Well worth the read from the source, below. Because Quantum is Coming. Qubit
Goldman Sachs, IBM researchers estimate quantum advantage for derivative pricing
+ The financial services industry is full of potential applications for quantum computing, including optimization, simulation and machine learning. But it’s not that easy to determine which applications are most likely to benefit from quantum advantage, and exactly how powerful quantum computers must be to run those applications significantly better than classical systems can.
+ That’s what we are trying to address. In a new preprint now on arXiv, “A Threshold for Quantum Advantage in Derivative Pricing”, our quantum research teams at IBM and Goldman Sachs provide the first detailed estimate of the quantum computing resources needed to achieve quantum advantage for derivative pricing – one of the most ubiquitous calculations in finance.
Those resource requirements are out of reach of today’s systems, but we aim to provide a roadmap to further improve algorithms, circuit optimization, error correction and planned hardware architectures.
Our main goal was to show in as much concrete, quantifiable detail as possible what is needed for quantum advantage in derivative pricing to be both possible and meaningful, and highlight where the challenges remain in achieving quantum advantage. This sort of analysis is important because it identifies the specific bottlenecks we know of today, making it more likely that additional research will determine how to unplug those bottlenecks.
+ We describe the challenges in previous quantum approaches to this problem, and introduce a new method for overcoming those obstacles. The new approach – called the re-parameterization method – combines pre-trained quantum algorithms with approaches from fault-tolerant quantum computing to dramatically cut the estimated resource requirements for pricing financial derivatives using quantum computers.
+ Our resource estimates give a target performance threshold for quantum computers able to demonstrate advantage in derivative pricing. The benchmark use cases we examined need 7.5k logical qubits and a T-depth of 46 million (referring to the number of gates, or operations a qubit can perform before decoherence). We also estimate that quantum advantage in this scenario would need T-gates to run at 10Mhz or faster, assuming a target of 1 second for pricing certain types of derivatives.
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