Welcome to the Multiverse

Quantum Computing

We have seen the Marvel movies that talk about the multiverse such as Ant-Man and the Wasp: Quantumania.

However, today Google announced that the multiverse might be real due to its newest benchmark around Quantum Computing. A new quantum semiconductor from Google code-named “Willow” conducted a computation so fast that it “lends credence to the notion that quantum computation occurs in many parallel universes, in line with the idea that we live in a multiverse”

Building Blocks of Quantum Computing

We have heard about Quantum Computing and little things called qubits.

The most fundamental building block of a quantum computer is the qubit — a unit of quantum information that is comparable to a bit in a classical computer, but with the uncanny ability to represent a complex combination of both 0 and 1 simultaneously.

What makes qubits special?

Unlike classical bits, qubits can exist in a superposition of states, meaning they can be both 0 and 1 simultaneously. This property, along with another quantum phenomenon called entanglement, allows quantum computers to perform complex calculations much faster than classical computers.

How are qubits created?

Qubits can be created using various physical systems, including:

• Superconducting circuits: These circuits are cooled to extremely low temperatures to maintain quantum coherence.
• Trapped ions: Ions (atoms that have lost or gained electrons) are trapped and manipulated using lasers.
• Photons: Particles of light can be used to encode quantum information.
• Quantum dots: Tiny semiconductor structures that can hold individual electrons.

But to understand Quantum Computing we need to see what it can create.

What could Quantum Computing do?

One of the most promising aspects of quantum computing is discovering new types of matter that never existed before. Other opportunities include:

Material Science and Drug Discovery:

Simulating molecules: Quantum computers can simulate complex molecular interactions, accelerating the discovery of new materials and drugs.

Optimizing chemical reactions: By understanding the quantum nature of chemical reactions, researchers can optimize processes for greater efficiency and yield.

Artificial Intelligence and Machine Learning:

Enhanced AI algorithms: Quantum computing can power more advanced AI algorithms, leading to breakthroughs in areas like natural language processing and image recognition.

Optimized machine learning models: Quantum computers can accelerate the training and optimization of machine learning models, enabling faster and more accurate predictions.

Financial Modeling and Optimization:

Risk assessment: Quantum computers can analyze vast datasets to identify complex financial risks and develop more effective risk management strategies.

Portfolio optimization: By considering a wider range of factors and scenarios, quantum computers can help optimize investment portfolios.

Cryptography and Cybersecurity:

Breaking current encryption: Quantum computers could potentially crack current encryption methods, making it crucial to develop quantum-resistant encryption algorithms.

Secure communication: Quantum key distribution (QKD) offers a highly secure method for transmitting information, protected by the laws of quantum mechanics.

Climate Modeling and Sustainability:

Simulating climate systems: Quantum computers can model complex climate systems with greater accuracy, helping us understand and predict climate change.

Optimizing energy systems: By analyzing vast amounts of data, quantum computers can optimize energy grids and renewable energy sources.

Fear Not of Breaking the World’s Security

Given enough qubits, an algorithm invented by mathematician Peter Shor in 1994 could crack the encryption that underpins much of today’s internet. Fortunately, researchers have devised new encryption schemes that sidestep this risk, and earlier this year the US National Institute of Standards and Technology (NIST) released new “post-quantum” encryption standards.

This new post-quantum encryption standards are designed to protect sensitive information in a future where quantum computers are powerful enough to break current encryption methods. These standards are based on mathematical problems that are believed to be resistant to attacks from both classical and quantum computers.

Here’s a simplified explanation of how they work:

1. Key Establishment:

CRYSTALS-Kyber: This algorithm is used for key exchange, allowing two parties to establish a shared secret key over an insecure channel.

How it works: It involves mathematical operations on lattices, which are geometric structures that are difficult to solve for quantum computers. By exchanging information and performing complex calculations, the two parties can securely establish a shared secret key.

2. Digital Signatures:

CRYSTALS-Dilithium and SPHINCS+: These algorithms are used for digital signatures, which verify the authenticity and integrity of digital documents.

How they work: They involve mathematical operations on lattices and hash functions, respectively. These operations are computationally intensive for classical and quantum computers, making it difficult to forge signatures.

Quantum Computing Brings the Noise

But with Quantum there’s not just a fight between Quantum and Classical Computing there’s also a fight to prevent the noise.

Noise in quantum computing refers to the various disturbances and errors that can affect the delicate quantum states of qubits. These disturbances can lead to the loss of quantum information and hinder the accurate execution of quantum algorithms.

• Sources of Noise Decoherence: This is the primary source of noise, where qubits interact with their environment, losing their quantum properties and transitioning to classical states.
• Thermal Noise: Thermal fluctuations can cause qubits to randomly flip their states, leading to errors. Electromagnetic Interference: Electromagnetic radiation from sources like microwaves or radio waves can disrupt the delicate quantum states.
• Imperfect Quantum Gates: The physical implementation of quantum gates can introduce errors due to timing inaccuracies or other hardware imperfections.

So now as we look at AI versus Quantum Computing, why are so many startups and technology companies still focused on classical computing scenarios using Artificial Intelligence and not looking towards the future with Quantum?

• Skillset and Expertise: Quantum computing requires a specialized skillset that is not widely available. Developing quantum algorithms and programming quantum computers is a highly specialized field.
• Limited Applications: While quantum computing has the potential to revolutionize certain industries, its current capabilities are limited to specific applications. Many problems can still be efficiently solved using classical computers and AI.
• Focus on Near-Term Gains: Many companies are focused on delivering products and services in the near term to generate revenue and grow their businesses. Quantum computing, while promising, is a long-term investment with uncertain returns.

However the biggest drawback to Classical Computing is energy consumption.

Currently AI models, particularly large language models, generally consume more energy than current quantum computers.

AI Energy Consumption

AI models, especially large language models and neural networks, require significant computational power and energy to train and operate.

AI models, especially large language models, are incredibly energy-intensive. Training these models requires vast amounts of electricity, often exceeding the power consumption of entire countries.

• Energy Source Sustainability: The energy powering AI models must come from sustainable sources to minimize environmental impact. While renewable energy sources like solar and wind are becoming more prevalent, they are not always reliable or sufficient to meet the demands of AI.
• Energy Storage: AI models often operate continuously, requiring a constant supply of energy. Developing efficient and scalable energy storage solutions, such as advanced batteries, is crucial to address this challenge.
• Energy Efficiency: Improving the energy efficiency of AI hardware and algorithms is essential to reduce power consumption. This includes developing more energy-efficient chips, optimizing software, and exploring alternative computing architectures.
• Cooling: The massive energy consumption of AI models generates significant heat, requiring efficient cooling systems. Finding sustainable cooling solutions that minimize energy consumption is a critical challenge.

Room Temperature Quantum Computing

Quantum computers are still in their early stages of development, and their energy consumption is not yet fully understood.

A majority of current quantum computers require extremely cold temperatures (near absolute zero) to operate, which necessitates significant energy input for cooling systems.

However, theoretical advancements suggest that future quantum computers could be more energy-efficient than classical computers for certain tasks. Especially when technology advances are allowing for room temperature quantum computers.

While current quantum computers typically operate at extremely low temperatures (near absolute zero), researchers have made strides in developing qubits that can maintain quantum coherence at room temperature.

• Photons as qubits: Photons are less susceptible to environmental disturbances, allowing them to retain their quantum state at higher temperatures. However, challenges remain in efficiently manipulating and detecting these photonic qubits.
• Solid-state qubits: This is based on nitrogen-vacancy centers in diamond. These qubits have shown promise in maintaining quantum coherence at higher temperatures, but further research is needed to scale them up and improve their performance.

Your Brain is a Quantum Computer?

If you cannot wrap your brain around how Quantum Computing works, that might be the point. Theorists believe your brain might contain 100 billion quantum bits, which would make your own brain more powerful than all the digital computers in the world combined.

Yes, there is evidence that our brains may use quantum computation:

• Brain functions and quantum processes

Scientists from Trinity College Dublin adapted an idea used to prove quantum gravity to study the human brain. They found that the brain functions they measured were correlated with short-term memory and conscious awareness. The scientists believe that quantum processes are an important part of these functions.

• Quantum mechanics and brain behavior

Quantum mechanics can describe how measurement affects physical systems. Quantum probability can also describe behavioral phenomena in psychology and decision-making.

• Microtubules in neurons

Some work suggests that microtubules in neurons may temporarily maintain superposition states and exhibit quantum properties.

• Superradiance

Large structures built out of the amino acid tryptophan, like neurons tubulin, can display superradiance.

Superradiance is a fascinating phenomenon in quantum mechanics where a group of atoms or molecules, when excited to the same energy level, collectively emit photons in a synchronized and highly intense burst. This cooperative emission results in a much brighter and faster pulse of light than would be possible from individual atoms acting independently.

Multiverse Of Options

So as Marvel pivots to it’s new movies that will be released in 2025, there might be a multiverse where the movies that Marvel had planned on creating using Kang the Conqueror is already happening.

“Have I killed you before?” — Kang to Ant-Man

There might even be a multiverse that already has Quantum Computing working as I talked about in a previous blog talking about that our universe might be a simulation.

Either way, Schrödingers cat is a famous thought experiment that illustrates the strange nature of quantum superposition. In this scenario, a cat is placed in a sealed box with a device that could kill the cat, triggered by a random quantum event. Until the box is opened and the cat observed, quantum theory suggests that the cat exists in a superposition of states, both alive and dead.

So as you reading this, think there might be a version of you not reading this. Or worse, yet you are not even alive.

Yes I am suggesting all of us are Schrödingers cat.

Welcome to the multiverse.

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Article written with assistance from Google Gemini.