Quantum Intelligence Part 1.
Utilizing quantum mechanics to create the future of AI, Part 1. Learn Quantum Computing
Quantum computing and artificial intelligence are probably some of the world’s most heavily recognized buzzword technologies to date. These different emerging technologies present a plethora of new applications for society, some of which still remain untapped. However, when we begin to make these new developments, we will reach some extraordinary unexpected outcomes. This new future is approaching rapidly, and will likely be a catalyst for development in our lives. This is an introductory part 1 of quantum artificial intelligence.
Quantum Computing
Quantum computing was a paradigm proposed in 1980 by the physicist Paul Benioff. Quantum mechanical models, or diagrams describing the quantum interactions within different systems, like the atom or a motherboard. Benioff hypothesized a model of the Turing machine, a method of describing the computational processes of an abstract machine. After his proposition, the power of quantum computing was realized by Yuri Manin and Richard Feynman, who believed that quantum computers could simulate and solve many more complexities than normal computers.
On a very high level, quantum computing is actually a simple technological concept; it mainly concerns leveraging two quantum phenomena, entanglement, and superposition to execute computational tasks. Due to the nature of quantum information, quantum computers are exponentially more powerful than classical computing systems, as the are able to break strong encryptions, solve very difficult functions, and run simulations, as they essentially teleport to the solution, or are in a superimposed state of all solutions at once, meaning they always arrive at the optimal answer.
Quantum Computing from a Programming Lens ䷢
Though quantum computing has evolved to the point where it’s open source and remains on a remote and more accessible cloud, but there are still key methods that are used in programming for quantum systems development. The most convenient form of this is quantum logic gates, which is a quantum circuit that utilizes a standard low amount of qubits, which are the bit models of quantum systems that exhibit the behavior described in the introductory section. The quantum circuit is the model that is essentially a conglomeration of different logic gates.
A quick side note:
If you aren’t familiar with matrices or simple concepts in linear algebra, don’t worry! You can understand development without a strong mathematics background, although it is advised, especially when considering the physics end of quantum computing. Still, read on!
Quantum logic gates are time reversible, a capability classical computers do not posses, meaning that they can complete all functions that classical circuits can and more, but not vice versa. In computational mathematics, quantum logic gates are represented as unitary matrices, which are square matrices whose conjugate transpositions = its inverse. In quantum mechanics, we use a notation called bra-ket, or Dirac notation, in which a vector v in an abstract vector space V. In simpler terms, a state of a quantum system is being depicted.
- the “ket” is a qubit notation seen often; |v〉being the ket vector, which shows the quantum states
- the “bra” is the bra vector; ⟨f|is the notation, belong to a certain space, in which the elements of that space represent some linear map from the space to C
- Therefore, qubits can be represented as:
In vector form. For nonindustrial/beginner purposes, there is a typical one or two qubit model, which is what currently prevents us from running more complex algorithms and methods locally, like quantum annealing.
Due to superposition causing qubits to remain in both binary states at once, it has equal complex probability amplitudes, represented with ket as 1 and 0,
In taking two qubits, we get:
where the ⊗ is the tensor product, an operator for combining quantum states.
When calculating the state, we multiply by the vector, giving
as the quantum state resultant.
Using different programming languages like python, and new application programming interfaces (API), we can actually program quantum systems remotely. I’ve personally done so with quite a few, like continuous variable quantum gates, photonic quantum computing systems, and quantum simulations, among some others.
Some Quick Physics on Quantum Computing ⚗️
Quantum computing is an interesting technology, as it involves the intersection between two difficult and information packed disciplines.
So we’ve already gone over superposition: a behavior of qubits that describes the phenomena of being in two indiscrete states at once, that is only stopped when introduced to some sort of interference, like a bias for them to assume a classical value or 0 or 1, as opposed to the 0 and 1 quantum state.
So now, we’ll go over two other important phenomena that without using any deep math (I’m making a separate article for that), but more conceptually through diagrams and models. These are entanglement and tunneling.
Quantum entanglement: This is a property of particles, or qubits in this case, that describe how when qubits are in are paired in close proximity that their quantum states cannot be discerned, shown by this diagram
, then they become entangled, meaning that they are physically separable, but mutually indistinguishable. The point is, they’re connected in a very special manner. When on qubit assumes a value after being introduced to some variety that causes qubit coherence, the other corresponds with the same behavior, and vice versa. The wave function collapse or any other physical correspondence will be equal and opposite, and the qubits then operate as a single entangled system.
Quantum entanglement actually poses a large amount of different applications within interconnectivity, as the ability to manipulate properties of entangled states, no matter their distance of separation gives way to a sort of data teleportation technique, which is currently in theoretical/minimal testing stages. However, quantum entanglement as a concept does pose an ideal way to completely reduce communication latencies.
Wave-mechanical Tunneling: This is a quantum behavior called quantum tunneling that allows a wavefunction like this
to essentially phase, or propogate through a potential barrier. A wave function is just a function that is a wave, but it's also a mathematical and graphical description of an isolated quantum system’s quantum state, with numerous fluctuations and multiple extrema.
In math, the wave function is mainly centered around limits, the approximate value of a function as a certain input reaches, or rather, approaches a certain value, and a usage of limits, called the derivative, which is essentially the instantaneous rate of change within a curve, or the slope of a tangent line that crosses two infinitesimally close points on a curve (if you agree with Leibnitz).
It is important to note that quantum entanglement isn’t the wavefunction disappearing or teleporting, but its rather a continuous movement, where it passes through the barrier, which is typically rectangular or square.
In quantum mechanisms, the barrier is a one dimensional concern, and involves a lot more math, but I promised to keep the numbers out and the simple in, so we’ll move forward without Schrödinger array of important equations and time dependent calculations, or anything related to them for that matter. However, it is important to note that the process is time dependent, involves kinetic energy, and uses calculations with the hamiltonian.
There are many types of quantum tunneling:
- Phase space tunneling
- Resonance assisted tunneling
- Chaos assisted tunneling
They involve oscillatory free particle behaviors, but each introduce a different concept of dynamical quantum tunneling, where a quantum particle is transferred between two unrelated systems, and there isn’t necessarily a barrier.
Quantum tunneling has many practical applications when considering energy and electricity, especially within cold emissions with superconductors, nuclear reactions like fusion and fission, as well as further implications in astrochemistry and quantum biology. It’s a very exciting concept that can give numerous physical insights!
Up next?
So quantum computing clearly has so many applications, but what about when its combined with artificial intelligence? Well, that’s the premise of this article, and it’s what I’ll be talking about in part 3 of this series on quantum AI, after the overview of what artificial intelligence is more in-depth, so stay tuned.
In the meantime, you can start researching more on your own, or stay excited and wait to see whats next. The quantum space is so exciting, and its going to completely disrupt the way that we’ll live our lives. Just think about the vision.
“A quantum central intelligence”
My name is Okezue Bell, and I’m a 14 y/o innovator/entrepreneur in the quantum computing and AI spaces. I’m also currently making developments in foodtech and cellular agriculture, as well as biocomputing! Contact me more:
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