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# Quantum Supremacy Is Unlikely, Scientist Says

By Subhash Kak 12 hours ago Tech

(Image: © Shutterstock)

*This article was originally published at **The Conversation.** The publication contributed the article to Space.com’s **Expert Voices: Op-Ed & Insights**.*

*Subhash Kak**, Regents Professor of Electrical and Computer Engineering, Oklahoma State University*

Google announced this 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. IBM promptly critiqued the claim, saying that its own classical supercomputer could perform the computation at nearly the same speed with far greater fidelity and, 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.RECOMMENDED VIDEOS FOR YOU…

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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.

**Related: ****18 Times Quantum Physics Blew Our Minds**

## 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.Google’s Sycamore chip is kept cool inside their quantum cryostat. (Image credit: Eric Lucero/Google, Inc.)

## 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.

**Related: ****The 11 Most Beautiful Mathematical Equations**

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.

## Quantum cryptography

While the problem of noise is a serious challenge in the implementation of quantum computers, it isn’t so in quantum cryptography, where people are dealing with single qubits, for single qubits can remain isolated from the environment for significant amount of time. Using quantum cryptography, two users can exchange the very large numbers known as keys, which secure data, without anyone able to break the key exchange system. Such key exchange could help secure communications between satellites and naval ships. But the actual encryption algorithm used after the key is exchanged remains classical, and therefore the encryption is theoretically no stronger than classical methods.

Quantum cryptography is being commercially used in a limited sense for high-value banking transactions. But because the two parties must be authenticated using classical protocols, and since a chain is only as strong as its weakest link, it’s not that different from existing systems. Banks are still using a classical-based authentication process, which itself could be used to exchange keys without loss of overall security.

Quantum cryptography technology must shift its focus to quantum transmission of information if it’s going to become significantly more secure than existing cryptography techniques.

## Commercial-scale quantum computing challenges

While quantum cryptography holds some promise if the problems of quantum transmission can be solved, I doubt the same holds true for generalized quantum computing. Error-correction, which is fundamental to a multi-purpose computer, is such a significant challenge in quantum computers that I don’t believe they’ll ever be built at a commercial scale.

*This article is republished from **The Conversation** under a Creative Commons license. Read the **original article.*

*Follow all of the Expert Voices issues and debates — and become part of the discussion — on **Facebook** and **Twitter**. The views expressed are those of the author and do not necessarily reflect the views of the publisher. *

[*You’re smart and curious about the world. So are The Conversation’s authors and editors.* You can get our highlights each weekend.]

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# Quantum supremacy

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This article may be too technical for most readers to understand. Please help improve it to make it understandable to non-experts, without removing the technical details. (October 2019) (Learn how and when to remove this template message) |

In quantum computing, **quantum supremacy** is the goal of demonstrating that a programmable quantum device can solve a problem that classical computers practically cannot (irrespective of the usefulness of the problem).^{[1]}^{[2]} The term **quantum eclipse** is also suggested by Kevin Tian and Ewin Tang^{[3]}. By comparison, the weaker **quantum advantage** is the demonstration that a quantum device can solve a problem merely faster than classical computers. Conceptually, this goal involves both the engineering task of building a powerful quantum computer and the computational-complexity-theoretic task of finding a problem that can be solved with current technology and has a believed superpolynomial speedup over the best known or possible classical algorithm for that task.^{[4]}^{[5]} The term was originally popularized by John Preskill^{[1]} but the concept of a quantum computational advantage, specifically for simulating quantum systems, dates back to Yuri Manin‘s (1980)^{[6]} and Richard Feynman‘s (1981) proposals of quantum computing.^{[7]}

Examples of proposals to demonstrate quantum supremacy include the boson sampling proposal of Aaronson and Arkhipov,^{[8]} D-Wave’s specialized frustrated cluster loop problems,^{[9]} and sampling the output of random quantum circuits.^{[10]}

Like factoring integers, sampling the output distributions of random quantum circuits is believed to be hard for classical computers based on reasonable complexity assumptions.^{[10]} Google previously announced plans to demonstrate quantum supremacy before the end of 2017 by solving this problem with an array of 49 superconducting qubits.^{[11]} However, as of early January 2018, only Intel has announced such hardware.^{[12]} In October 2017, IBM demonstrated the simulation of 56 qubits on a conventional supercomputer, increasing the number of qubits needed for quantum supremacy.^{[13]} In November 2018, Google announced a partnership with NASA that would “analyze results from quantum circuits run on Google quantum processors, and … provide comparisons with classical simulation to both support Google in validating its hardware and establish a baseline for quantum supremacy.”^{[14]} Theoretical work published in 2018 suggests that quantum supremacy should be possible with a “two-dimensional lattice of 7×7 qubits and around 40 clock cycles” if error rates can be pushed low enough.^{[15]} On June 18, 2019, Quanta Magazine suggested that quantum supremacy could happen in 2019, according to Neven’s law.^{[16]} On September 20, 2019, the *Financial Times* reported that “Google claims to have reached quantum supremacy with an array of 54 q[u]bits out of which 53 were functional, which were used to perform a series of operations in 200 seconds that would take a supercomputer about 10,000 years to complete”.^{[17]}^{[18]} On October 23, Google officially confirmed the claims.^{[19]}^{[20]}^{[21]} IBM responded by suggesting some of the claims are excessive, and suggested that it could take 2.5 days instead of 10,000 years.^{[22]}^{[23]}^{[24]}

## Contents

## Computational complexity[edit]

Main article: Quantum complexity theory

Complexity arguments concern how the amount of some resource needed to solve a problem scales with the size of the input to that problem. As an extension of classical computational complexity theory, quantum complexity theory is about what a working, universal quantum computer could accomplish without necessarily accounting for the difficulty of building one or dealing with decoherence and noise.^{[25]} Since quantum information is a generalization of classical information, it is clear that a quantum computer can efficiently simulate any classical algorithm.^{[25]}

The complexity class of bounded-error quantum polynomial time (BQP) problems is the class of decision problems that can be solved in polynomial time by a universal quantum computer.^{[26]} It is related to important classical complexity classes by the hierarchy{\displaystyle P\subseteq BPP\subseteq BQP\subseteq PSPACE}.^{[27]} Whether any of these containments is proper is still an open question.^{[27]}

The difficulty of proving what cannot be done with classical computing is a common problem in definitively demonstrating quantum supremacy. Contrary to decision problems that require yes or no answers, sampling problems ask for samples from probability distributions.^{[28]} If there is a classical algorithm that can efficiently sample from the output of an arbitrary quantum circuit, the polynomial hierarchy would collapse to the third level, which is considered very unlikely.^{[10]} Boson sampling is a more specific proposal, the classical hardness of which depends upon the intractability of calculating the permanent of a large matrix with complex entries, which is a #P-complete problem.^{[29]} The arguments used to reach this conclusion have also been extended to IQP Sampling,^{[30]} where only the conjecture that the average- and worst-case complexities of the problem are the same is needed.^{[28]}

## Proposed experiments[edit]

The following are proposals for demonstrating quantum computational supremacy using current technology, often called NISQ devices.^{[2]} Such proposals include (1) a well-defined computational problem, (2) a quantum algorithm to solve this problem, (3) a comparison best-case classical algorithm to solve the problem, and (4) a complexity-theoretic argument that, under a reasonable assumption, no classical algorithm can perform significantly better than current algorithms (so the quantum algorithm still provides a superpolynomial speedup).^{[4]}^{[31]}

### Shor’s algorithm for factoring integers[edit]

Main article: Shor’s algorithm

This algorithm finds the prime factorization of an *n*-bit integer in {\displaystyle {\tilde {O}}(n^{3})} time^{[32]} whereas the best known classical algorithm requires {\displaystyle 2^{O(n^{1/3})}}time and the best upper bound for the complexity of this problem is {\displaystyle O(2^{n/3+o(1)})}.^{[33]} It can also provide a speedup for any problem that reduces to integer factoring, including the membership problem for matrix groups over fields of odd order.^{[34]}

This algorithm is important both practically and historically for quantum computing. It was the first polynomial-time quantum algorithm proposed for a real-world problem that is believed to be hard for classical computers.^{[32]} Namely, it gives a superpolynomial speedup under the reasonable assumption that RSA, today’s most common encryption protocol, is secure.^{[35]}

Factoring has some benefit over other supremacy proposals because factoring can be *checked* quickly with a classical computer just by multiplying integers, even for large instances where factoring algorithms are intractably slow. However, implementing Shor’s algorithm for large numbers is infeasible with current technology,^{[36]}^{[37]} so it is not being pursued as a strategy for demonstrating supremacy.

### Boson sampling[edit]

Main article: Boson sampling

This computing paradigm based upon identical photons sent through a linear-optical network can solve certain sampling and search problems that, assuming a few complexity-theoretical conjectures (that calculating the permanent of Gaussian matrices is #P-Hard and that the polynomial hierarchy does not collapse) are intractable for classical computers.^{[8]} However, it has been shown that boson sampling in a system with large enough loss and noise can be simulated efficiently.^{[38]}

The largest experimental implementation of boson sampling to date had 6 modes so could handle up to 6 photons at a time.^{[39]} The best proposed classical algorithm for simulating boson sampling runs in time {\displaystyle O(n2^{n}+mn^{2})} for a system with *n* photons and *m* output modes.^{[40]}^{[41]} BosonSampling is an open-source implementation in R. The algorithm leads to an estimate of 50 photons required to demonstrate quantum supremacy with boson sampling.^{[40]}^{[41]}

### Sampling the output distribution of random quantum circuits[edit]

The best known algorithm for simulating an arbitrary random quantum circuit requires an amount of time that scales exponentially with the number of qubits, leading one group to estimate that around 50 qubits could be enough to demonstrate quantum supremacy.^{[15]} Google had announced its intention to demonstrate quantum supremacy by the end of 2017 by constructing and running a 49-qubit chip that will be able to sample distributions inaccessible to any current classical computers in a reasonable amount of time.^{[11]} But the largest quantum circuit simulation completed successfully on a supercomputer now contains 56 qubits.^{[42]} This may require increasing the number of qubits to demonstrate quantum supremacy.^{[13]} On October 23, 2019, Google published the results of this quantum supremacy experiment in the Nature article, “Quantum Supremacy Using a Programmable Superconducting Processor” in which they developed a new 53-qubit processor, named “Sycamore”, that is made of fast, high-fidelity quantum logic gates, in order to perform the benchmark testing. Google claims that their machine performed the target computation in 200 seconds, and estimated that their classical algorithm would take 10,000 years in the world’s fastest supercomputer to solve the same problem.^{[43]} IBM disputed this claim, saying that an improved classical algorithm should be able to solve that problem in two and a half days in that same supercomputer. ^{[44]}

## Controversy[edit]

### Skepticism[edit]

Quantum computers are much more susceptible to errors than classical computers due to decoherence and noise.^{[45]} The threshold theorem states that a noisy quantum computer can use quantum error-correcting codes^{[46]}^{[47]} to simulate a noiseless quantum computer assuming the error introduced in each computer cycle is less than some number.^{[48]} Numerical simulations suggest that that number may be as high as 3%.^{[49]}

However, it is not known how the resources needed for error correction will scale with the number of qubits.^{[50]} Skeptics point to the unknown behavior of noise in scaled-up quantum systems as a potential roadblock for successfully implementing quantum computing and demonstrating quantum supremacy.^{[45]}^{[51]}

There have also been algorithmic breakthroughs in classical computing as a result of quantum computing research resulting in comparable performance of classical computers. This implies that at some level quantum supremacy may be trying to prove a negative; that an algorithm doesn’t exist that allows classical computers to perform equally well.^{[52]}

### Name choice[edit]

Some researchers have suggested not using the term “quantum supremacy”, stating that the word “supremacy” evokes distasteful comparisons to the racist belief, white supremacy. A controversial^{[53]}^{[54]} Nature commentary^{[55]} signed by thirteen researchers puts forward the alternative phrase “quantum advantage”.

## See also[edit]

- List of quantum processors
- Sycamore processor which was claimed in 2019 to have attained 50x quantum supremacy in a random digits classification task
^{[citation needed]}^{[56]}

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^{a}^{b}Neville, Alex; Sparrow, Chris; Clifford, Raphaël; Johnston, Eric; Birchall, Patrick M.; Montanaro, Ashley; Laing, Anthony (2017-10-02). “No imminent quantum supremacy by boson sampling”.*Nature Physics*.**13**(12): 1153–1157. arXiv:1705.00686. Bibcode:2017arXiv170500686N. doi:10.1038/nphys4270. ISSN 1745-2473. **^**Edwin Pednault; John A. Gunnels; Giacomo Nannicini; Lior Horesh; Thomas Magerlein; Edgar Solomonik; Robert Wisnieff (October 2017). “Breaking the 49-Qubit Barrier in the Simulation of Quantum Circuits”. arXiv:1710.05867 [quant-ph].**^**“Quantum Supremacy Using a Programmable Superconducting Processor”.*Google AI Blog*. Retrieved 2019-11-02.**^**Metz, Cade (23 October 2019). “Google Claims a Quantum Breakthrough That Could Change Computing”.*The New York Times*. Retrieved 14 January 2020.- ^ Jump up to:
^{a}^{b}Kalai, Gil (2011-06-02). “How Quantum Computers Fail: Quantum Codes, Correlations in Physical Systems, and Noise Accumulation”. arXiv:1106.0485 [quant-ph]. **^**Shor, Peter W. (1995-10-01). “Scheme for reducing decoherence in quantum computer memory”.*Physical Review A*.**52**(4): R2493–R2496. Bibcode:1995PhRvA..52.2493S. doi:10.1103/PhysRevA.52.R2493. PMID 9912632.**^**Steane, A. M. (1996-07-29). “Error Correcting Codes in Quantum Theory”.*Physical Review Letters*.**77**(5): 793–797. Bibcode:1996PhRvL..77..793S. doi:10.1103/PhysRevLett.77.793. PMID 10062908.**^**Aharonov, Dorit; Ben-Or, Michael (1999-06-30). “Fault-Tolerant Quantum Computation With Constant Error Rate”. arXiv:quant-ph/9906129.**^**Knill, E. (2005-03-03). “Quantum computing with realistically noisy devices”.*Nature*.**434**(7029): 39–44. arXiv:quant-ph/0410199. Bibcode:2005Natur.434…39K. doi:10.1038/nature03350. ISSN 0028-0836. PMID 15744292.**^**Kalai, Gil (2016-05-03). “The Quantum Computer Puzzle (Expanded Version)”. arXiv:1605.00992 [quant-ph].**^**Dyakonov, M. I. (2007). “Is Fault-Tolerant Quantum Computation Really Possible?”. In S. Luryi; J. Xu; A. Zaslavsky (eds.).*Future Trends in Microelectronics. Up the Nano Creek*. Wiley. pp. 4–18. arXiv:quant-ph/0610117. Bibcode:2006quant.ph.10117D.**^**Tang, Ewin (2019-05-09). “A quantum-inspired classical algorithm for recommendation systems”.*Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing – STOC 2019*. pp. 217–228. arXiv:1807.04271v3. doi:10.1145/3313276.3316310. ISBN 9781450367059.**^**Board, The Editorial. “Opinion | Achieving Quantum Wokeness”.*WSJ*. Retrieved 2019-12-21.**^**Knapton, Sarah (2019-12-17). “Academics derided for claiming ‘quantum supremacy’ is a racist and colonialist term”.*The Telegraph*. ISSN 0307-1235. Retrieved 2019-12-21.**^**Palacios-Berraquero, Carmen; Mueck, Leonie; Persaud, Divya M. (2019-12-10). “Instead of ‘supremacy’ use ‘quantum advantage'”.*Nature*.**576**: 213–213. doi:10.1038/d41586-019-03781-0.**^**Martinis, John; Boixo, Sergio. “Quantum Supremacy Using a Programmable Superconducting Processor”.*Google AI Blog*. Alphabet. Retrieved 5 December 2019.

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