Quantum computing is getting ever closer to realizing its potential as a transformative technology for many businesses. This past week a pair of announcements provided a glimpse of how two diverse sectors, steel manufacturing and finance, may be on the cusp of being able to do things with quantum computers that were until now impossible.

Cambridge Quantum Computing, a U.K.-based company that recently agreed to merge with the quantum computing arm of industrial giant Honeywell and spin out as a new publicly traded company, said it had worked with Japan’s Nippon Steel Corporation, one of the world’s leading steel producers, to simulate the behavior of iron crystals in two different configurations.

This chemical simulation is so complex scientists cannot perform it accurately on a conventional computer. In this case, Nippon Steel and Cambridge Quantum Computing used an IBM quantum computer, accessed over the Internet, and specialized algorithms, developed by Cambridge Quantum Computing, to run the simulation.

Scientists involved in the research said the techniques could eventually aid in the creation of new types of steel as well as help answer fundamental questions about what happens in the earth’s solid iron core, where the metal is subjected to extreme heat and pressure.

Also on Tuesday, researchers from Goldman Sachs, IonQ (a company that builds quantum computers), and QC Ware, a startup that specializes in quantum computing algorithms, said they had demonstrated how a fundamental mathematical technique that underpins the pricing of financial risk can be run better and faster on a quantum computer than on conventional ones.

Monte Carlo and iron crystal simulation

Researchers had previously theorized this kind of “quantum advantage” should exist for this mathematical method, called a Monte Carlo simulation. But this is the first time that scientists have demonstrated clear evidence of this improved performance using a specialized quantum algorithm on real quantum computing hardware.

Will Zeng, the head of quantum research at Goldman Sachs, said that the experiment was able to show that with a sufficiently powerful enough quantum computer, there should be a significant performance improvement in pricing financial risk.

He cautioned, however, that current quantum computers are not powerful enough to run the large Monte Carlo simulations the investment bank would need to better price complex derivative contracts or calculate overnight value-at-risk calculations for asset portfolios, two areas in which Goldman hopes quantum computers will eventually offer a major advantage.

Currently, Goldman uses conventional computing techniques to price derivatives, with a calculation taking anywhere from less than a second to a several minutes, depending on the financial instrument’s complexity. But the results may not be as accurate as what can be achieved with a quantum computer. And, as Zeng notes, when dealing with financial products that are highly leveraged, even a small percentage improvement in risk pricing can result in a huge difference in profitability.

In the case of valuing the risk of an entire asset portfolio, the issue is both accuracy and the cost of computing time—the calculations are so complex that it literally takes a supercomputing cluster all night to run them. The more accurate the result of the calculation, potentially the less capital Goldman needs to hold in reserve to guard against sudden drops in the value of its portfolio. A powerful quantum computer might be able to achieve more accurate answers in just minutes.

The small experiment Goldman, IonQ, and QC Ware conducted involved just four quantum processing units, known as qubits, with the ability to carry out about 100 logical operations. Zeng said that Goldman has estimated that outperforming a conventional computer in pricing a single complex derivatives contract would require a quantum computer with about 8,000 qubits and the ability to carry out about 54 million operations.

The research is nonetheless significant because of the vast array of problems that can be addressed using Monte Carlo simulations, from determining the potential effects of price changes to creating more resilient supply chains. The technique is also important for many machine-learning applications. Used in cases where there are many different possible outcomes, a Monte Carlo simulation builds up a picture of the probability distribution of the possible scenarios.

For the simulation of the iron crystals, the Cambridge Quantum Computing and Nippon Steel scientists used a IBM quantum processor with seven qubits. Here, too, the researchers noted that achieving a more accurate simulation of the energy states of the iron crystals would require a much more powerful, and less error prone, quantum device than what currently exists.

Papers about both experiments were published on the non–peer reviewed research repository arxiv.org. You can see the finance research here and the quantum chemistry research paper here.

Quantum processing power

Quantum computers have theoretically exponentially greater processing power than conventional computers because they harness phenomenon from quantum mechanics to help perform calculations. In a conventional computer, information is stored in a binary format, called a bit, that can be either a 0 or 1. In a quantum computer, qubits can exist in a state called superposition, in which they can represent both 0 and 1 simultaneously. In a traditional computer, each bit functions independently. In a quantum computer, a property called entanglement allows qubits to influence one another, in theory speeding up calculation times.

The quantum computers used in the two research papers each utilize a different method to create quantum effects. The IBM machine has qubits made from superconducting materials, such as niobium and aluminum, anchored on a silicon chip, and cooled to extremely low temperatures. The IonQ processor uses powerful lasers to trap ions from a rare earth metal, ytterbium, and uses these to form its qubits.

A major problem with today’s qubits is that they can be held in a quantum state only for a relatively short period of time, ranging from about 120 microseconds (or millionths of a second) for superconducting qubits, to up to 10 minutes for trapped ions. And when the qubits fall out of a quantum state, they produce errors that must then be corrected, either by using more qubits or by using software algorithms.

Both experiments employed some of these “error reduction” algorithms to try to improve the results. And both involved systems in which part of the calculation is run on conventional semiconductor computer chips and some on quantum processors.