Errors are part of the Quantum Computing equation.

By 2035, IBM envisions fault tolerant computers, capable of breaking encryption algorithms.

Quantum Safe Algorithms?

There’s a handful of encryption algorithms that are considered ‘quantum safe’

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Richard Feynman was one of the pioneers in the field of quantum computation. His work laid the groundwork for understanding how quantum systems could be used to perform computations in ways that classical computers cannot.

1. Basic Principles:
   • Feynman proposed that a quantum computer could simulate any quantum physical system, highlighting the natural alignment between quantum mechanics and computation.
   • He emphasized that quantum systems, described by quantum mechanics, can perform many calculations simultaneously due to the principle of superposition.
2. Quantum Bits (Qubits):
   • Unlike classical bits, which can be either 0 or 1, qubits can be in a superposition of both states simultaneously. This property allows quantum computers to process a vast amount of possibilities at once.
3. Quantum Gates:
   • Quantum gates manipulate qubits through unitary operations, preserving quantum information and ensuring that transformations are reversible.
   • The quantum gates operate on the principles of quantum mechanics, including superposition and entanglement, which enable parallelism and complex computations.
4. Quantum Parallelism:
   • A quantum computer can evaluate many possibilities simultaneously. For instance, it can factorize large numbers much more efficiently than classical computers using algorithms like Shor’s algorithm.

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Reversibility is a fundamental concept in quantum computing, contrasting with classical computing, where operations are often irreversible.

1. Reversibility:
   • Quantum computations are inherently reversible because they are governed by unitary transformations. This means every quantum operation has a unique inverse.
   • Reversible computation implies that no information is lost during the computation process, aligning with the principles of quantum mechanics.
2. Irreversibility:
   • Classical computations can be irreversible; for example, the AND gate in classical logic destroys information about the input once the output is produced.
   • Irreversible operations lead to the generation of heat and energy dissipation, which is a significant limitation in miniaturizing classical computing components.
3. Quantum Reversibility in Practice:
   • Reversible quantum gates, such as the Hadamard gate, Pauli-X, and controlled-NOT (CNOT) gate, perform operations without losing information.
   • Quantum circuits are designed to maintain reversibility, ensuring that the final state of the quantum system can be traced back to its initial state.
4. Implications for Quantum Computation:
   • The reversibility of quantum operations implies that quantum computers can perform complex calculations without the loss of information, making them potentially more efficient for specific problems.
   • Reversible quantum computation is crucial for error correction in quantum systems, as it helps in maintaining coherence and reducing decoherence effects.

Feynman’s insights into quantum computation and the importance of reversibility have driven the development of quantum algorithms and error-correcting codes, which are essential for the practical realization of quantum computers. His work underscores the transformative potential of quantum computation in solving problems that are intractable for classical computers.

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The Halting Problem is a fundamental concept in the theory of computation, specifically related to Turing machines. A Turing machine is a mathematical model of computation that defines an abstract machine capable of manipulating symbols on a strip of tape according to a set of rules. Here is an explanation of the halting problem:

1. Definition:
   • A Turing machine $T$ is said to halt on input $x$ if it eventually stops processing and produces an output.
   • The halting problem is the question of determining, given a Turing machine $T$ and an input $x$, whether $T$ halts when run with $x$.
2. Undecidability:
   • Alan Turing proved that a general algorithm to solve the halting problem for all possible Turing machine-input pairs cannot exist. This means there is no Turing machine that can determine for every pair $(T,x)$ whether $T$ halts on $x$.
   • The proof uses a technique known as diagonalization and involves the construction of a Turing machine that leads to a contradiction if such a halting algorithm exists.
3. Implications:
   • The undecidability of the halting problem has profound implications in computer science and mathematics, indicating that there are inherent limitations to what can be computed.
   • It also implies that there are some problems for which no algorithm can determine a solution in finite time, highlighting the boundaries of algorithmic computation.

In the Feynman Lectures on Computation, the halting problem is discussed in the context of universal Turing machines and the limitations of computability:

• The book explains that if you had an effective procedure for computation, you could find a Turing machine to perform that computation.
• It distinguishes between functions that always halt (complete functions) and those that do not always halt (partial functions).
• The halting problem is introduced by questioning whether we can determine in advance if a Turing machine will halt for a specific input, concluding that it is not possible to construct a computable function to predict this

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Quantum Gates and Operators in Qubit Devices

and  applications of quantum devices

Types of qubit devices used in quantum computing include

1. Superconducting Qubits:
   • Principle: Superconducting qubits are based on superconducting circuits, which exhibit quantum behaviors at very low temperatures.
   • Types: There are several types of superconducting qubits, including charge qubits, flux qubits, and transmon qubits.
   • Advantages: They are relatively easy to fabricate using established semiconductor technology and can be integrated into complex circuits.
   • Challenges: Superconducting qubits are susceptible to noise and decoherence, requiring advanced error correction techniques to maintain coherence.
2. Trapped Ion Qubits:
   • Principle: Trapped ion qubits use ions confined in electromagnetic traps. Quantum information is stored in the internal energy levels of the ions.
   • Advantages: They have long coherence times and high-fidelity gate operations. They are well-suited for precision measurements and quantum simulations.
   • Challenges: Scaling up the number of qubits is difficult due to the complexity of controlling many ions simultaneously.
3. Semiconductor Qubits:
   • Principle: Semiconductor qubits are based on the quantum states of electrons in semiconductor materials. Examples include quantum dot qubits and donor qubits.
   • Advantages: They can be manufactured using techniques similar to those used in classical semiconductor devices, potentially allowing for integration with existing technology.
   • Challenges: They face challenges related to coherence times and scalability.
4. Topological Qubits:
   • Principle: Topological qubits are based on exotic particles called anyons, which exist in two-dimensional systems. They rely on the principles of topological quantum computation.
   • Advantages: They are theoretically immune to local sources of noise and decoherence, promising more robust qubits.
   • Challenges: The practical realization of topological qubits is still in its early stages, and creating the necessary conditions for anyons to exist is experimentally challenging.
5. Photonic Qubits:
   • Principle: Photonic qubits use the quantum states of photons, such as polarization or phase, to represent quantum information.
   • Advantages: Photons are excellent carriers of quantum information over long distances, making them ideal for quantum communication.
   • Challenges: Implementing quantum gates with photons is difficult, and creating scalable photonic quantum computing architectures remains a challenge.

These types of qubits represent the forefront of research in quantum computing, each with its unique set of advantages and challenges. The continued development of these technologies is crucial for the advancement of practical and scalable quantum computers.

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Find the scalar product ⟨Ψ−(θ)∣Ψ+(θ)⟩\langle \Psi_{-}(\theta) | \Psi_{+}(\theta) \rangle⟨Ψ−​(θ)∣Ψ+​(θ)⟩ and discuss.

Given: ∣Ψ+(θ)⟩=cos⁡(θ)∣0⟩+sin⁡(θ)∣1⟩|\Psi_{+}(\theta)\rangle = \cos(\theta) |0\rangle + \sin(\theta) |1\rangle∣Ψ+​(θ)⟩=cos(θ)∣0⟩+sin(θ)∣1⟩ ∣Ψ−(θ)⟩=cos⁡(θ)∣0⟩−sin⁡(θ)∣1⟩|\Psi_{-}(\theta)\rangle = \cos(\theta) |0\rangle – \sin(\theta) |1\rangle∣Ψ−​(θ)⟩=cos(θ)∣0⟩−sin(θ)∣1⟩

To find the scalar product:

⟨Ψ−(θ)∣Ψ+(θ)⟩=(cos⁡(θ)⟨0∣−sin⁡(θ)⟨1∣)(cos⁡(θ)∣0⟩+sin⁡(θ)∣1⟩)\langle \Psi_{-}(\theta) | \Psi_{+}(\theta) \rangle = (\cos(\theta) \langle 0| – \sin(\theta) \langle 1|)(\cos(\theta) |0\rangle + \sin(\theta) |1\rangle)⟨Ψ−​(θ)∣Ψ+​(θ)⟩=(cos(θ)⟨0∣−sin(θ)⟨1∣)(cos(θ)∣0⟩+sin(θ)∣1⟩)

Expanding the product, we get:

⟨Ψ−(θ)∣Ψ+(θ)⟩=cos⁡2(θ)⟨0∣0⟩+cos⁡(θ)sin⁡(θ)⟨0∣1⟩−sin⁡(θ)cos⁡(θ)⟨1∣0⟩−sin⁡2(θ)⟨1∣1⟩\langle \Psi_{-}(\theta) | \Psi_{+}(\theta) \rangle = \cos^2(\theta) \langle 0|0\rangle + \cos(\theta)\sin(\theta) \langle 0|1\rangle – \sin(\theta)\cos(\theta) \langle 1|0\rangle – \sin^2(\theta) \langle 1|1\rangle⟨Ψ−​(θ)∣Ψ+​(θ)⟩=cos2(θ)⟨0∣0⟩+cos(θ)sin(θ)⟨0∣1⟩−sin(θ)cos(θ)⟨1∣0⟩−sin2(θ)⟨1∣1⟩

Using the orthonormality of the basis states ($⟨0|0⟩=1$, $⟨1|1⟩=1$, and $⟨0|1⟩=⟨1|0⟩=0$), we have:

⟨Ψ−(θ)∣Ψ+(θ)⟩=cos⁡2(θ)⋅1+0−0−sin⁡2(θ)⋅1=cos⁡2(θ)−sin⁡2(θ)\langle \Psi_{-}(\theta) | \Psi_{+}(\theta) \rangle = \cos^2(\theta) \cdot 1 + 0 – 0 – \sin^2(\theta) \cdot 1 = \cos^2(\theta) – \sin^2(\theta)⟨Ψ−​(θ)∣Ψ+​(θ)⟩=cos2(θ)⋅1+0−0−sin2(θ)⋅1=cos2(θ)−sin2(θ)

Therefore:

⟨Ψ−(θ)∣Ψ+(θ)⟩=cos⁡(2θ)\langle \Psi_{-}(\theta) | \Psi_{+}(\theta) \rangle = \cos(2\theta)⟨Ψ−​(θ)∣Ψ+​(θ)⟩=cos(2θ)

Discussion: The scalar product ⟨Ψ−(θ)∣Ψ+(θ)⟩=cos⁡(2θ)\langle \Psi_{-}(\theta) | \Psi_{+}(\theta) \rangle = \cos(2\theta)⟨Ψ−​(θ)∣Ψ+​(θ)⟩=cos(2θ) tells us about the overlap between the states ∣Ψ−(θ)⟩|\Psi_{-}(\theta)\rangle∣Ψ−​(θ)⟩ and ∣Ψ+(θ)⟩|\Psi_{+}(\theta)\rangle∣Ψ+​(θ)⟩. When $\theta =0$, $\cos (2\theta )=1$, so the states are identical. When θ=π4\theta = \frac{\pi}{4}θ=4π​, $\cos (2\theta )=0$, indicating that the states are orthogonal.

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1. Church-Turing Principle: Deutsch extends the classical Church-Turing hypothesis, which states that any function naturally regarded as computable can be computed by a Turing machine. He formulates a physical principle asserting that “every finitely realizable physical system can be perfectly simulated by a universal model computing machine operating by finite means.”
2. Limitations of Classical Computers: Classical deterministic computing machines compute functions deterministically, while stochastic (probabilistic) classical machines provide outputs based on probability distributions. However, these classical models do not fully adhere to the Church-Turing principle when considering the continuous nature of classical systems versus the discrete nature of Turing machines.
3. Quantum Computers: Deutsch introduces the concept of quantum computers as a generalization of classical Turing machines. Quantum computers can potentially simulate any physical system perfectly, adhering to the strong form of the Church-Turing principle. Unlike classical machines, quantum computers leverage quantum superposition and entanglement, allowing them to perform certain tasks more efficiently through quantum parallelism.
4. Quantum Parallelism: One of the remarkable properties of quantum computers is their ability to perform many calculations simultaneously due to quantum parallelism. This is achieved by preparing the quantum system in a superposition of states and then evolving it according to quantum dynamics, allowing for the simultaneous exploration of multiple computational paths.
5. Implications for Physics: The paper explores the deeper connections between quantum computation and physical theories. Quantum complexity theory, for instance, provides a more physically realistic definition of complexity and knowledge within a physical system compared to classical complexity theory.
6. Universal Quantum Computer: Deutsch describes a universal quantum computer capable of simulating any physical system, arguing that quantum mechanics, unlike classical mechanics, is compatible with the Church-Turing principle in its strongest form. This universal quantum computer would have properties that are not reproducible by any classical Turing machine, though it would not compute non-recursive functions.
7. Experimental Validation: While the principles underlying the theory of quantum computers are grounded in physical assertions, their empirical validation follows the same criteria as other physical principles, relying on the consistency with experimentally corroborated theories and observations.

In summary, Deutsch’s work lays the foundational theoretical framework for quantum computing, highlighting the limitations of classical computing and the potential of quantum computers to transform our understanding of computation and its relationship with physical systems

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