{"id":846829,"date":"2026-09-13T22:36:14","date_gmt":"2026-09-13T22:36:14","guid":{"rendered":"https:\/\/www.newsbeep.com\/us\/846829\/"},"modified":"2026-09-13T22:36:14","modified_gmt":"2026-09-13T22:36:14","slug":"not-ionq-not-rigetti-computing-this-quantum-computing-stock-could-be-septembers-biggest-winner","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/us\/846829\/","title":{"rendered":"Not IonQ, Not Rigetti Computing. This Quantum Computing Stock Could Be September’s Biggest Winner."},"content":{"rendered":"

Quantum computing sounds like science fiction to most people, and in some ways it is. Instead of flipping binary bits on or off, quantum machines use qubits<\/a> that can sit in mixed states. In theory, this allows quantum algorithms to process certain problems that would take a classical supercomputer longer than the age of the universe to solve.<\/p>\n

Spoiler alert: We are nowhere close to achieving this feat at scale. For now, quantum computing systems are expensive and still more of a research project than a commercial product. That hasn’t stopped some investors from treating a handful of quantum computing stocks as potential lottery tickets, though. IonQ (IONQ<\/a> -0.24%) and Rigetti Computing (RGTI<\/a> +0.73%) are two of the cleanest public bets as they actually build quantum computing systems. Nvidia (NVDA<\/a> -0.03%) sits in a different spot.<\/p>\n

Although Nvidia designs GPUs and CPUs that run some of the largest artificial intelligence (AI) training clusters<\/a> in the world, it is also quietly parlaying existing infrastructure to sit inside of hybrid classical-quantum environments. This approach makes Nvidia a more subtle quantum name compared to pure plays<\/a> like IonQ or Rigetti. In my eyes, this positioning is precisely why the stock could be the one that actually wins as September unfolds.<\/p>\n

\"Quantum<\/p>\n

Image source: Getty Images.<\/p>\n

What the latest numbers from IonQ and Rigetti tell investors<\/p>\n

IonQ recently posted its best quarter yet. Revenue during the second quarter reached $80 million, up 287% year over year<\/a>. The company also raised its 2026 outlook to between $280 and $290 million. However, after closing the SkyWater foundry<\/a> purchase at the end of July, management is now talking up to $460 million of combined revenue for the full year.<\/p>\n

The problem with IonQ is everything underneath its top-line. Operating losses remain enormous as research, sales, and admin costs dwarf the company’s revenue. In reality, IonQ is growing fast from a relatively small base while buying itself a factory. All told, the company is still years and tens of billions of dollars away from the kind of scale that pays infrastructure bills without the need for constant capital raises or narrative-driven support.<\/p>\n

\"IONQ<\/a><\/p>\n

IONQ Revenue (Quarterly)<\/a> data by YCharts<\/a><\/p>\n

Rigetti is even smaller than IonQ. During the second quarter, the company booked $5.1 million in revenue, up 183% year over year. On the positive side, Rigetti’s gross margins are improving, and operating losses aren’t widening by an amount that would otherwise scare off investors. Moreover, the company was also awarded a grant of as much as $100 million from the CHIPS and Science Act<\/a>. In reality, though, $5 million of sales against $28 million of operating losses is negligible progress. Rigetti is not at the scale where its business can drive the stock on fundamentals alone. <\/p>\n

Both IonQ and Rigetti remain stocks whose prices are mainly driven by headlines around partnerships, a fidelity milestone, or a government check. These storylines can send the stocks flying. But the absence of these very same types of headlines can also send them the other way.<\/p>\n

Nvidia is no longer just a GPU company<\/p>\n

Nvidia used to be easy to describe: It sold graphics processing units<\/a> (GPUs) so big tech could build generative models. This description is now incomplete because Nvidia sits across the entire AI value chain. The company designs the processors, the interconnects, the networking, and the software stack that developers use for training AI and inference runs. Moreover, Nvidia’s customers are no longer just a handful of cloud infrastructure giants. A cohort of businesses across AI clouds, industrial, and sovereign enterprises (ACIE) is actually growing even faster than the traditional hyperscalers<\/a>.<\/p>\n

This same approach to building a full-spectrum platform is now showing up in Nvidia’s quantum ambitions. The company does not actually build its own quantum processors. Instead, it builds the classical hardware and software side that quantum computing machines will need if the technology ever becomes useful at scale.<\/p>\n

In other words, Nvidia can provide the GPUs that are used to simulate quantum optimization problems, the low-latency links that allow quantum chips to talk to a supercomputer, and the software layer\u00a0that helps researchers treat a quantum device and a GPU cluster as one unified system. <\/p>\n

Nvidia is quietly selling the compute and the plumbing that connects quantum environments to classical computing. Even when a lab or a government agency experiments with hardware from IonQ or Rigetti, there is a good chance Nvidia silicon and software are also in the loop.<\/p>\n

\"Nvidia<\/p>\n

Today’s Change<\/p>\n

(-0.03%) $-0.07<\/p>\n

Current Price<\/p>\n

$218.29<\/p>\n

Key Data Points<\/p>\n

Market Cap<\/p>\n

$5.3TMarket cap calculated using publicly traded shares outstanding only. Does not include unlisted, private, or dual-class non-traded shares. Implied market cap may vary.<\/p>\n

Day’s Range<\/p>\n

$218.15 – $222.00<\/p>\n

52wk Range<\/p>\n

$164.27 – $236.54<\/p>\n

Volume<\/p>\n

89.1M<\/p>\n

Avg Vol<\/p>\n

131.8M<\/p>\n

Gross Margin<\/p>\n

74.67%<\/p>\n

Dividend Yield<\/p>\n

0.24%<\/p>\n

Why Nvidia stock may make a move in September<\/p>\n

Throughout earnings season, investors learned that cloud hyperscalers and their AI-native cousins are spending at an almost cartoonish pace. Combined capital expenditures (capex) among the largest hyperscalers are running close to\u00a0$800 billion this year<\/a> and they anticipate spending to rise to $1.3 trillion in 2027.<\/p>\n

A huge slice of that spending will go to Nvidia and the companies that feed it memory, packaging, and power. This is precisely why Nvidia just projected roughly 70% revenue growth<\/a> next fiscal year and said its customer demand is actually running even hotter than that.<\/p>\n

The limiting factor is supply, especially high bandwidth memory (HBM). In turn, Nvidia is committing $279 billion in long-term supply agreements<\/a> to lock in enough memory and storage from the three big producers — Micron Technology, SK Hynix, and Samsung. This is a data center and chip story that can be measured every quarter.<\/p>\n

By comparison, quantum computing is still a rounding error in these same AI infrastructure budgets. While the government and some research labs continue to write checks, AI hyperscalers are mostly watching, running experiments in-house, and waiting. That leaves IonQ and Rigetti primarily as narrative stocks, gaining when the story is hot and stalling when it gets quiet.<\/p>\n

On the other hand, Nvidia already has the secular tailwinds<\/a> of AI infrastructure spending that are happening right now. If big tech chooses to explore quantum-AI hybrid applications, Nvidia almost certainly plays a part in those projects.<\/p>\n

This combination of visible, large-scale demand plus a credible seat at the quantum table is why I think Nvidia stock has a much clearer near-term catalyst than pure plays like IonQ or Rigetti, both of which still have to prove they can turn scientific progress into predictable revenue and profitable unit economics.<\/p>\n","protected":false},"excerpt":{"rendered":"Quantum computing sounds like science fiction to most people, and in some ways it is. Instead of flipping…\n","protected":false},"author":2,"featured_media":846830,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[46],"tags":[191,74],"class_list":["post-846829","post","type-post","status-publish","format-standard","has-post-thumbnail","category-computing","tag-computing","tag-technology"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/846829","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/comments?post=846829"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/846829\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media\/846830"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media?parent=846829"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/categories?post=846829"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/tags?post=846829"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}