain large quantum correlations and therefore would benefit from modeling with quantum computers. ? Moving into phase II, they will begin applying their algorithms to the targets identified during phase I, and estimate the resources required to implement these models on quantum hardware. They will further focus on developing simplified models that capture the classically challenging features of the NMR and protein-ligand binding problem, but are more amenable for near-term quantum implementations.? ? #quantum?#simulation?#Harvard?#MIT?#QuEra?#BroadInstitute?#atomarrays https://lnkd.in/gdz7YKfP

Meet performer Stanford. The team is led by PI?Grant Rotskoff (Stanford University), co-PIs Norm Tubman, Ryan LaRose, Sophia Economou (Virginia Tech), Brenda Rubenstein (Brown University), Vojtech Vlcek, and Andres Montoya Castillo (University of Colorado Boulder), integrating expertise across quantum information science, quantum and classical dynamics, chemistry, and biophysics. The project aims to understand hydrolysis reactions in protein environments, with a particular focus on adenosine triphosphate (ATP) and guanosine triphosphate (GTP), the all-important metabolic reactions that drive cellular function. Metabolic dysfunction is a key determinant of disease, including many cancers, so a detailed, mechanistic understanding of how proteins catalyze the hydrolysis of ATP and GTP could aid the design of new inhibitors and therapeutic strategies. In Phase 1, they significantly advanced in developing variational quantum algorithms for quantum chemistry calculations, largely based on co-PI Economou’s influential adaptive variational quantum eigensolver algorithm, ADAPT-VQE. In particular, the developed algorithm reduced both measurement costs and circuit depth by such a significant margin (https://lnkd.in/gNZnKGnD) that realistic Hamiltonians should be within reach for physical hardware before Phase 3. The team also developed measurement reduction strategies that create a smart measurement basis and are the current state-of-the-art (https://lnkd.in/gZT-vFS5). Finally, the team carried out proof-of-principle molecular dynamics studies in which VQE was used to build molecular dynamics force fields to drive quantum mechanically accurate dynamical simulations (https://lnkd.in/gqg3kQyd).? In Phase 2, the team will leverage these developments to prepare model Hamiltonians for real quantum hardware by conducting scaling experiments using circuit simulations. The team has worked closely with the quantum computing team at the Department of Energy’s National Energy Research Scientific Computing Center and plans to build out HPC infrastructure for quantum computing-driven molecular dynamics. The resulting calculations will inform the development of molecular dynamics models of hydrolysis that will then be used to answer fundamental questions about one of the most important chemical reactions in human biology. #AdaptVQE #Quantumcomputing #AI #QuantumforBio #QML #QuantumChemistry #QuantumSimulation? #WellcomeLeap

Let's meet Performer 'Cambridge'. The team is led by PI? Sergii Strelchuk (University of Oxford and University of Cambridge) and co-PIs Richard Durbin (University of Cambridge), James McCafferty (Wellcome Sanger Institute), and David Yuan (European Bioinformatics Institute | EMBL-EBI) Genome sequencing is vital for applications used in monitoring disease outbreaks and personalized medicine. The structure of many challenging problems in computational genomics and pangenomics in particular makes them suitable candidates for quantum computing speedups. The resulting advances have the potential to unlock transformative health benefits that depend on large-scale genomic analysis. ? In Phase 1, the team adapted complex problems like genome assembly and the construction of phylogenetic trees into a hybrid quantum-classical framework, enabling promising quantum speedups with emerging technology. Their key innovations include scalable quantum data encoding algorithms, which set the stage for storing and manipulating significant amounts of genomic data, and faster algorithms for genome assembly and phylogenetic tree inference.? ? In Phase 2, the team aims to simulate the new approaches to HPC using machine-learning-oriented encoding schemes and tensor network methods. They shall also test the algorithms' ability to resolve parts of the genome graph that are intractable classically. They will gain further insight into performance at scale to move forward in Phase 3 to implementation on real quantum hardware. Collaborating with quantum hardware vendors, they aim to ensure that the proposed implementations account for hardware-specific architecture and noise properties.

Meet performer Infleqtion. The team is lead by PI Fred Chong and co-Pi Teague Tomesh?at Infleqtion,?Samantha Riesenfeld?and?Alexander Pearson at University of Chicago and Aram Harrow at Massachusetts Institute of Technology. Their aim is to explore how quantum computing can enhance data processing for multi-modal cancer data, focusing on the complex relationships between genomic, transcriptomic, and pathomic aspects of cancer biology, a computationally demanding challenge with many unanswered questions. Their survey on quantum technology applications in oncology was published as the first of its kind in Nature Cancer. In Phase 1, the team developed a hybrid quantum-classical algorithm for feature selection which is an important dimensionality reduction technique to mitigate overfitting issues in multi-modal cancer data and may uncover novel biological insights by identifying predictive feature sets. Their approach leverages mutual information to quantify the subtle correlations between features and construct a combinatorial optimization problem that can be solved by a quantum computer to identify small and informative feature sets. Simulations on the NERSC Perlmutter supercomputer produced promising results for the algorithm's performance. In Phase 2, the team will continue scaling up their circuit simulations and leverage extensive cross-layer compilation techniques to prepare for experiments on real quantum hardware. Simultaneously, they will first apply their feature selection algorithm to tasks such as predicting cancer tissue of origin and then examine more challenging tasks like selecting features to?better predict a cancer’s response to treatment, paving the way toward high-impact applications of quantum computing to cancer treatment and personalized medicine. Q4Bio wishes performer Infleqtion the very best of luck. https://lnkd.in/dSYUug7e

Meet performer Nottingham Myotonic dystrophy is a genetic condition that causes progressive muscle weakness and wasting. It is the most frequent form of muscular dystrophy in adults worldwide, with an estimated 6,500 people affected in the UK. The Nottingham team, led by PI Jonathan Hirst (University of Nottingham) and co-PI Katherine Inzani (University of Nottingham), Ashley Montanaro (Phasecraft), and Tommaso Macrì (QuEra Computing Inc.), aims to tackle this critical issue with cutting-edge quantum computing. They aim to utilize quantum computing to deepen our understanding of the biochemical processes involved in treating myotonic dystrophy. The strategy is to utilize quantum-enhanced atomistic modeling pioneered by Phasecraft, embedded into multiscale quantum chemical simulations developed at the University of Nottingham, with quantum circuits tailored to QuEra Computing Inc.'s world-leading neutral atom quantum computers. In Phase I, the team demonstrated quantum-enhanced density functional theory simulations in a setting that is both familiar and practical for computational chemists. They also developed a framework across the full stack of classical simulation, quantum software, and quantum hardware, providing a self-contained process capable of delivering end-to-end quantum-enhanced simulations. By advancing this challenging research frontier, the team aims to provide insights into the mechanisms of new drugs and bring quantum advantage to the life sciences and chemistry communities. #Quantumcomputing #QuantumforBio #QuantumChemistry #QuantumSimulation #WellcomeLeap #QuantumAlgorithms #practicalquantumadvantage #Q4Bio #DensityFunctionalTheory #CovalentInhibitors

It is time to meet performer 'Copenhagen'. The team is led by Professor and PI Matthias Christandl, Department of Mathematical Sciences, University of Copenhagen (K?benhavns Universitet) (UCPH). Co-PIs from UCPH are Kresten Lindorff-Larsen, Department of Biology, Professor Gemma Solomon, Department of Chemistry and NQCP Niels Bohr Institute, and Professor Anders Krogh(Department of Computer Science). Co-PIs from outside UCPH are Professor Aram Harrow and Professor Troy Van Voorhis (Massachusetts Institute of Technology), Professor Markus Reiher (ETH Zürich) and Corporate Vice President Allan Christian Shaw (Novo Nordisk A/S). The primary goal of their project ‘Molecular Recognition from Quantum Computing’ is to design and implement a fully integrated computational framework that uses quantum computers to simulate and quantify molecular recognition processes with unprecedented accuracy. In phase 1, the team developed a bottom-up computational pipeline to understand binding in biologically and pharmacologically relevant protein-ligand systems. Key to their work is the understanding of the embedding of smaller quantum systems into a larger classical environment. The innermost quantum regions will be treated with a quantum computer, for which the team has developed tailored quantum algorithmic strategies (e.g. state preparation and perturbation extensions). in Phase 2, they will continue working on large-scale simulations of developed algorithms in the first phase using classical high-performance computing. #Quantumcomputing #AI #QuantumforBio #QML #QuantumChemistry #QuantumSimulation #WellcomeLeap

Time to meet performer qBraid. The team consists of PI:?Kanav Setia, co-Pi (Laura Gagliardi, (University of Chicago), Troy Van Voorhis (Massachusetts Institute of Technology), Yuri Alexeev (NVIDIA), Raymond Samuel (NCAT), Yuan Liu, NCState and Matthew Otten (UWisconsin), Shengtao??Wang and Nate Gemelke(QuEra Computing Inc.) qBraid's Quanta-Bind project aims to unravel the mystery of Amyloid Beta protein's role in Alzheimer's disease. This intrinsically disordered protein forms plaques in the brains of patients, but its exact interaction with transition metal ions remains unclear. Traditional computational methods have struggled to provide conclusive results due to scaling issues. Quanta-Bind addresses this by combining fragmentation methods with quantum computing, using generalized superfast encoding to map fermionic Hamiltonians to qubit Hamiltonians. In Phase 1, the team successfully developed a software pipeline that processes protein geometry and generates quantum circuits using various fragmentation techniques. This pipeline has been validated on small-scale systems and optimized for current quantum hardware. For Phase 2, qBraid plans to run and refine noisy quantum circuits for diverse systems, paving the way for Phase 3 of the Q4Bio program. This innovative approach could revolutionize our understanding of protein-metal ion interactions and potentially accelerate Alzheimer's research. #QuantumComputing #Q4Bio #QuantumSoftware #QEC #Quantum4Biology #qBraid #QuantumApplications

It’s time to introduce the Phase 2 Q4Bio performers in more detail. Meet Performer "Algorithmiq". The team is lead by PI Sabrina Maniscalco and co-Pi Vijay Krishna from Cleveland Clinic and Ivano Tavernelli from IBM.? ? Their main goal in this project is to unleash the power of quantum computers to rationalize optimal drugs for photodynamic cancer therapy. ? Throughout Phase 1, they developed an end-to-end quantum algorithmic software framework which allowed the team to perform the first, successful photochemistry quantum simulations on IBM system one at Cleveland Clinic for a clinically relevant photosensitizer drug molecule. ? In Phase 2, the focus will be on large-scale noisy?simulations of the algorithms developed in Phase 1 using classical high-performance computing (HPC) to assess their scalability beyond utility scale?including Algorithmiq’s noise-mitigation techniques.? ? Thanks to Sabrina Maniscalco Stefan Knecht Elsi-Mari Borrelli from the Algorithmiq team for their participation. #Quantumcomputing #QuantumforBio #QuantumChemistry #QuantumSimulation #WellcomeLeap

Quantum for Bio (Q4Bio) is a $40M +$10M Supported Challenge Program aimed at accelerating the applications of quantum computing in human health. Q4Bio is focused on identifying, developing, and demonstrating biology and health applications that will benefit from the quantum computers expected to emerge in the next 3-5 years. $40M in research is directed at our ultimate goal -- demonstrate the potential of quantum computing in solving critical health challenges. And $10M in prizes await those who do so! This is the place to meet the teams taking on the challenge. Follow along in their journey as they make progress, overcome challenges, collaborate, and yes, also compete. wellcomeleap.org/q4bio #Quantumcomputing #AI #QuantumforBio #QML #QuantumChemistry #Pangenomics #QuantumSimulation #WellcomeLeap