关于量子计算，我们仍不知道它到底能做什么

Core Insights - The quantum computing field is experiencing significant growth but still grapples with the fundamental question of its practical applications [1][2][3] - There is a call for a more pragmatic approach to developing quantum algorithms, focusing on verifiable and practical solutions rather than adhering strictly to traditional standards [4][5] Technological Momentum - The theoretical foundation for quantum error correction is solid, with several platforms nearing or achieving error correction thresholds [2] - A substantial investment of $100 billion over the next few decades could lead to the construction of a large-scale quantum computer [2] Application Challenges - Unlike nuclear fusion, which has clear application value if successful, quantum computing lacks sufficient application drivers to justify the massive investments in research and infrastructure [3] - The industry must accelerate algorithm development alongside hardware advancements to maintain investment momentum [4] Empowering Theorists - Theoretical research plays a crucial role in shaping the future of quantum computing, with historical examples like Geoffrey Hinton's work in AI demonstrating the impact of theoretical foundations [5] - The community is looking for new theoretical insights to drive practical advancements in quantum computing [5] Challenges in Quantum Algorithms - Ideal quantum algorithms are traditionally expected to meet three criteria: provable correctness, classical intractability, and practical applicability [6] - Strict adherence to these criteria may hinder the discovery of innovative quantum algorithms [7][10] New Standards for Quantum Algorithms - A shift towards a more pragmatic standard is suggested, where quantum algorithms should achieve super-quadratic speedup compared to the best classical algorithms [9] - The focus should be on finding quantum algorithms that are sensitive to input variations and produce outputs that can be verified or repeated [11][12] Classification of Quantum Algorithms - Quantum algorithms can be categorized based on their output types, including search problems, numerical calculations, quantum property proofs, and sampling tasks [14][17] - Hamiltonian simulation is highlighted as a well-known application of quantum computing, with potential to solve classically intractable problems [15] Future Directions - There is a need to explore new input distributions and frameworks to discover genuinely novel quantum algorithms [17] - Quantum technology also holds promise in areas like sensing, communication, and data processing, although current focus remains on achieving computational advantages [20] Research Landscape - Despite the importance of new quantum algorithms, the number of research efforts in this area remains low, attributed to the complexity of quantum algorithm research [21] - A "mission-driven" exploratory mindset is encouraged to advance the field, allowing for the pursuit of quantum advantages in underexplored areas [22][23]