Liangkun Technology Secures Hundreds of Millions in Angel Round Funding, AI4S Needs Quantum-Level Precision Data | 36Kr Exclusive
Another "water seller" has entered the quantum computing track, but this time it's not selling shovels— it's selling an operating system. Quantenkun has just completed an angel round of several hundred million RMB. The founder, Lü Dingshun, has a clear logical chain: the model capabilities of AI for Science (AI4S) are bottlenecked by data precision, while quantum computing is inherently a high-precision simulator of the microscopic world, capable of providing "quantum-level" clean data for AI. I
Analysis
Another "water seller" has entered the quantum computing track, but this time it's not selling shovels— it's selling an operating system. Quantenkun has just completed an angel round of several hundred million RMB. The founder, Lü Dingshun, has a clear logical chain: the model capabilities of AI for Science (AI4S) are bottlenecked by data precision, while quantum computing is inherently a high-precision simulator of the microscopic world, capable of providing "quantum-level" clean data for AI. It sounds perfect, but can this path of "hardware lags, software leads" actually work?
Lü Dingshun's resume is indeed impressive—a PhD from Tsinghua University's first cohort in quantum computing, with stints at Huawei and ByteDance, and even leading a team to challenge Google's "quantum supremacy" narrative on a GPU cluster of hundreds of cards. This operation itself is quite "exciting," but he claims not to be overly thrilled. He is clear-headed: 53 quantum bits can still be勉强 chased by classical computers, but when the scale reaches 100 or 200 bits, classical computers will fall behind. The real excitement lies in using this computational power to solve "real-world big problems" like high-temperature superconductivity and new material design. During his time at Huawei, he used quantum computers as a hammer looking for nails; at ByteDance, his approach shifted to combining AI, quantum computing, and classical algorithms, all with one goal: solving scientific problems more accurately and efficiently.
Thus, Quantenkun was born, positioned as the "operating system for quantum computing power." They don't build hardware, nor do they directly serve end-user researchers; instead, they occupy the middle ground—packaging quantum algorithms, AI models, and industry workflows into "scientific intelligence units." This idea is both clever and pragmatic. Currently, quantum hardware is still in the "scarce resource" phase; a slight inefficiency in algorithms could mean the difference between "runs" and "doesn't run"—a binary outcome. What Lü Dingshun's team does is squeeze as much value as possible from the limited quantum bits, using algorithmic optimization to compensate for hardware shortcomings. They even convert chemical problems requiring tens of thousands of bits into solvable problems using just 20 quantum bits through quantum embedding methods. This "leveraging the small to achieve the great" approach truly embodies the value at the software level.
However, stepping back, the risks here cannot be ignored. The advancement of software ultimately depends on progress in hardware. If the scaling of quantum hardware doesn't meet expectations, or if there's a disruptive shift in technological routes (e.g., among ion traps, superconducting qubits, or topological qubits), could the algorithmic moat built in advance suddenly find itself facing no battlefield on the other side? Lü Dingshun says the algorithmic barrier lies in "design and optimization," making the path as short as possible. But if future hardware provides enough raw computational power, will such extreme optimization still be as critical? It's like designing the optimal route for a horse-drawn carriage, only to have a railway built to your doorstep the next day.
Another intriguing point is the founder's mention that "the team is a reflection of the founder's inner cognition"—they recruit people with "strong ambition." This is good, but cross-disciplinary teams (quantum, AI, high-performance computing) inherently have high coordination costs. Aligning top quantum physicists, algorithm engineers, and industry experts toward the somewhat vague goal of "solving real-world big problems" likely poses management challenges as formidable as the technical hurdles.
Returning to the investment logic, institutions like Inno Angel and Baidu Ventures are betting on the long-term narrative that "AI4S needs quantum computing." What Quantenkun offers is a solution that can generate value even during the current immature phase of hardware—using a hybrid heterogeneous computing platform to orchestrate classical computing, AI, and quantum computing, prioritizing "bottleneck" fields like strongly correlated materials and chemical simulations. If they can continuously demonstrate advantages of a few decimal places over traditional methods, as they have with high-temperature superconductivity models, this path will only widen.
Ultimately, Quantenkun is like a competitor in a vast but fog-shrouded arena, choosing not to directly participate in the race to "build the most advanced machine," but instead to first set the rules and create the training software. The bet is that once the fog lifts, all competitors will need the "navigation system" they provide. The idea is exciting, but it also tests patience and resolve. After all, in the long-termist track of quantum computing, excitement is important, but surviving and proving one's indispensability is even more so.
Disclaimer: The above content is generated by AI and is for reference only.