Shallow entangled circuits for quantum time series prediction on IBM devices
Mostafizur Rahaman Laskar, Richa Goel · 2025
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Description
Forecasting time series usually means large neural networks. It can also mean models that need many data points. Mostafizur Rahaman Laskar and Richa Goel propose Quantum Time Series instead. Their framework loads normalized sequences into single-qubit rotations. Shallow entanglement layers link the qubits, so the circuit can remember recent steps. Among several loading schemes, a phase-based sparse pattern scaled best. Its depth grows only linearly as qubits are added.
On synthetic and geophysical data, the shallow circuits reproduced complex patterns from limited data. Runs on IBM Heron and Eagle processors held up to 100 qubits. The authors read this as a scalable route for near-term quantum forecasting.
Catalog record
| CREATOR | Mostafizur Rahaman Laskar, Richa Goel |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 41 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |