Archive for the ‘Quantum Computer’ Category

The Future of Quantum Computing with Neutral-Atom Arrays – The Quantum Insider

At the recent MCQST Colloquium held at the Max Planck Institute for Quantum Optics, Johannes Zeiher provided a compelling overview of the advances in quantum simulation and quantum computing with neutral-atom arrays. His presentation offered valuable insights into how these systems are poised to transform quantum technology.

Zeiher started by explaining the core motivation behind their work.

Our goal is to understand, control and create many-body systems using individually controllable neutral atoms, he stated. These neutral atoms, arranged using optical tweezers, serve as a powerful platform for studying quantum phenomena due to their high level of controllability and scalability.

One of the key advantages of neutral-atom arrays is their ability to simulate complex quantum systems.

We can use these systems to study strongly correlated systems, transport out of equilibrium dynamics, and phase transitions, Zeiher elaborted. This capability is vital for exploring fundamental aspects of quantum mechanics and for developing new technological applications.

Zeiher also stressed the importance of long-range interactions in these systems.

Long-range interactions introduce competing length scales, which can lead to rich and complex physical phenomena, he noted. By manipulating these interactions, researchers can simulate various phases of matter, such as the superfluid and Mott insulator phases, and even more exotic states like the Haldane insulator and density wave phases.

In terms of practical applications, Zeiher discussed the potential of neutral-atom arrays in quantum computing.

Neutral atoms offer a promising platform for quantum computing due to their scalability and the high fidelity of quantum gates, he said. Recent advancements have pushed the fidelity of two-qubit gates to over 99.5%, putting them on par with other leading quantum computing platforms.

One of the groundbreaking techniques Zeiher discussed is the use of Rydberg dressing. By coupling atoms off-resonantly to Rydberg states, researchers can induce long-range interactions while maintaining a high level of stability. He explained that Rydberg dressing allows them to significantly enhance the lifetime of these states, enabling complex quantum simulations and computations over extended periods.

Zeiher concluded his talk by drawing attention to the broader implications of their research.

The ability to control and manipulate neutral atoms with such precision opens up new frontiers in both quantum simulation and quantum computing, he remarked.

The insights from these systems do not just allow one to push understanding in the realm of quantum mechanics further. Still, they will also serve as a frontier toward innovative technologies that have the potential to be revolutionary in most fields, from materials science to cryptography.

Zeiher uncovered the revolutionizing potential that neutral-atom arrays bear in quantum technology in his talk at the MCQST Colloquium. Given developments in controlling long-range interactions and fidelity of quantum gates, these systems will be of great importance for the future of quantum computing and simulation.

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The Future of Quantum Computing with Neutral-Atom Arrays - The Quantum Insider

Biden administration is providing $504 million to 12 tech hubs – Fortune

The Biden administration said Tuesday that it was providing $504 million in implementation grants for a dozen tech hubs in Ohio, Montana, Nevada and Florida, among other locations.

The money would support the development of quantum computing, biomanufacturing, lithium batteries, computer chips, personal medicine and other technologies.

The administration is trying to encourage moretechnological innovationacross the country, instead of allowing it be concentrated in a few metro areas such as San Francisco, Seattle, Boston and New York City.

The reality is there are smart people, great entrepreneurs, and leading-edge research institutions all across the country, Commerce SecretaryGina Raimondosaid in a call previewing the announcement. Were leaving so much potential on the table if we dont give them the resources to compete and win in the tech sectors that will define the 21st century global economy.

The money comes from the Commerce Departments Economic Development Administration. In October 2023, PresidentJoe Bidendesignated 31 tech hubs. Raimondo said the administration was pushing for more funding for the program so that all the designated tech hubs can get additional resources to compete.

The tech hubs receiving funding include:

$41 million for the Elevate Quantum Tech Hub in Colorado and New Mexico

$41 million for the Headwaters Hub in Montana

$51 million for Heartland BioWorks in Indiana

$51 million for the iFAB Tech Hub in Illinois

$21 million for the Nevada Tech Hub

$40 million for the NY SMART I-Corridor Tech Hub in New York

$44 million for ReGen Valley Tech Hub in New Hampshire

$45 million for the SC Nexus for Advanced Resilient Energy in South Carolina and Georgia

$19 million for the South Florida ClimateReady Tech Hub

$51 million for the Sustainable Polymers Tech Hub in Ohio

$51 million for the Tulsa Hub for Equitable & Trustworthy Autonomy in Oklahoma

$51 million for the Wisconsin Biohealth Teach Hub.

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Biden administration is providing $504 million to 12 tech hubs - Fortune

The Interplay of AI, Cybersecurity & Quantum Computing – The Quantum Insider

At the Tech.eu Summit in London, Dr. Ken Urquhart, Global Vice-President of 5G/Edge/Satellite at Zscaler, and Steve Brierley, Founder and CEO of Riverlane, discussed the critical intersection of artificial intelligence (AI), cybersecurity and quantum computing. Moderated by Duygu Oktem Clark, Managing Partner at DO Venture Partners, the talk underlined both the challenges and opportunities these technologies present.

Urquhart opened the discussion by addressing the limitations of AI in cybersecurity.

AI, as we apply it today, involves algorithms that are interpretable and useful for cyber defense, he said. However, he pointed out that current AI technologies, such as neural networks and large language models, come with issues like statistical variability and hallucinations, where the AI makes things up that may not be true.

Urquhart explained that these statistical models could become less accurate over time, adding: You need to be thoughtful about how you apply AI because it can give less accurate answers if asked the same question twice in a row over a span of hours or days.

Brierley shared his thoughts into the advancements in quantum computing and its implications for cybersecurity. He noted that while todays quantum computers are extremely error-prone and capable of only about 100 to 1,000 operations before failure, significant progress is being made with quantum error correction.

Quantum error correction is a layer that sits on top of the physical qubits and corrects errors in real-time, Brierley explained.

This development is crucial for achieving cryptographically relevant quantum computing capabilities.

2023 and 2024 have been pivotal years as we crossed the threshold in various qubit modalities, making error correction viable, he said. Brierley projected that within the next two to three years, we could see quantum computers performing up to a million operations, surpassing what classical computers can simulate.

As AI and quantum computing advance, ethical and security challenges emerge. Urquhart stressed the importance of understanding AIs current limitations.

We are on a journey with artificial intelligence. It does not think; it is a collection of statistical outcomes, he stated. Urquhart warned against over-reliance on AI for critical decisions, as its current form can lead to significant errors.

Brierley added that quantum computing has the potential to revolutionize industries, particularly in simulating molecular dynamics and chemical interactions.

Quantum computers can replace time-consuming lab experiments with simulations, transforming industries like drug discovery and material science, he said.

Both experts agreed on the necessity of collaboration among academia, industry and government to harness these technologies responsibly. Brierley called attention to the importance of a coordinated effort, likening it to a Manhattan-scale project to build the worlds most powerful quantum computers. We need effective collaboration across sectors to ensure the technology benefits society, he said.

Urquhart echoed this sentiment, giving emphasis to the role of commercial entities in driving innovation and the governments role in providing a regulatory and funding environment.

The machinery is there; we just need the will to engage and make it run, he remarked.

Looking ahead, both Urquhart and Brierley stressed the urgency of preparing for the impact of quantum computing on cybersecurity.

Quantum computing will break most encryption at some point, Urquhart warned, urging businesses to act now to mitigate future risks.

Brierley concluded: Quantum computers are not just faster computers; they represent a massive step forward for specific problems, and their potential for both good and bad is immense.

The discussion underscored the transformative potential of AI and quantum computing while cautioning against complacency. As these technologies evolve, proactive collaboration and ethical considerations will be paramount in shaping a secure digital future.

Featured image: Credit: Tech.eu

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The Interplay of AI, Cybersecurity & Quantum Computing - The Quantum Insider

Post-Quantum Cryptography: Migrating to Quantum Resistant Cryptography – Trend Micro

By Morton Swimmer, Mark Chimley, Adam Tuaima

In the previous parts of this series, we have learned about cryptography, what makes quantum computers unique, and how quantum computers break this cryptography. In the fourth and final part of our study on post-quantum cryptography, we will look at quantum-resistant algorithms that could replace our existing cryptography.

The algorithmic assumption of most existing public key cryptography in common use is that factoring large integers is hard; because of quantum computers and Shors algorithm, this algorithmic assumption is now a vulnerability. We need new assumptions that are quantum resistant, easy to solve if we know the key, but practically impossible to solve without the key on both classical and quantum computers. In the following sections, we get into commonly discussed schemes to solve these new requirements.

Learning with errors (LWE) is based on the idea of representing secret information as a set of equations and then intentionally introducing errors to hide the real value of the hidden information. There are several variants of this, such as ring learning with errors (RLWE) and module learning with errors (MLWE).

Figure 1. In learning with errors, an input value is littered with noise to create an output value.

We can visualize this as a function that takes input values and outputs the result. The true function, represented by the red line, is secret, and we add noise, represented in orange, to create the output value. For more complicated equations, the error makes it difficult to recover the true function accurately.

This scheme hides information within a mathematical grid, a lattice, that makes it extremely difficult to unlock without the right key. Figure 2 shows a two-dimensional lattice, where vectors b1 and b2 are the basis vectors. The lattice problem requires looking for the shortest vector to get to the vector of origin by combining the two basis vectors. While doable but already difficult in a two-dimensional space, the problem becomes increasingly complex in higher dimensions. This problem is known to be NP-Hard, a term that refers to a class of problems where no known algorithm is efficient enough to solve it in practical time, but if a solution is provided (in the case of our discussion, a basis vector that will act as a key), the problem can be verified in polynomial time.

Figure 2. The lattice problem requires looking for the shortest vector by combining the two basis vectors to get to the vector of origin.

Hash-based cryptography is the generic term for constructions of cryptographic primitives based on the security of hash functions. All the currently used cryptographic hashes are considered quantum resistant if sufficiently large keys are used. Doubling the key size is usually enough, but the larger the key, the more unique a hash value can be to the input data, and the smallest of changes will produce a significantly different hash value. So, we can use hash-based cryptography to construct digital signature schemes such as the Merkle signature scheme, zero-knowledge proofs (ZKP), and computational integrity proofs (CIP). The seminal works by Merkle and Lamport that were developed in the 1970s already explored this, but the idea and concept was rediscovered and further refined with the search for post-quantum cryptography.

The code-based approach to cryptography is derived from correcting transmission errors when communicating over a noisy channel. Instead of an unintentionally noisy channel, we intentionally introduce random noise into the stream of cyphertext containing hidden error correcting codes. Without knowing the error correction scheme, it is very difficult to decode the message, and this is referred to the decoding problem. While code-based approaches have been around since at least 1978 with Robert McElieces research, this problem is also known to be NP-Hard. There is also no known quantum solution to solve this problem in practical time.

Elliptic-curve cryptography (ECC) is a type of encryption that uses the points in an elliptic curve to scramble a message and secure data. ECC itself is also broken by quantum computing, by way of connecting two different but related elliptic curves, or isogenous curves.

Figure 3. Isogeny-based cryptography requires looking for the rational map between two elliptic curves that maintains their mathematic properties to allow for both curves to communicate and share information securely.

Isogeny-based encryption is a variant of ECC that uses this concept; it relies on it being easy to come up with a set of isogenies and elliptic curves, but very hard to find the isogenies given only the elliptic curves. Secret keys are derived from a randomly chosen isogeny and the public key is derived from the target of these isogenies. The keys used in this encryption are about the same size as those used in current cryptography, which makes it an attractive option for securing data. Finding the isogenies used to compute points on two known target curves is akin to computing the discrete logs of values in a Diffie-Hellman (DH) key exchange protocol. There is no relation between the problem of finding the isogenies and problems with known good quantum computing solutions and are therefore considered quantum resistant.Its important to note that this description of isogeny-based encryption is simplified for brevity; further explanation will require a deeper and lengthier diver into elliptic curve cryptography.

Quantum computers with a large enough capacity to break our current cryptography do not exist yet, so why should we care?

There is a scenario where an attacker can capture all the internet traffic from a connection, including the key-establishment and the symmetrically encrypted data stream, and when quantum computers of sufficient size exist in the future, the attacker will be able to find the shared key within the captured internet traffic and decrypt the data stream with it. We call this attack on forward-secrecy a harvest attack. As such, there is therefore a need for quantum resistant key establishment and digital signing schemes

The National Institute of Standards and Technology (NIST) initiated a selection process for post-quantum cryptography in 2017 with nearly 70 candidates that have been narrowed down to 15 candidates after the third round of evaluations was completed in 2022. Eight of the 15 candidates are still under evaluation, while seven candidates are already slated for standardization.

Equally important, in 2021 the Internet Engineering Task Force (IETF) discussed a hybrid approach to post-quantum cryptography in TLS 1.2, a prudent method to mitigate any unknown weaknesses in any single algorithm. This idea has been echoed by the German BSI (Federal Office for Information Security) and French ANSSI (Cybersecurity Agency).

Key exchange cryptography is also known as key establishment methodology or key encapsulation mechanism (KEM), where a key is agreed on that can be used for symmetric encryption between two parties. It is a subset of what public key cryptography can do and we currently use algorithms like Diffie-Hellman or RSA (RivestShamirAdleman) for this.

The current selection of KEM finalists in the NIST post-quantum cryptography (PQC) standardization are CRYSTALS-Kyber, NTRU, and Saber (lattice-types), and Classic McEliece (code-based). However, only CRYSTALS-Kyber was selected for standardization in the third round.

CRYSTALS-Kyber is now considered a primary algorithm and has been given the Federal Information Processing Standards (FIPS) number 203. This lattice-based scheme has a LWE module for key generation. Its origins date back to 2005 but the NIST submission was based on a paper released in 2018, making it a young algorithm by cryptography standards. The performance is relatively good, but like the other structured lattice KEMs, Kybers public key is in the order of a thousand bytes still small compared to other candidates, with bandwidth requirements also on the lower side. Kyberhas comparatively fast key generation, encapsulation and decapsulation in software implementations and there are already some optimizations for various software and hardware solutions. It can be implemented on field-programmable gate arrays (FPGA) effectively. Kybers authors put effort into showcasing its security, so it is considered mature despite the relatively new approach. Earlier this year, however, Yilei Chen published algorithms for finding solutions to the lattice problem on a quantum computer. While this is a step in the direction of breaking lattice-based schemes such as Kyber, the inventors have built in various safeguards. For example, Kyber combined LWE with the lattice-based scheme to keep it safe by our current state of knowledge; but it should be noted that combining schemes is still a new field of cryptography.

Classic McEliece was proposed in 2017 but based on the original McEliece algorithm from 1978. This makes it different from many of the other proposals that are LWE and lattice-based. It remains to be seen if it eventually becomes a standard.Classic McEliece, along with other code-based BIKE and HQC, have advanced to the fourth round for further consideration.

Isogeny-based SIKE also initially advanced to the next round as an alternate candidate, but was then retracted when an attack against it was found. There are currently no further isogeny-based schemes in the race, but research continues.

Lattice-type FrodoKEM and NTRU Prime were designated alternatives but will not be standardized. Even though FrodoKEM is now out of the NIST-PQC race, the German BSI and the French ANSSI still consider it a good candidate. There have been appeals to reinstate its consideration in NISTs fourth round.

Meanwhile, the digital signature schemes are referred to as SIG in the NIST standardization process, where CRYSTALSDilithium, FALCON, and SPHINCS+ have been selected for standardization.

CRYSTALS-Dilithium is a lattice-based scheme based on the Fiat-Shamir paradigm to keep key sizes manageable. It uses short keys and signatures likeFALCON but does not require floating point operations. This makes it easier to implement on various CPUs and makes it efficient; NIST strongly recommends Dilithium for implementors. Dilithium is being standardized as FIPS 204.

FALCON is also a lattice-based scheme but is based on the "hash and sign" paradigm. It requires floating point operations and has a complex internal data structure, making it difficult to implement on a variety of CPUs. However, FALCON requires the smallest bandwidth of the candidate schemes and is faster when verifying a signature, but its slower than Dilithium when signing. It was chosen as an alternative when lower bandwidth and higher security is required, but NIST has yet to assign it a FIPS number.

SPHINCS+ is based on the combination of various hash-based schemes that can perform key generation and validation quickly, creating short public keys, but it can be slow when signing, creating long signatures. SPHINCS+ could be tricky to implement and perform cryptanalysis on. Two attacks with specific parameters on SPHINCS+ were discovered in the third round of evaluation, making it a surprising choice for standardization, with the FIPS number 205. We assume they did this to have an alternative to the other two lattice schemes. In this case, it should be noted that care should be taken to avoid insecure parameter sets when using the algorithms.

The Internet Engineering Task Force (IETF) has proposed their own SIG algorithms: XMSS (RFC 8391) and LMS (RFC 8708). LMS is within the IETF standards, while XMSS is created for information only. These will likely remain niche algorithms, at best, in favour of the NIST and FIPS standards.

An alternative to KEM is Quantum Key Exchange (QKE), which is a secure key exchange algorithm that uses the entanglement of particles to establish a shared secret that, if snooped on, will be invalidated. The invalidation happens because the attacker needs to measure the quantum state of the entangled particles, which will result in the collapse of its superposition. This algorithm is called BB84, after the authors Charles Bennett and Gilles Brassard who developed it in 1984.

QKE requires special hardware and a dedicated transmission medium, usually fiber optic cables, but a satellite link has also been successfully tested. Having been tested since the 2000s, the distance that keys can be transmitted has improved from tens of kilometers to around a thousand kilometers.

The technology has already been used in practice: in 2023, Toshiba, the BT Group (formerly British Telecom) and HSBC bank (Hongkong and Shanghai Banking Corporation Limited) tested a secure link in London where symmetric keys were exchanged using QKE, and the data is encrypted over any standard communication link using a symmetric cipher.

In the foreseeable future, this will not be the technology that secures our everyday web server connections but can secure connections between large hubs of computing such as data centers, and it could also connect a data center to an office complex. The current requirement for specialized equipment and a dedicated fiber optic cable makes personal use impossible for the moment.This technology is related to the quantum internet, but to avoid confusion, we refer to it as Quantum Key Distribution Network.

The Open Quantum Safe (OQS) project has taken all finalists and a few alternate candidates and added them to liboqs, an open-source C library for quantum-safe cryptographic algorithms implemented with various parameters for various CPUs. Support for a variety of CPUs beyond the x86 architecture is improving but many smaller IoT processors still have limited support, mostly because of memory limitations.

Open Quantum Safe has also forked the Open Secure Sockets Layer (OpenSSL) and Secure Shell (SSH) projects and added their respective libraries. This will be useful for testing both compatibility as well as performance, but liboqs is not yet recommended for production use.

Various commercial vendors of cryptography have started implementing PQC. These can be tracked at the Public Key Infrastructure (PKI) Consortiums website. Both Cloudflare and Amazon Web Services (AWS) claim to have implemented the finalists algorithms. The communications app, Signal, has started using CRYSTALS-Kyber for their message security.Likewise, Apple has also started using Kyber in their iMessage communications app; but they opted to call it PQ3.

In the last decade, we have gone from encrypted connections being optional to being nearly ubiquitous. Ten years ago, changing a cryptographic algorithm would have been comparatively simple compared to how it is today. Now, browsers use Transport Layer Security (TLS) with either an HTTPS or QUIC connection whenever possible, cloud data is encrypted at rest and in transport, and digital signing of software is the norm. Changing our cryptography now will be a huge task.

The threat landscape now also contains many state actors who may be able and willing to harvest network traffic to decrypt it later when quantum computers become available. This determines whether you decide to require forward-secrecy, such as requiring that encrypted messages stay unreadable for unauthorized viewers for at least five years.

The good news is that many individuals and companies may not have to do anything. Today, some products are starting to integrate PQC algorithms. Google recently integrated CRYSTALS-Kyber into version 116 of Chromium, which means that Google Chrome and other browsers based on Chromium will have PQC support. On the server side, some cloud providers like Google, Amazon and Cloudflare have enabled the same algorithm on the server so that this traffic will be forward-secret in the future.

For instance, Figure 3 shows what Cloudflares test page for PQC negotiation looked like before the upgrade of Chrome, while Figure 4 shows what it looks like after the upgrade and manually activating the Kyber (called X25519Kyber768Draft00) algorithm.

Figure 4. Cloudflares test page for PQC negotiation before Chromes upgrade

Figure 5. Cloudflares test page for PQC negotiation after the upgrade and manually activating the Kyber algorithm

Vendors will be doing many of the migrations to PQC for us using nascent libraries or their own implementations; however, enterprises should understand their exposure and threat model to make sure that some legacy applications do not prevent the upgrade. A mature organization should have a plan for upgrading their cryptography, regardless of the reasons.

Ultimately, this is a business strategy decision, and it depends on a companys threat model. If forward-secrecy is vital to the organization, early adoption will be vital. If not, enterprises can opt to wait for vendors to implement PQC and roll it out within their systems gradually. An informed decision involving business leadership will ensure its success.

The first stage in upgrading to new algorithms is to identify cryptographic algorithm use. For the purposes of upgrading to post-quantum cryptography, we must first look at existing public key cryptography. We need to know what self-managed cryptography is used, by whom, where, and for what assets. We must also identify the respective value of the assets being encrypted.

Next, we need to know how systems are communicating with each other: If a server supports PQC but a client does not, an enterprise cannot enforce PQC. While the largest cloud providers are being proactive, some services may not get updated, which needs to be determined.

We know that all PQC algorithms have larger key sizes and requires more CPU and memory capacity to compute compared to current algorithms. The performance impact is of concern on servers that need to establish numerous connections. The server hardware will have to be scaled up, or cryptographic co-processors may be needed if these exist for PQC by then. Some cloud providers offload the TLS connection to specialized servers, which could also alleviate the problem.

Enterprises need to understand the risk to their respective businesses. Some questions to consider are:

These evaluations should be collected in a report and used as a reference to help identify further priority actions.

Before execution, the upgrade plan needs to be tested to identify and negotiate which algorithm will be used by each library, as well as enabling and monitoring the logs to make sure a PQC algorithm is used when it is expected. Stress testing connections to see if the larger keys and lower performance has negative impacts is another important test.

NIST will most likely standardize the PQC algorithms in 2024 or 2025. The German BSI, French ANSSI and Japanese CRYPTREC are likely to adopt the same guidelines. Until that happens, we dont expect any of the PQC algorithms to be integrated into mainstream cryptographic libraries, but a good time to start planning for post-quantum cryptography is still now.

We cannot say for certain when large enough quantum computers will exist, or even if that goal is attainable within the next few generations. By our estimate, it looks likely that well see smaller, still useful machines, in a few years. Larger, more universal machines may follow in a decade. While that sounds like a long way away, changing cryptography in an enterprise is no small feat and should be tackled as early as possible. Furthermore, the benefits are already tangible if we better understand our cryptographic stance, especially considering that so much of security rides on good cryptography.

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Post-Quantum Cryptography: Migrating to Quantum Resistant Cryptography - Trend Micro

Quantum computers are like kaleidoscopes why unusual metaphors help illustrate science and technology – The Conversation Indonesia

Quantum computing is like Forrest Gumps box of chocolates: You never know what youre gonna get. Quantum phenomena the behavior of matter and energy at the atomic and subatomic levels are not definite, one thing or another. They are opaque clouds of possibility or, more precisely, probabilities. When someone observes a quantum system, it loses its quantum-ness and collapses into a definite state.

Quantum phenomena are mysterious and often counterintuitive. This makes quantum computing difficult to understand. People naturally reach for the familiar to attempt to explain the unfamiliar, and for quantum computing this usually means using traditional binary computing as a metaphor. But explaining quantum computing this way leads to major conceptual confusion, because at a base level the two are entirely different animals.

This problem highlights the often mistaken belief that common metaphors are more useful than exotic ones when explaining new technologies. Sometimes the opposite approach is more useful. The freshness of the metaphor should match the novelty of the discovery.

The uniqueness of quantum computers calls for an unusual metaphor. As a communications researcher who studies technology, I believe that quantum computers can be better understood as kaleidoscopes.

The gap between understanding classical and quantum computers is a wide chasm. Classical computers store and process information via transistors, which are electronic devices that take binary, deterministic states: one or zero, yes or no. Quantum computers, in contrast, handle information probabilistically at the atomic and subatomic levels.

Classical computers use the flow of electricity to sequentially open and close gates to record or manipulate information. Information flows through circuits, triggering actions through a series of switches that record information as ones and zeros. Using binary math, bits are the foundation of all things digital, from the apps on your phone to the account records at your bank and the Wi-Fi signals bouncing around your home.

In contrast, quantum computers use changes in the quantum states of atoms, ions, electrons or photons. Quantum computers link, or entangle, multiple quantum particles so that changes to one affect all the others. They then introduce interference patterns, like multiple stones tossed into a pond at the same time. Some waves combine to create higher peaks, while some waves and troughs combine to cancel each other out. Carefully calibrated interference patterns guide the quantum computer toward the solution of a problem.

The term bit is a metaphor. The word suggests that during calculations, a computer can break up large values into tiny ones bits of information which electronic devices such as transistors can more easily process.

Using metaphors like this has a cost, though. They are not perfect. Metaphors are incomplete comparisons that transfer knowledge from something people know well to something they are working to understand. The bit metaphor ignores that the binary method does not deal with many types of different bits at once, as common sense might suggest. Instead, all bits are the same.

The smallest unit of a quantum computer is called the quantum bit, or qubit. But transferring the bit metaphor to quantum computing is even less adequate than using it for classical computing. Transferring a metaphor from one use to another blunts its effect.

The prevalent explanation of quantum computing is that while classical computers can store or process only a zero or one in a transistor or other computational unit, quantum computers supposedly store and handle both zero and one and other values in between at the same time through the process of superposition.

Superposition, however, does not store one or zero or any other number simultaneously. There is only an expectation that the values might be zero or one at the end of the computation. This quantum probability is the polar opposite of the binary method of storing information.

Driven by quantum sciences uncertainty principle, the probability that a qubit stores a one or zero is like Schroedingers cat, which can be either dead or alive, depending on when you observe it. But the two different values do not exist simultaneously during superposition. They exist only as probabilities, and an observer cannot determine when or how frequently those values existed before the observation ended the superposition.

Leaving behind these challenges to using traditional binary computing metaphors means embracing new metaphors to explain quantum computing.

The kaleidoscope metaphor is particularly apt to explain quantum processes. Kaleidoscopes can create infinitely diverse yet orderly patterns using a limited number of colored glass beads, mirror-dividing walls and light. Rotating the kaleidoscope enhances the effect, generating an infinitely variable spectacle of fleeting colors and shapes.

The shapes not only change but cant be reversed. If you turn the kaleidoscope in the opposite direction, the imagery will generally remain the same, but the exact composition of each shape or even their structures will vary as the beads randomly mingle with each other. In other words, while the beads, light and mirrors could replicate some patterns shown before, these are never absolutely the same.

Using the kaleidoscope metaphor, the solution a quantum computer provides the final pattern depends on when you stop the computing process. Quantum computing isnt about guessing the state of any given particle but using mathematical models of how the interaction among many particles in various states creates patterns, called quantum correlations.

Each final pattern is the answer to a problem posed to the quantum computer, and what you get in a quantum computing operation is a probability that a certain configuration will result.

Metaphors make the unknown manageable, approachable and discoverable. Approximating the meaning of a surprising object or phenomenon by extending an existing metaphor is a method that is as old as calling the edge of an ax its bit and its flat end its butt. The two metaphors take something we understand from everyday life very well, applying it to a technology that needs a specialized explanation of what it does. Calling the cutting edge of an ax a bit suggestively indicates what it does, adding the nuance that it changes the object it is applied to. When an ax shapes or splits a piece of wood, it takes a bite from it.

Metaphors, however, do much more than provide convenient labels and explanations of new processes. The words people use to describe new concepts change over time, expanding and taking on a life of their own.

When encountering dramatically different ideas, technologies or scientific phenomena, its important to use fresh and striking terms as windows to open the mind and increase understanding. Scientists and engineers seeking to explain new concepts would do well to seek out originality and master metaphors in other words, to think about words the way poets do.

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Quantum computers are like kaleidoscopes why unusual metaphors help illustrate science and technology - The Conversation Indonesia