J.P. Morgan announced that a team of researchers from the bank, Quantinuum, Argonne National Laboratory, Oak Ridge National Laboratory, and the University of Texas at Austin demonstrated a potential application of a quantum computer by realizing Certified Quantum Randomness.
Randomness has many industrial uses, from solving complex mathematical problems to essential applications in areas such as cryptography, fairness and privacy. The group conducted the first successful demonstration of a novel quantum computing protocol to generate Certified Randomness. The researchers leveraged a task originally designed to demonstrate quantum advantage, called Random Circuit Sampling (RCS), to perform a certified-randomness-expansion protocol, which outputs more randomness than it takes as input. This task is unachievable by classical computation.
The 56-qubit Quantinuum System Model H2 trapped-ion quantum computer, with its high-fidelity and all-to-all qubit connectivity, was used for this study, demonstrating that a quantum computer can now achieve computational power beyond that offered by the most powerful classical supercomputers. Accessing H2 remotely over the internet, the team generated certifiably random bits.
“This work marks a major milestone in quantum computing, demonstrating a solution to a real-world challenge using a quantum computer beyond the capabilities of classical supercomputers today,” said Marco Pistoia, head of Global Technology Applied Research and Distinguished Engineer at J.P. Morgan, said in a statement. “This development of Certified Randomness not only shows advancements in quantum hardware, but will be vital to further research, statistical sampling, numerical simulations and cryptography.”
“Today, we celebrate a pivotal milestone that brings quantum computing firmly into the realm of practical, real-world applications,” said Rajeeb Hazra, president and CEO of Quantinuum. “Our application of Certified Quantum Randomness not only demonstrates the unmatched performance of our trapped-ion technology but sets a new standard for delivering robust quantum security and enabling advanced simulations across industries like finance, manufacturing, and beyond. At Quantinuum, we are driving pioneering breakthroughs to redefine industries and unlock the full potential of quantum computing.”
In addition, Classiq Technologies, a quantum computing software company, announced that it achieved significant compression of quantum circuits in implementing quantum algorithms for Monte Carlo simulations as part of a project conducted by Sumitomo Corporation. Sumitomo used Classiq’s quantum platform and quantum algorithms provided by Mizuho–DL Financial Technology (Mizuho-DL FT).
In a joint statement, the companies wrote that the project achieved 95% quantum circuit compression to drive finance innovation and demonstrated efficiency improvements in implementing quantum algorithms for credit portfolio risk management calculations.
In the financial industry, Monte Carlo simulations are widely used for derivative pricing and asset risk evaluation. However, these simulations require the generation of vast scenarios using random numbers, leading to high computational costs and long processing times. Quantum computing has the potential to efficiently handle large-scale probabilistic simulations compared to conventional methods, offering significant benefits in speeding up simulations and improving risk assessment accuracy.
This project explored the use of Classiq’s technology to generate more efficient quantum circuits for a novel quantum Monte Carlo simulation algorithm incorporating pseudo-random numbers proposed by Mizuho-DL FT. The project aimed to evaluate the feasibility of implementing quantum algorithms in financial applications in the future.
Significant compression was achieved in quantum circuits for two types of quantum Monte Carlo simulations designed for credit portfolio risk management. Both simulations improved computational efficiency on quantum hardware and enhanced scalability for larger financial problems.
By enabling high-precision calculations with fewer resources, the study demonstrated that large-scale probabilistic simulations for financial risk management may be feasible. Additionally, the reduced circuit depth improved fault tolerance, minimizing the impact of noise.