How to check whether the quantum chip is computing correctly

A new method determines whether circuits are accurately executing complex operations that classical computers can’t tackle.

How to check whether the quantum chip is computing correctly, In a step toward practical quantum computing, 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.

Quantum chips perform computations using quantum bits, called “qubits,” that can represent the two states corresponding to classic binary bits — a 0 or 1 — or a “quantum superposition” of both states simultaneously.

Full-scale quantum computers will require millions of qubits, which isn’t yet feasible. In the past few years, researchers have started developing “Noisy Intermediate Scale Quantum” (NISQ) chips, which contain around 50 to 100 qubits. That’s just enough to demonstrate “quantum advantage,” meaning the NISQ chip can solve certain algorithms that are intractable for 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.

Our technique provides an important tool for verifying a broad class of quantum systems. Because if I invest billions of dollars to build a quantum chip, it sure better do something interesting.

The researchers’ work essentially traces an output quantum state generated by the quantum circuit back to a known input state. Doing so 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.

At the core of the new protocol, called “Variational Quantum Unsampling,” lies a “divide and conquer” approach, Carolan says, that breaks the output quantum state into chunks.

This allows us to break the problem up to tackle it in a more efficient way,” Carolan says. For this, the researchers took inspiration from neural networks — which solve problems through many layers of computation — to build a novel “quantum neural network” (QNN), where each layer represents a set of quantum operations.

To run the QNN, they used traditional silicon fabrication techniques to build a 2-by-5-millimeter NISQ chip with more than 170 control parameters tunable circuit components that make manipulating the photon path easier. Pairs of photons are generated at specific wavelengths from an external component and injected into the chip. The photons travel through the chip’s phase shifters which change the path of the photons interfering with each other.

This produces a random quantum output state which represents what would happen during computation. The output is measured by an array of external photodetector sensors.

That output is sent to the QNN. The first layer uses complex optimization techniques to dig through the noisy output to pinpoint the signature of a single photon among all those scrambled together. Then, it unscrambles that single photon from the group to identify what circuit operations return it to its known input state. Those operations should match exactly the circuit’s specific design for the task.

All subsequent layers do the same computation removing from the equation any previously unscrambled photons until all photons are unscrambled.

In experiments, the team 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.

But it’s nearly impossible for classical computers to compute those samples, due to the unpredictable behavior of photons. It’s been theorized that NISQ chips can compute them fairly quickly. Until now, however, there’s been no way to verify that quickly and easily, because of the complexity involved with the NISQ operations and the task itself.

In experiments, the researchers were able to “unsample” two photons that had run through the boson sampling problem on their custom NISQ chip and in a fraction of time it would take traditional verification approaches.

While the method was designed for quantum verification purposes, it could also help capture useful physical properties, Carolan says. For instance, certain molecules when excited will vibrate, then emit photons based on these vibrations.

By injecting these photons into a photonic chip, Carolan says, the unscrambling technique could be used to discover information about the quantum dynamics of those molecules to aid in bioengineering molecular design.

It could also be used to unscramble photons carrying quantum information that have accumulated noise by passing through turbulent spaces or materials.


The study was published in Massachusetts Institute of Technology

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