unicorn | Symbolic Execution , Bounded Model Checking
kandi X-RAY | unicorn Summary
kandi X-RAY | unicorn Summary
Unicorn compiles 64-bit RISC-V ELF binaries via finite state machines over the theory of bitvectors and arrays of bitvectors to SMT and SAT formulae as well as quadratic unconstrained binary optimization (QUBO) models and quantum circuits (QCs) that are all logically equivalent. Satisfiability of the SMT and SAT formulae generated by Unicorn can be checked using standard SMT and SAT solvers, respectively. QUBO models are generated by a component of Unicorn called QUBOT and target quantum annealers. QCs are generated by another component of Unicorn called QUARC and target gate-model quantum computers. Unicorn models RISC-V code execution with a bit-precise finite state machine over all RISC-V machine states using 64-bit bitvectors, one for each general-purpose 64-bit CPU register, 1-bit bitvectors, one for each possible program counter value, and an array of 64-bit bitvectors modeling 64-bit main memory. Unicorn constructs the finite state machine such that a state S is reachable from the initial state in n state transitions if and only if there is input to the RISC-V code that makes a RISC-V machine reach the machine state modeled by S after executing the RISC-V code for no more than n instructions. Unicorn outputs the finite state machine for a given state S either as QUBO model, that is, as objective function of a quantum annealer for a given bound on n, or as quantum circuit for a gate-model quantum computer. The output of a quantum machine is an input to the RISC-V code for reaching S during execution, if there is such input, resembling what a symbolic execution engine or bounded model checker for RISC-V would do. Unicorn reports the size of QUBO models and quantum circuits in number of quantum bits. QUBO models grow linearly in n (quadratically in n if the RISC-V code's memory consumption is unbounded). Quantum circuits grow linearly in code size (or memory usage if the RISC-V code's memory consumption is unbounded). Unicorn optimizes size by applying SMT and SAT solvers during compilation. Whenever a quantum bit can be shown to represent a constant value for all inputs, the bit is replaced with that value. Given a time budget T, Unicorn attempts to find solutions for all quantum bits within T. Only if Unicorn runs out of time, involving a quantum machine makes sense yet only as long as the number u of undetermined quantum bits is less than the number a of quantum bits available on the machine. We call a-u the quantum advantage with time budget T. Unicorn supports the base 64-bit RISC-V instruction set with some extensions (specifically rv64im at the moment), and five system calls (exit, brk, openat, read, write). RISC-V code gets input through the read system call. We plan to support more RISC-V extensions and more system calls eventually. For more information on how Unicorn works check out our paper.
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Trending Discussions on Quantum Computing
QUESTION
According to the Q# documentation, a single qubit can be measured by M.The method uses Pauli-Z. But why Pauli Z can be used to measure a single qubit? I have known the matrix of Pauli-Z like below:
and the output result is given by the distribution:
But what's the relationship between the matrix and the formula? What's happened with method M? I really need your help.
...ANSWER
Answered 2022-Mar-25 at 18:18Pauli Z matrix defines the basis in which the measurement is performed. A measurement in the Pauli Z basis is the same as the computational basis measurement, projecting the state onto one of the states |0⟩ or |1⟩ (the eigenstates of Pauli Z matrix).
I'm not up for spelling the math here, since classical StackOverflow doesn't support LaTeX. You can find a good tutorial on single-qubit measurements in Q# in the Quantum Katas project.
QUESTION
Recently, I'm learning the Q# language for machine learning. The sample of half-moons has been run correctly. Now I want to learn the detail of the code. But there is too little explanation to find. There are too many methods I can't understand and there are no introductions in detail. For example, it only explains the name, parameters for the method, but no further information. I really can't understand it. So is there an exits detailed document for machine learning for beginners? Thank u very much.
how to get the detained document
...ANSWER
Answered 2022-Mar-25 at 17:34Q# machine learning library implements one specific approach, circuit-centric quantum classifiers. You can find the documentation for this approach at https://docs.microsoft.com/en-us/azure/quantum/user-guide/libraries/machine-learning/intro and the subsequent pages in that section. The paper it's based on is 'Circuit-centric quantum classifiers', Maria Schuld, Alex Bocharov, Krysta Svore and Nathan Wiebe.
QUESTION
I'm currently trying to make my own TransformationPass to use when compiling a QuantumCircuit for specific hardware, but I'm struggling to get things to work with the DAGCircuit that gets passed to the run(self, dag) method that gets overridden. My main issue at the moment is trying to figure out which qubits each node in the graph actually operates on. I can access the wire for each node, but accessing the qubit index from there raises a DeprecationWarning.
I can simply ignore the warning, but it gives me the impression that I should be going about this another way.
Is there a formal method for accessing the qubit (either object or simply its index) given the DAG?
...ANSWER
Answered 2022-Mar-22 at 11:46For DAGCircuit right now there isn't a great answer for this. The .index attribute is deprecated as in the case of standalone bit objects on the circuit if they're in a register it might not yield the result you expect (it'll be the register index not the index on the circuit necessarily).
I typically do this by having something like:
QUESTION
I use VS Code for C# and Unity3D and TypeScript and Angular and Python programming, so I have pretty much every required extension, including the .NET Framework and Core as well as the Quantum Development Kit (QDK) plus the Q# Interoperability Tools and also C# and Python extensions for VS Code.
I have devised the following steps to create my first quantum Hello World based on a few tutorials:
...ANSWER
Answered 2022-Feb-27 at 10:24With help from a user on another forum, it turns out the problem was the command:
QUESTION
I tried to implement Deutsch algorithm using qiskit. The following is a code.
...ANSWER
Answered 2021-Aug-01 at 05:36Deutsch algorithm applies the X gate to the qubit you use for phase kickback trick, to prepare it in the |-⟩ state before applying the oracle. Your implementation applies it to the "data" qubit instead, so that the combined effect of the algorithm (after H gates cancel out) is just preparing the data qubit in |1⟩ state.
QUESTION
I'm using the qiskit textbook, and it creates a QuantumCircuit and then draws the circuit, and it looks like this:
I see the same result when running the textbook as a jupyter notebook in IBM's quantum lab.
However, when I download the textbook as a jupyter notebook and run it myself locally, it looks like this:
I don't like this very much, and I think I am missing something simple. The code that is running is exactly the same. I am using MacOS 11.4 (Big Sur). The following code is sufficient to show a difference when I run it online vs. locally:
...ANSWER
Answered 2021-Jun-05 at 17:40Because Qiskit has multiple drawers. Those are:
textmpllatexlatex_source.
The drawer you see in the IBM Quantum Lab is the one based on Matplotlib. You can get the same output by qc.draw('mpl').
To set a default, you can change (or create if does not exist) the file ~/.qiskit/settings.conf) with the entry circuit_drawer = mpl.
QUESTION
I had to try the example of qiskit’s Traveling Salesman Problem with 3 nodes and executing it at IBM backend called simulator_statevector.Can execute and get the result normally.
But when trying to solve the TSP problem with more than 3 nodes,I changed n = 3 to n = 4.
...ANSWER
Answered 2021-Jun-05 at 12:02I found the answer, my method is to increase the Ansat number of reps from 5 to 7.
from solving TSP 4 node problem
QUESTION
I am trying to code finance portfolio optimisation problem into a quantum annealer, using the Hamiltonian Ising model. I am using the dwave module
...ANSWER
Answered 2021-May-12 at 03:26If you are familiar with the physics of the Ising model (e.g. just look it up on wikipedia), you will find out that the term "linear bias" h is used instead of the physics term external constant magnetic field and the term "quadratic bias" J is used instead of the physics term of interaction between a pair of (neighbouring in the case of the Ising model) spins. My guess is that the h and J coefficients must be learned from some given data. Your job is to cast (interpret) the data available to you into an Ising model configuration (state) and then use some sort of optimization with unknown h and J that minimizes the difference between the model's solutions (theoretical Ising model configuration) and the observed data.
QUESTION
I've installed Qiskit-textbook by pip install git+https://github.com/qiskit-community/qiskit-textbook.git#subdirectory=qiskit-textbook-src. But I don't know where is it downloaded
ANSWER
Answered 2021-May-11 at 12:41That command installs the Qiskit Textbook package, which is a Python package containing some of the problems and widgets used in the textbook. You can see the location of an installed package using pip show :
QUESTION
This may show my naiveté but it is my understanding that quantum computing's obstacle is stabilizing the qbits. I also understand that standard computers use binary (on/off); but it seems like it may be easier with today's tech to read electric states between 0 and 9. Binary was the answer because it was very hard to read the varying amounts of electricity, components degrade over time, and maybe maintaining a clean electrical "signal" was challenging.
But wouldn't it be easier to try to solve the problem of reading varying levels of electricity so we can go from 2 inputs to 10 and thereby increasing the smallest unit of storage and exponentially increasing the number of paths through the logic gates? I know I am missing quite a bit (sorry the puns were painful) so I would love to hear why or why not. Thank you
...ANSWER
Answered 2021-Feb-24 at 18:40"Exponentially increasing the number of paths through the logic gates" is exactly the problem. More possible states for each n-ary digit means more transistors, larger gates and more complex CPUs. That's not to say no one is working on ternary and similar systems, but the reason binary is ubiquitous is its simplicity. For storage, more possible states also means we need more sensitive electronics for reading and writing, and a much higher error frequency during these operations. There's a lot of hype around using DNA (base-4) for storage, but this is more on account of the density and durability of the substrate.
You're correct, though that your question is missing quite a bit - qubits are entirely different from classical information, whether we use bits or digits. Classical bits and trits respectively correspond to vectors like
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