Verifying Each Computing Path of a Quantum Calculation for Correctness

researchers from MIT, Google, and elsewhere have designed a system that can verify when quantum chips have accurately performed complex computations that classical computers can’t. They trace each computing path for correct behavior. This modular verification reduces the complexity of validation and speeds up verification. This will speed up validated quantum chip design and speed up checking on experiments.

Full-scale error-corrected quantum computers will require millions of qubits. In the past few years, researchers have started developing “Noisy Intermediate Scale Quantum” (NISQ) chips, which contain around 50 to 100 qubits. This is enough to demonstrate “quantum advantage,” meaning the NISQ chip can solve certain algorithms that are beyond classical computers. Verifying that the chips performed operations as expected, however, can be very inefficient. The chip’s outputs can look entirely random, so it takes a long time to simulate steps to determine if everything went according to plan.

Above- Researchers from MIT, Google, and elsewhere have designed a novel method for verifying when quantum processors have accurately performed complex computations that classical computers can’t. They validate their method on a custom system (pictured) that’s able to capture how accurately a photonic chip (“PNP”) computed a notoriously difficult quantum problem. Image: Mihika Prabhu

They trace an output quantum state generated by the quantum circuit back to a known input state. This reveals which circuit operations were performed on the input to produce the output. Those operations should always match what researchers programmed. If not, the researchers can use the information to pinpoint where things went wrong on the chip.

In experiments, they successfully ran a popular computational task used to demonstrate quantum advantage, called “boson sampling,” which is usually performed on photonic chips. In this exercise, phase shifters and other optical components will manipulate and convert a set of input photons into a different quantum superposition of output photons. Ultimately, the task is to calculate the probability that a certain input state will match a certain output state. That will essentially be a sample from some probability distribution.

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