# Quantum Supremacy Is Unlikely, Scientist Says

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Google announcedthis fall to much fanfare that it had demonstrated “quantum supremacy” — that is, it performed a specific quantum computation far faster than the best classical computers could achieve. IBMpromptly critiqued the claim, saying that its own classical supercomputer could perform the computation atnearly the same speed with far greater fidelityand, therefore, the Google announcement should be taken “with a large dose of skepticism.”

This wasn’t the first time someone cast doubt on quantum computing. Last year, __Michel Dyakonov__, a theoretical physicist at the University of Montpellier in France, offered a slew of technical reasons __why practical quantum supercomputers will never be built__ in an article in IEEE Spectrum, the flagship journal of electrical and computer engineering.

So how can you make sense of what is going on?

As someone who has worked on __quantum computing__ for __many years__, I believe that due to the inevitability of random errors in the hardware, useful quantum computers are unlikely to ever be built.

## What’s a quantum computer?

To understand why, you need to understand how quantum computers work since they’re fundamentally different from classical computers.

A classical computer uses 0s and 1s to store data. These numbers could be voltages on different points in a circuit. But a quantum computer works on quantum bits, also known as qubits. You can picture them as waves that are associated with amplitude and phase.

Qubits have special properties: They can exist in superposition, where they are both 0 and 1 at the same time, and they may be entangled so they share physical properties even though they may be separated by large distances. It’s a behavior that does not exist in the world of classical physics. The __superposition vanishes when the experimenter interacts__ with the quantum state.

Due to superposition, a quantum computer with 100 qubits can represent 2100 solutions simultaneously. For certain problems, this exponential parallelism can be harnessed to create a tremendous speed advantage. Some __code-breaking problems could be solved exponentially faster on a quantum machine__, for example.

There is another, narrower approach to quantum computing called __quantum annealing__, where qubits are used to speed up optimization problems. D-Wave Systems, based in Canada, has built optimization systems that use qubits for this purpose, but critics also claim that these systems __are no better than classical computers__.

Regardless, companies and countries are investing massive amounts of money in quantum computing. China has developed a __new quantum research facility worth US$10 billion__, while the European Union has developed a €1 billion ($1.1 billion) __quantum master plan__. The United States’ __National Quantum Initiative Act__ provides $1.2 billion to promote quantum information science over a five-year period.

Breaking encryption algorithms is a powerful motivating factor for many countries — if they could do it successfully, it would give them an enormous intelligence advantage. But these investments are also promoting fundamental research in physics.

__Many companies are pushing to build quantum computers__, including Intel and Microsoft in addition to Google and IBM. These companies are trying to build hardware that replicates the circuit model of classical computers. However, current experimental systems have less than 100 qubits. To achieve useful computational performance, you probably need machines with hundreds of thousands of qubits.

## Noise and error correction

The mathematics that underpin quantum algorithms is well established, but there are daunting engineering challenges that remain.

For computers to function properly, they must correct all small random errors. In a quantum computer, such errors arise from the non-ideal circuit elements and the interaction of the qubits with the environment around them. For these reasons the qubits can lose coherency in a fraction of a second and, therefore, the computation must be completed in even less time. If random errors — which are inevitable in any physical system — are not corrected, the computer’s results will be worthless.

In classical computers, small noise is corrected by taking advantage of a concept known as thresholding. It works like the rounding of numbers. Thus, in the transmission of integers where it is known that the error is less than 0.5, if what is received is 3.45, the received value can be corrected to 3.

Further errors can be corrected by introducing redundancy. Thus if 0 and 1 are transmitted as 000 and 111, then at most one bit-error during transmission can be corrected easily: A received 001 would be a interpreted as 0, and a received 101 would be interpreted as 1.

Quantum error correction codes are a generalization of the classical ones, but there are crucial differences. For one, the unknown qubits cannot be copied to incorporate redundancy as an error correction technique. Furthermore, errors present within the incoming data before the error-correction coding is introduced cannot be corrected.