What was shown
The authors work out how a quantum computer could price a financial derivative, a contract whose payout depends on market prices. Their method writes the payout formula straight into the quantum state, like pressing a stencil onto a sheet instead of measuring and cutting every line by hand. For contracts of practical interest this cuts the number of T-gates, the costly steps of a quantum circuit, by about 16 times and the number of logical qubits by about 4 times. Nothing was run on a machine. It is a resource count done as a calculation.
“With the assumption that this derivative contract can be priced classically in one second [8], a quantum processor would need to execute T-gates at a rate of 45 MHz to match the classical performance if QSP with Usqrt is used, compared to 207 MHz with the arithmetic method and 430 MHz using QSP and Usin”
— Section 6, Resource Estimates for Quantum Advantage, in: Derivative Pricing using Quantum Signal Processing1
- How it was done: a calculation on paper, not run on any machine
- Qubits: The count is 4.7 k logical qubits, meaning qubits protected against errors, not the raw physical devices.
- Error correction: No error-correction scheme is specified; the counts exceed what near term devices are expected to manage.
- Compared against classical computing: no comparison stated in the paper
Who it affects
Finance, specifically pricing derivative contracts, where a bank must value a contract whose payout depends on market prices. For someone there today, nothing changes: this is a resource estimate for a future machine, and no quantum computer has priced a contract with this method.
Finance: McKinsey & Company estimates $400B to $600B by 2035 — value at stake for the industry, not vendor revenue (Quantum Technology Monitor, April 2026, p. 24).
This figure is an estimate, not a measurement: McKinsey & Company is a consultancy with its own quantum advisory business. It covers a whole use case, not the result of this paper.
What is still missing
The paper names what is needed, 4.7 k logical qubits and 10⁹ T-gates at 45 MHz, but no machine has been shown to deliver it. The classical side is only an assumption that the contract can be priced in one second, not a benchmark against a named classical method. The method covers the payoff component only, and only functions of a single variable.
Against published roadmaps: the task needs 4,700 logical qubits and 1 billion gates. Each of these has been announced, but by different sources:
- IonQ2 (a vendor describing its own planned product): 8,000 logical qubits for 2029.
- Quantinuum3 (a vendor describing its own planned product, in a securities filing): 10 billion gates for 2029.
No one on file has announced a single machine that is stated to do all of it, so this brief gives no year. An announcement is not a delivered machine. Roadmaps count gates of every kind, so a machine of that size is necessary for this task but may not be sufficient.
Who is working on it
The paper (July 2023) lists its authors' affiliations as: Goldman Sachs, New York, NY. That is who signed this work — it is not a ranking of who is ahead.
Strengthens Quantum Signal Processing as a replacement for quantum arithmetic when derivative payoffs are encoded into quantum amplitudes. The result is not tied to any one kind of quantum hardware.
How this brief was made
This brief was written by software, not by a person: a language model read the full text of the paper, and code then checked every quotation, number and name in this brief against that text before it was published. The rules are set out under Method. Responsible for the rules and for this page: Georg Singer. The paper is peer-reviewed, published in Quantum 8, 1322 (2024). Georg Singer holds no shares in any company or institution named in this brief (as of October 2026).
Sources
- arxiv.org Derivative Pricing using Quantum Signal Processing
- ionq.com IonQ
- sec.gov Quantinuum