Venture capitalist Alexa von Tobel noted that, “In many ways, quantum is today where AI was back in 2015, which is a lot of really big research and science projects and starting to have practical applications rather than just pure research.” This period is characterized by intense speculation on future disruption rather than demonstrated business metrics.

Valuation paradox of hopes versus holdings

The influx of capital—fueled by both private funding and significant government intervention—has driven a dramatic increase in company valuations. The market is pricing these entities not on current performance but on the “perceived probability of them achieving a revolutionary breakthrough in the future.”

Several “pure-play” quantum computing firms that have gone public through Special Purpose Acquisition Companies (SPACs) offer a clear barometer of this speculation. Companies such as IonQ (IONQ), Rigetti Computing (RGTI), D-Wave Quantum (QBTS), and Quantum Computing Inc. (QUBT) command multi-billion-dollar market capitalizations that starkly contrast with their financial fundamentals.

The disconnect is arguably most stark for Quantum Computing Inc. (QUBT), which reported LTM revenue of just $0.4 million against a market cap of $3.5 billion, resulting in an Enterprise Value to Revenue multiple exceeding 7,800x.

These firms are simultaneously sustaining “massive net losses” while prioritizing heavy R&D investment. This investment structure is “indicative of a market driven entirely by speculation on a future paradigm shift.”

The intense investment climate is further underscored by government interest. Shares of companies in the space recently surged after news broke that the Trump administration was in talks to take equity stakes in quantum firms in exchange for federal funding.

Companies discussing the government becoming a shareholder include IonQ, Rigetti Computing, and D-Wave Quantum, with other companies such as Quantum Computing considering similar arrangements. On the day the discussions were updated, D-Wave was up 20%, while IonQ, Rigetti, and Quantum Computing were all up over 10%.

Parallels to the pre-breakthrough AI era

The history of AI is marked by alternating periods of intense optimism (AI summers) and disillusionment (AI winters). Early AI winters were caused by grand promises that failed to materialize and the severe limitations of available computing power.

The current quantum boom is strongly reminiscent of the speculative fervor that surrounded the expert systems market in the 1980s, a period that collapsed when hype “outran the practical, economic value of the technology.”

QC’s current stage is often compared to “artificial intelligence in 2010, when neural networks were on the cusp of becoming useful but before advances like protein structure predictor AlphaFold or generative AI chatbots.”

AI’s eventual “spring” was unlocked by a convergence of massive datasets, algorithmic breakthroughs (deep learning), the transformer, and the necessary computational horsepower provided by GPUs.

QC now faces a similar transition, but its core obstacle, known as quantum decoherence, is a “fundamental aspect of quantum physics.” This is not merely an engineering challenge like those AI faced, but a “profound challenge in materials science, experimental physics, and control engineering.”

Demonstrable advances in capability

Despite the financial fragility, the quantum industry can point to significant, albeit nascent, technological advances that justify the sense of transition. Like early neural networks, quantum computing is already “proving useful as a platform for research and experimentation”.

Google recently published results showing that a quantum computer can run 13,000 times faster than a classical supercomputer. More recently, Google’s quantum processor ran a benchmark algorithm in minutes that would take “today’s fastest supercomputers 10 septillion years.” Furthermore, the firm successfully demonstrated “below threshold” error correction, which researchers consider a long-standing challenge in the field.

These breakthroughs are beginning to find limited, high-value applications. HSBC claimed that quantum computing tools had “made its trading more efficient in tests.” The lender tested a tool developed by IBM on European corporate bond market data and found the technology was “34% better than traditional means in predicting how likely an order was to be filled.” Philip Intallura, HSBC group head of quantum technologies, called this their “most tangible demonstration of just how close we are from extracting value from quantum computing.”

Experts note that this level of progress has already shortened the perceived timeline for a “cryptographically relevant quantum computer” (CRQC), which can crack modern encryption. This year alone, there was a “20-fold decrease in the estimated size of the quantum computer needed to run Shor’s algorithm and crack the widely used RSA-2048 encryption,” according to one analysis.

“The real inflection point will come when AI and quantum systems intersect – AI to interpret complexity, quantum to compute it,” Dr. Kristin Milchanowski, Chief AI Officer, BMO Financial Group, tells EETimes. “That convergence will redefine efficiency, capital allocation, and the very architecture of financial intelligence.”

Geopolitical buffer against winter

While the risk of a “quantum winter” or market correction exists if noisy, intermediate-scale quantum (NISQ) machines fail to deliver practical advantage, a total collapse akin to those experienced by AI decades ago appears less likely.

The high valuations of public firms like IonQ, Rigetti, D-Wave, and Quantum Computing Inc. are a clear echo of the speculative investment that defined AI’s boom-and-bust cycles.

critical difference between QC today and early AI is the “geopolitical imperative.” The existential threat posed by quantum computers—the ability to break modern cryptography—transforms QC development from a purely commercial race into a strategic imperative.

This dynamic has led to massive and persistent public funding, serving as a financial backstop. The federal funding currently under discussion with private firms, which could include warrants, royalties, or equity stakes, comes from the Chips Research and Development Office.

The strong, state-level interest is less sensitive to short-term commercial returns and can serve as a buffer against the kind of total funding collapse that characterized AI’s winters.

By: DocMemory
Copyright © 2023 CST, Inc. All Rights Reserved