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Here is the sixty-eighth episode of Quantum Foam, Quantum Compute Engines. A quantum computer is a computer that represents and processes information using quantum states. A quantum state is a mathematical entity that represents a physical system. Quantum mechanics specifies the construction, evolution, and measurement of a quantum state. Knowledge of the quantum state and the rules for the systems evolution in time exhausts all that can be known about a quantum system. Quantum computations exploit phenomena such as superposition, interference, and entanglement. Quantum computers have the potential to complete some calculations exponentially faster than classical computers. A large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations. Current hardware implementations of quantum computation are largely experimental and are suitable for only certain specialized tasks. The basic unit of computation in quantum computing is the qubit. It serves a similar function as the bit in ordinary or classical computing. Unlike a classical bit, a qubit can exist in a linear combination of states known as a quantum superposition. The result of measuring a qubit is 1 of the 2 states given by a probabilistic rule. If a quantum computer manipulates the qubit in a particular way wave interference effects amplify the probability of the desired measurement results. Quantum algorithm design involves creating procedures that allow a quantum computer to perform this amplification. A Bloch Sphere is a theoretical representation that is used to depict the idea of a qubit.
A quantum compute engine is basically the software that runs on a quantum computer. One of the most heated debates in quantum computing is over which hardware approach will ultimate win and become the next type of computer architecture. There are approaches such as superconducting qubits, trapped ions, neutral atoms, photonics, topological qubits, and quantum annealers that all take different routes to the same broad goal. Each have different strengths and limitations. Superconducting and trapped ion systems are among the most commercially developed today. Neutral atoms are being explored for larger systems. Photonics have a strong tie to quantum networking. Quantum annealers target specific optimization problems. Topological qubits remain at an earlier stage. Each approach offers different trade-offs in speed, error rates, coherence time, and scalability. For the last quarter of the episode, we are taking a look at a specific open-sourced quantum compiler that is used in IBM quantum systems. This is an intermediate, multi-level representation framework which includes definitions for several dialects to represent parameterized quantum computation at multiple levels of abstraction. The compiler also offers Python bindings and a diagnostic system. This can generate an abstract syntax tree that is translated to a high-level dialect. An extensible, hierarchical target system for modeling the heterogeneous nature of control systems at compilation time is included. Target-based and generic compilation passes are added using a pipeline interface to translate the input down to low-level intermediate representations and can take advantage of back ends and tooling to generate machine executable binaries. This is an example of how a real quantum compute engine utilizes a system that is meant to run quantum algorithms with other useful functionality.
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