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The pilot pairs a fixed quantum reservoir on a 108-qubit Rigetti processor with a classical linear readout, and reports better P&L and Sharpe ratios than classical ML and time-series baselines on a 43-asset backtest. Those gains come with larger drawdowns, no transaction costs, and a vendor-linked pilot without general quantum advantage, so it is an early signal rather than proof. Readers should watch for independent replication and results that include costs and drawdown control.
FirstQFM Demonstrates Quantum Reservoir Computing on Rigetti’s 108-Qubit Processor to Outperform Classical Financial Benchmarks
FirstQFM, in collaboration with SW7 Group and Rigetti Computing, has successfully demonstrated a financial forecasting pilot using quantum reservoir computing (QRC) on Rigetti’s 108-qubit processor. This QRC system outperformed classical machine learning models and time-series foundation models in financial benchmarks, achieving significantly higher cumulative gross profit and loss and Sharpe ratios. The study highlights QRC as a promising and latency-efficient approach for deploying NISQ processors in quantitative finance, despite not claiming general quantum advantage. The post FirstQFM Demonstrates Quantum Reservoir Computing on Rigetti’s 108-Qubit Processor to Outperform Classical Financial Benchmarks appeared first on Quantum Computing Report.
Stockholm-based quantum software startup FirstQFM, in collaboration with London quantitative research firm SW7 Group and Rigetti Computing, has demonstrated a financial forecasting pilot using quantum reservoir computing (QRC) executed on Rigetti’s 108-qubit Cepheus-1-108Q superconducting processor. Across…
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