There are a number of ways to convert a string to its Unicode representation in JavaScript, depending on the desired format of the output. Here are a few approaches, each with explanations and examples: Method 1: Using charCodeAt() for individual characters This method iterates through each character in the string and uses charCodeAt() to get its Unicode code point. It's suitable when you need the individual code points for each character. function stringToUnicodeCodePoints(str) { let codePoints = []; for (let i = 0; i < str.length; i++) { codePoints.push(str.charCodeAt(i)); } return codePoints; } let myString = "Hello, world!"; let unicodePoints = stringToUnicodeCodePoints(myString); console.log(unicodePoints); // Output: [72, 101, 108, 108, 111, 44, 32, 119, 111, 114, 108, 100, 33] Explanation: The function stringToUnicodeCodePoints takes a string str as input. It initializes an empty array codePoints to store the Unicode code points. ...
Quantum computing has seen remarkable progress in recent years. After the first demonstrations of “quantum supremacy” or “quantum advantage”, companies are now publishing roadmaps projecting commercial quantum computers with millions of qubits within a decade.
Meanwhile, new quantum computing approaches based on photons, atoms and topology are catching up to the early leaders using superconducting circuits and ion traps.
The fundamental building blocks of quantum computers are qubits (quantum bits). Unlike regular bits, qubits can exist in a superposition of 0 and 1, allowing massive parallelism. When entangled together, qubits enable certain computations like simulation of quantum systems to be done exponentially faster. The catch is quantum states are fragile and error-prone. The threshold for useful applications is estimated to be between hundreds of thousands to millions of physical qubits.
In 2019, Google announced its 53-qubit Sycamore processor achieved quantum supremacy by sampling the output distribution of a pseudo-random quantum circuit. This sampling would take 10,000 years on a classical supercomputer but their quantum processor did it in 200 seconds. However, the computational task itself was not very useful other than proving a point. In 2021, a 100 photon quantum computer from China also showed quantum advantage doing a similar sampling task.
In 2022, IBM announced demonstrating quantum “utility” using its 127-qubit Eagle processor to simulate time evolution of a quantum spin system. While an impressive physics simulation, the notion of “utility” was still debatable. IBM plans to scale up to over 1000 physical qubits by end of 2022 and eventually to more than one million qubits by 2030 using a modular architecture. Google has also outlined a roadmap to scale up its Sycamore quantum processors but without specific timelines.
The early leaders in the race for quantum computing are based on superconducting circuits (Google, IBM, Rigetti etc) and trapped ions (IonQ, Honeywell Quantum etc). Superconducting qubits require complex cryogenic systems to cool them down to millikelvin temperatures. Ion trap qubits involve manipulating individual atoms trapped by electric fields and also require cooling to cryogenic temperatures.
In the past couple of years, new approaches have shown significant progress to rival the early leaders. Photonic quantum computing uses photons as qubits. While qubits are prone to errors, photonics has the advantage of room temperature operation. Challenges include scaling up the optical elements and single photon sources into integrated photonic chips. Startups like PsiQuantum, Xanadu and QuiX are at the forefront along with academic groups.
For example, Xanadu has built a 216-qubit photonic processor called Borealis using a layered architecture. The Canadian company PsiQuantum is developing qubits based on squeezed states of light and aims to have over 1 million photonic qubits by around 2025 based on silicon photonics chips manufactured by GlobalFoundries. Researchers in the Netherlands, Germany and China have all demonstrated prototype photonic chips with tens of qubits.
Atoms held in optical tweezers is emerging as another promising approach. Here, individual neutral atoms are trapped in arrays using focused laser beams. This enables flexible 3D qubit configurations. Startups like ColdQuanta, Atom Computing and Pasqal are developing qubits encoded in atomic states. Coherence times exceeding tens of seconds have been shown. So far these systems have operated with tens of qubits but scaling promises to be easier than in ion traps.
Interest is rising in topological quantum computing where the qubits are protected from errors by topological properties like conservation of angular momentum. Microsoft has been the pioneer but faced setbacks in their approach using exotic quasiparticles called Majorana zero modes. However, in 2022, they reported a breakthrough re-demonstrating these elusive particles in a superconducting-semiconducting nanowire architecture. Google and Quantinuum have also now shown advances in creating alternative topological qubits.
Most big tech companies have now published vague quantum computing roadmaps. Microsoft expects functional systems in less than 10 years. IBM aims for more than 1000 qubits this year and 1 million by 2030. Google is investing heavily but hasn't revealed timelines. Amazon released its first quantum computer in late 2021 but is discreet about next steps. Similar claims of million qubit computers by around 2030 have been made by startups like PsiQuantum, ColdQuanta, Atom Computing and others.
A key challenge is scaling up qubits without losing qubit performance and fidelity. This requires advances in materials, fabrication and control electronics. Modular architectures built up from sub-modules containing arrays of qubits will be critical. Packaging multiple modules with sophisticated interconnects then enables scaling up to very large numbers. Companies are also architecting comprehensive software and cloud platforms to leverage NISQ (noisy intermediate-scale quantum) processors available today. Hybrid quantum-classical algorithms that combine classical and quantum hardware are an active area of research.
Realistically, while reaching the million qubit milestone is important, useful applications may emerge much sooner than that. Focus is increasing on quantum machine learning as a potential killer app for NISQ processors. Other promising near term uses are quantum chemistry, optimization, quantum finance and sampling applications.
Some believe quantum networking for communication security may prove commercially viable earlier than general purpose quantum computing. However, there are still skeptics who think engineering a fault-tolerant, fully error-corrected quantum computer may not be possible at all.
Geopolitical tensions are also catalyzing quantum progress. Facing technology sanctions, China is investing heavily and has unveiled a $15 billion National Laboratory for Quantum Information this year. The European Union plans to launch a quantum communication infrastructure connecting major cities by 2030. Quantum computing is seen as a critical technology where US dominance is threatened especially by China. This concern was a major factor behind the US National Quantum Initiative Act enacted in 2018 which is now funding more than $1 billion in quantum research and development each year.
The steady pace of advances and growing investments by both tech giants and startups suggest quantum computing systems able to surpass classical supercomputers at useful applications could become a reality in the next 10 to 15 years. But there are still deep technical challenges to be overcome before this vision materializes.
Pioneering physicists and engineers are pushing the boundaries of manipulating matter and light at nanoscales to usher in this quantum future. Concerted long term efforts across materials, software, computer science and commercialization will be key to eventually unlocking the full potential of quantum computing.