D-Wave Quantum Still Lags Behind The Industry Average, But Pessimism Is Somewhat Bloviated Post Q1 2026 Revenue Drop
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Peter Zak2 FollowersFollow5ShareSavePlay(10min)CommentsSummaryD-Wave Quantum Inc. underperforms peers on return and volatility metrics and remains more unpredictable than other pure-play quantum companies, but investor pessimism is exaggerated.Recent revenue declines were expected due to prior one-time sales; upcoming bookings and $42.4M in performance obligations could materially lift revenue in Q3 or Q4 2026.QBTS's stock is more sensitive to revenue than EPS, with recent contracts and backlog potentially driving a near-term re-rating despite sector hype and volatility.With $588.4M in cash and a six-year runway, QBTS is positioned to weather short-term unpredictability; a 'Hold' rating is justified over prevailing 'Sell' sentiment.Editor's note: Seeking Alpha is proud to welcome Peter Zak as a new contributing analyst. You can become one too! Share your best investment idea by submitting your article for review to our editors. Get published, earn money, and unlock exclusive SA Premium access. This article was written byPeter Zak2 FollowersFollowMy approach combines fundamental reasoning, data analysis, and real-time market signals to identify asymmetric risk-reward opportunities driven by structural shifts, policy changes, and market inefficiencies. I successfully followed global trends and implemented event driven strategies, for example during the COVID-19 pandemic I followed global infections, social media trends, government announcements, hospitals updates, etc. and made over 50 percent profits from my investments in biotech and Pharma ETFs and individual companies, like BioNTech. Similarly, followed the news prior to the Russian invasion of Ukraine and bough gas and wheat during the buildup of the troops at the Ukrainian border, as there was little risk in these assets declining in value if Russia would not invade, but significant upside considering Ukraine wheat supplies and reliance on Russian gas. I independently built and implemented statistical arbitrage models across DEX exchanges (dYdX, Hyperliquid) to exploit price discrepancies for perpetual futures contracts, using web sockets (for real time execution), and implemented a long-short market- neutral arbitrage strategy via automated API order flows by longing the slightly undervalued and shorting overvalued assets. Constructed real-time algorithmic trading models executing minute-to-minute level trades on Binance, based on sequency-based ML models (e.g. LSTM) trained on OHLC, volume, and technical indicators; deployed on AWS EC2 for continuous 24/7 operation. Additionally, developed and tested other quantitative strategies (long-short, macro-driven) on stocks, ETFs and commodities, benchmarked Python vs C++ execution speed, and validated trading using Trading212 and Tiger Brokers and their APIs.Analyst’s Disclosure: I/we have no stock, option or similar derivative position in any of the companies mentioned, and no plans to initiate any such positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.Seeking Alpha's Disclosure: Past performance is no guarantee of future results. No recommendation or advice is being given as to whether any investment is suitable for a particular investor. Any views or opinions expressed above may not reflect those of Seeking Alpha as a whole. Seeking Alpha is not a licensed securities dealer, broker or US investment adviser or investment bank. Our analysts are third party authors that include both professional investors and individual investors who may not be licensed or certified by any institute or regulatory body.