TBNQuantum
FirstQFM Says Quantum System Beats Classical Forecasting Benchmarks
Publication: The Quantum Insider
Author: Matt Swayne
Source published: 03/11/2026, 14:01:10 UTC
Briefing created: 03/11/2026, 14:50:21 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 03/11/2026, 14:34:25 UTC.
Briefings
FirstQFM's quantum reservoir computing system, running on Rigetti's Cepheus-1-108Q hardware, outperformed classical financial forecasting benchmarks across 43 anonymized assets and 600 out-of-sample periods. The system achieved 52.02% directional accuracy and 112% higher cumulative gross portfolio profit than the strongest classical benchmark, with an annualized Sharpe ratio of 1.22. FirstQFM released the beta version in July 2026, and CEO Vish Ramakrishnan stated the solution is commercially ready for enterprise customers.
Key points
- FirstQFM's quantum reservoir computing system achieved 52.02% directional accuracy on 43 assets, surpassing classical models.
- The system generated 112% higher cumulative gross portfolio profit than the strongest classical benchmark with an annualized Sharpe ratio of 1.22.
Context
FirstQFM's quantum reservoir computing (QRC) system, running on Rigetti's Cepheus-1-108Q hardware, outperformed classical machine learning and statistical models in financial forecasting. The evaluation used data anonymized by SW7 Group and covered 600 out-of-sample periods across 43 assets. FirstQFM released the beta version in July 2026, with CEO Vish Ramakrishnan confirming the solution is commercially ready for enterprise customers.
Limitations
Evaluation data was anonymized by SW7 Group and covered 600 out-of-sample periods across 43 assets. Model selection was based only on validation data, and the system used chronological training, validation, and test periods. Results are from a single evaluation run and do not account for market conditions beyond the specified period.
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