Code Distance: Increasing the code distance, which is the number of physical qubits used to encode a single logical qubit, exponentially suppresses the logical error rate. Willow demonstrates that increasing the lattice size leads to an exponential reduction in the logical error rate. Scalability is another significant achievement of Willow. The chip demonstrates the ability to exponentially suppress errors as the number of qubits scales up. Expanding the lattice size of physical qubits leads to a corresponding reduction in the encoded error rate. This exponential error suppression is a crucial milestone in achieving fault-tolerant quantum computing. Computational Power and Benchmarking: Willow’s benchmark computation showcased its computational power, completing a specific calculation in under five minutes. The same calculation would take one of today’s fastest supercomputers approximately 10 septillion years. Willow’s ability to solve the benchmark problem in under five minutes signifies quantum supremacy for this specific task. Quantum volume, a metric used to assess the overall computational capacity of a quantum processor, is significantly higher in Willow compared to previous quantum processors. Willow leverages superconducting transmon qubits, which are formed by Josephson junctions and exhibit improved coherence times and reduced noise sensitivity. Willow employs a 2D grid layout that supports surface code error correction, designed to be robust and scalable. Transmon qubits operate by increasing the shunt capacitance across the Josephson junction, thereby reducing charge noise sensitivity, and resulting in improved coherence times. The transmon qubit’s potential well is anharmonic, allowing for distinct energy levels that can be selectively manipulated using microwave pulses. Quantum gate operations are performed using microwave pulses, which are carefully calibrated to achieve high-fidelity single-qubit and two-qubit gates. The chip operates at temperatures near absolute zero, achieved using dilution refrigerators, to minimize thermal noise and decoherence. Willow’s advancements have opened up new possibilities for quantum algorithms and applications. Some promising algorithms and applications include: • Optimization Problems: Willow is well-suited for solving optimization problems, which involve finding the best solution from a large set of possible solutions. Its error correction capabilities make it effective in tackling optimization problems infeasible for classical computers. The Quantum Approximate Optimization Algorithm (QAOA) is designed to solve combinatorial optimization problems. • Simulating Quantum Systems: Simulating quantum systems, such as molecules and materials, is one of the most promising applications of quantum computing. Willow’s ability to reduce errors exponentially allows for more accurate and efficient simulations, potentially leading to breakthroughs in chemistry and materials science. The Variational Quantum Eigensolver (VQE) is used for finding the ground state energy of quantum systems, which has applications in quantum chemistry and materials science. • Machine Learning: Quantum machine learning algorithms can potentially outperform classical algorithms in certain tasks. Willow’s enhanced error correction and stability make it a strong candidate for developing and running advanced quantum machine-learning algorithms.