Goldman’s tests found certain finance problems need far more logical qubits and much longer runtimes than near‑term quantum machines can deliver. JPMorgan has taken a different tack, reorganising its applied research team and hiring Rob Otter from State Street to lead quantum and other advanced‑tech projects — illustrating two routes banks are using as they probe quantum's potential.

Different bets on an early technology

Goldman Sachs and JPMorgan are both investing in quantum computing but taking different routes. Goldman concentrated a small team of specialists and worked with cloud partners to push on a few narrowly defined finance problems. JPMorgan has been building a broader applied research group that mixes quantum work with blockchain, computer vision and networking.

Goldman’s experiments produced stark results: the bank’s researchers concluded that the portfolio‑optimisation or risk‑calculation task they tested would require a very large number of logical qubits and algorithm runtimes far beyond current or near‑term machines. Those findings prompted the team to test alternative approaches.

What Goldman found — and why it matters

Goldman paired prototypes run with a cloud partner and then measured the resources those prototypes would need at scale. The assessment showed hardware and error‑correction requirements were orders of magnitude beyond present systems, and some prototyped algorithms could not be scaled without extremely long runtimes.

As a result, researchers shifted focus toward alternate algorithms, hybrid methods that mix classical and quantum steps, and use cases that might demand fewer qubits or less error correction. The change highlights how practical constraints — qubit counts, error rates and algorithm runtimes — are shaping research choices inside banks.

JPMorgan’s reshuffle and what it signals

JPMorgan recently replaced Marco Pistoia, who led the applied research group since 2020, and hired Rob Otter from State Street to run the team. Otter had run digital technology and quantum efforts at State Street.

The move keeps quantum inside a broader applied‑research remit rather than isolating it as a standalone lab. That suggests JPMorgan is emphasising cross‑cutting work where quantum ideas intersect with blockchain, networking and machine learning, rather than a single deep push on one quantum use case.

How the two paths differ technically

In short:

  • Goldman: Tests narrow finance problems against current quantum approaches, then measures the hardware, qubit and runtime needs — exposing a large gap between prototypes and production‑ready systems.
  • JPMorgan: Incorporates quantum into a wider set of tools and research areas, prioritising integration with other advanced technologies over a single concentrated hardware or algorithm effort.

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Goldman’s experiments pushed researchers toward hybrid algorithms and use cases that demand fewer qubits; JPMorgan’s hire of Rob Otter signals it will fold quantum into a broader applied‑research remit rather than isolate it as a standalone lab.

This article was created with AI assistance.