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FirstQFM reported that a quantum reservoir computing system running on Rigetti hardware outperformed the classical models it tested on next-day forecasts for 43 anonymized assets. The company reported 52.02% directional accuracy and 112% higher cumulative gross portfolio profit than the strongest classical benchmark by profit, but the findings are company-reported and do not establish that quantum forecasting generally outperforms classical methods.
FirstQFM said its quantum reservoir computing system, using features generated on Rigetti Computing hardware, outperformed the classical models included in a financial forecasting evaluation covering 43 anonymized assets and 600 out-of-sample periods. The company reported 52.02% directional accuracy and 112% higher cumulative gross portfolio profit than the strongest classical benchmark by profit; the results are a company-reported evaluation, not evidence that quantum systems broadly beat classical forecasting.
The evaluation tested next-day forecasts using data anonymized by SW7 Group. FirstQFM reported that its system achieved 51.76% macro-F1, as well as the highest directional accuracy among the models evaluated. The comparisons included statistical and machine-learning models and time-series foundation models, including Chronos-2 from Amazon, Granite TTM-R2 from IBM and Toto 2.0 from Datadog.
In an equally weighted portfolio spanning all 43 assets, FirstQFM reported 112% higher cumulative gross profit and loss than the classical benchmark with the strongest gross P&L. Its annualized Sharpe ratio was 1.22, compared with 1.02 for the classical benchmark with the highest Sharpe ratio. The company also said its system recorded directional accuracy above 50% for 36 of 43 assets and positive gross P&L for 41.
FirstQFM separately reported results for a five-asset portfolio selected using a historical diversification method. It said that portfolio had higher directional accuracy, macro-F1 and cumulative gross P&L than the full 43-asset portfolio, with an annualized Sharpe ratio of 1.73. The five-asset result is distinct from the full-portfolio comparison and does not by itself establish that the same performance would hold in live trading.
What the Trading Metrics Show
The results matter because they offer a concrete test of whether quantum computing can contribute to a financial forecasting task, rather than a laboratory-only demonstration. If the reported performance can be reproduced under independent testing and after real-world costs, it could be relevant to asset managers evaluating new forecasting tools. The reported metrics, however, describe this particular dataset, model comparison and evaluation design.
Reported gross portfolio profit is not the same as net returns available to investors. The source material does not give transaction costs, market impact, fees or other live-trading constraints. Nor does a Sharpe ratio from an evaluation period guarantee future risk-adjusted performance. The announcement is a benchmark result, not evidence that customers have obtained investment gains or that the system is commercially validated in deployment.
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How FirstQFM Ran the Test
SW7 Group anonymized the assets and forecasting task before sharing the data with FirstQFM. The evaluation used chronological training, validation and test periods, and FirstQFM said model selection was based only on validation data. It also said the quantum features were generated using Rigetti’s Cepheus-1-108Q system.
FirstQFM described its approach as a proprietary, foundation-model-based quantum reservoir computing system. The company released a beta version in July 2026. The detailed methodology and results are available in a technical blog linked by FirstQFM; the announcement itself does not amount to independent replication or peer-reviewed confirmation.
“Empirical work such as FirstQFM’s helps us probe the capabilities and limits of today’s quantum computers, while helping hardware teams understand which improvements matter most.”
— Subodh Kulkarni, Rigetti Computing chief executive
Limits of the Reported Evidence
The announcement and linked technical blog are FirstQFM’s account of its own evaluation. The source material does not identify an independent auditor, a peer-reviewed publication or an external replication. It also does not provide enough detail here to assess every model’s configuration, the exact test-period dates, asset composition, or how sensitive results were to changes in assumptions.
It remains unclear whether the reported advantage would persist with transaction costs, slippage and live-market execution, or across other assets and time periods. The 600 out-of-sample periods are reported by the company, but their precise duration and relationship to each asset are not specified in the supplied material. The findings should therefore be read as results for the reported test, not as a general comparison of quantum and classical forecasting.
Independent Checks and Live Testing
FirstQFM has published a technical blog with its methodology and results, which may allow other researchers and practitioners to examine the evaluation. The next evidence that would clarify the claims would include independent replication, fuller disclosure of test-period details and comparisons that include trading costs and execution assumptions.
The source material does not announce a date for external validation, a live deployment or another benchmark. Until such evidence is available, the reported figures remain an announced test result. FirstQFM’s beta product and its proposed use across sectors do not establish that the same performance has been achieved outside this evaluation.
Key Questions
What did FirstQFM report?
It reported that its quantum reservoir computing system outperformed the classical models tested on next-day forecasts for 43 anonymized assets and 600 out-of-sample periods, including 52.02% directional accuracy.
What does the 112% figure compare?
FirstQFM said the equally weighted 43-asset portfolio produced 112% higher cumulative gross P&L than the classical benchmark with the strongest gross P&L. It is a reported gross-profit comparison, not a net or guaranteed investor return.
Was the result independently verified?
The supplied material describes an announcement by FirstQFM and its project partners. It does not report independent verification, peer review or external replication.
Which quantum hardware was used?
FirstQFM said its quantum features were generated on Rigetti Computing’s Cepheus-1-108Q system.
Does the evaluation prove quantum forecasting is better?
No. It reports an advantage in one specific evaluation against the models included in that test. Whether the results generalize to other datasets or persist in live trading remains unclear.
Source: thequantuminsider.com
