I-Corps: Translation Potential of Co-Designed Quantum Compilation for Life Science Applications

This I-Corps project is based on the development of a software that increases the efficiency of future fault-tolerant quantum computers. Quantum computers may one day solve problems in chemistry, energy, and finance that are impossible for current machines. However, quantum computers demand enormous and expensive hardware and current software wastes scarce hardware because the software instructions, the error correction, and the machine are designed in isolation. This technology optimizes these layers together, reducing the hardware a given task requires. Organizations across sectors preparing to adopt quantum computing need quantitative guidance on which of their problems the technology will help, at what cost, and on what timeline, and they will need efficient software once capable machines are available. This technology may support faster progress in areas that require computationally intensive processes such as drug discovery and the design of new materials. This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of a cross-layer compiler for fault-tolerant quantum computation. Conventional quantum compilers treat the quantum algorithm, the quantum error-correcting code, and the hardware instruction set as independent layers, each optimized separately. This technology instead performs joint optimization across the entire stack, searching for the execution path that minimizes the fault-tolerant resource cost of a workload, measured in logical qubits and spacetime volume. Preliminary research has demonstrated two-to-seven-fold reductions in qubit footprint relative to state-of-the-art baselines. Applied to the life sciences, it targets the simulation of biologically relevant molecules, where fault-tolerant quantum computing holds its strongest theoretical advantage, as well as the identification of predictive patterns in complex biological data. Users benefit in two stages: near-term resource estimates that quantify what a target workload may cost on current and future hardware and when it becomes worthwhile, and, as hardware matures, an efficient path to executing those workloads. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.