However, analyzing it calmly, we see novel tech narratives every day. For example, some companies propose using space mirrors to reflect sunlight into your backyard at night for a party; others have technology to fly wind turbines like kites at altitudes of 500 to 15,000 meters to generate power from strong winds, transmitting it back to the ground via cables. From a personal perspective, we certainly welcome all technological innovations. But from a secondary market investment perspective, we place immense value on commercial viability, because rigorous secondary market valuations must be built on the assumption that a company can operate sustainably.

Back to the quantum computing sector, some stocks once surged 100-fold. We believe this is largely a "narrative premium" driven by US market liquidity. Its indefinitely postponed commercialization prospects and technical flaws will be infinitely magnified once liquidity tightens, potentially leading to a 90% drop in stock prices. Therefore, for sectors and individual stocks where the narrative premium is strongly decoupled from commercialization, we will research them and wait for shorting opportunities.

Taking RGTI in the quantum computing space as an example, its market cap once exceeded USD 10 billion. However, its debt-to-asset ratio is exceptionally high among peers, and its meager revenue completely fails to cover its investments. More importantly, at the technical level, sacrificing stability to pursue extremely fast computing speeds seems to deviate from the biggest engineering direction of quantum computing—namely, the stability of low error rates. Therefore, it was once our favorite short target, especially against the backdrop of tightening liquidity.

I. Long Technology Cycle: From "Narrative Bubble" to "Physical Reality"

Over the past two years, due to breakthrough announcements from Google, IBM, and some startups, quantum computing was endowed with the sci-fi narrative of "quantum supremacy" and "dimensional strikes on AI." However, entering 2025/2026, the industry encountered a brutal engineering bottleneck:

• The fog surrounding fault-tolerant quantum computing remains: The core challenge of the industry has shifted from pure "physics innovation (how many qubits are produced)" to extremely complex "systems engineering and error correction control (building a truly usable fault-tolerant computer)." Currently, the industry generally lacks quantum algorithms that are commercially viable and economically beneficial.

• The "cold water effect" from industry giants: Previously, executives of several computing giants publicly stated that the commercialization of quantum computing still requires a long wait of >10 years. This pessimistic quantitative assessment directly shattered the fantasy of replacing traditional silicon-based computing power in the short term and triggered a dramatic correction on Wall Street regarding the sector's advanced pricing.

Judging from balance sheets and income statements, these pure-play quantum computing targets have irreconcilable structural flaws:

• Inefficient revenue scale, unable to support high-multiple valuations: The annual revenue of most US pure-play quantum computing companies generally ranges from millions to tens of millions of USD, while earlier valuations were pushed up to billions or even tens of billions. This extreme Price-to-Sales (P/S) ratio and high beta coefficient (some targets have a Beta exceeding 3.5) make them the most vulnerable targets when market sentiment turns.
• Continuous cash flow outflows and equity dilution: Quantum computing R&D is a capital-devouring marathon. Take IonQ as an example: its quarterly revenue scale is severely imbalanced against its hundreds of millions of dollars in EBITDA losses and high capital expenditures. To maintain cash flow, companies frequently "drain" the secondary market through equity offerings or convertible bonds. The continuous dilution of shareholder equity has become the direct driver crushing the stock price from its highs.

III. Macro Liquidity and Industry Transmission: The "Chain Reaction" Triggered by AI Monetization Anxiety

The quantum computing sector does not exist independently of the broader tech stock cycle; its drawdown is heavily constrained by the overall tech ecosystem and macro interest rates:

1. Transmission Chain: Rising US Treasury yields / Tightening liquidity ➡️ Suppressing Discounted Cash Flow (DCF) valuations for long-term cash flows ➡️ Speculative high-growth stocks are the first to bear the brunt.
2. AI Industry Chain Monetization Anxiety: The market demands that companies prove the Return on Investment (ROI) for massive capital expenditures.
3. The Merciless Strike on Long-term DCF Valuations: Against the backdrop of oscillating and rising long-end US Treasury yields or maintaining high levels, the systemic rise in the denominator (discount rate) has dealt a devastating valuation blow to "ultimate long-term growth stocks" like quantum computing, which "pin their profits on 10 or even 20 years into the future."
4. The AI Investment Logic Shifts from "Storytelling" to "Looking at ROI": Citadel Securities and a host of Wall Street giants have recently warned frequently that the market is gradually losing patience with the massive capital expenditures on large models and AI computing power, demanding that companies must prove they can bring sufficient realistic returns. This "pragmatic" trend rapidly transmits downstream and to the more cutting-edge quantum computing field. As Wall Street begins to re-examine the revenue growth expectations of AI leaders like OpenAI and Anthropic, quantum computing—still in the "pure R&D stage"—has naturally become the first stop for capital fleeing to safe havens.

IV. The Next Catalyst for the Quantum Computing Industry

We believe the next catalyst will be a breakthrough that truly solves practical problems. For example, the entire industry is waiting for a quantum computing application that can instantly factor large numbers (almost all modern cryptographic security systems are built on the basis that large numbers are difficult to factor quickly).

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