{
    "info": {
        "author": "Ben Bartlett",
        "author_email": "benbartlett@stanford.edu",
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        "description": "# Simulator for quantum networks and channels\n\n<!-- images are hard-linked so they will show up on pypi page -->\n\nThe _Simulator for Quantum Networks and Channels_ (`SQUANCH`) is an open-source Python library for creating parallelized simulations of distributed quantum information processing. The framework includes many features of a general-purpose quantum computing simulator, but it is optimized specifically for simulating quantum networks. It includes functionality to allow users to easily design complex multi-party quantum networks, extensible classes for modeling noisy quantum channels, and a multiprocessed NumPy backend for performant simulations.\n\nA schematic overview of the modules available in `SQUANCH` is shown below. (Refer to the [documentation](https://att-innovate.github.io/squanch/) or the [whitepaper](https://arxiv.org/abs/1808.07047) for more details.)\n\n![Overview of SQUANCH framework structure](https://raw.githubusercontent.com/att-innovate/squanch/master/docs/source/img/moduleOverview.png)\n\n`SQUANCH` is developed as part of the Intelligent Quantum Networks and Technologies ([INQNET](http://inqnet.caltech.edu)) program, a [collaboration](http://about.att.com/story/beyond_quantum_computing.html) between AT&T and the California Institute of Technology. \n\n## Documentation\n\nDocumentation for this package is available at the [documentation website](https://att-innovate.github.io/squanch/) or as a [pdf manual](/docs/SQUANCH.pdf). We encourage interested users to read the whitepaper for the `SQUANCH` platform, \"A distributed simulation framework for quantum networks and channels\" (arXiv: [1808.07047](https://arxiv.org/abs/1808.07047)), which provides an overview of the framework and a primer on quantum information.\n\n## Installation \n\nYou can install SQUANCH directly using the Python package manager, `pip`:\n\n```\npip install squanch\n```\n\nIf you don't have `pip`, you can get it using `easy_install pip`.\n\n## Demonstrations\n\nDemonstrations of various quantum protocols can be found in the [demos](/demos) folder and in the [documentation](https://att-innovate.github.io/squanch/demos.html):\n\n- [Quantum teleportation](https://att-innovate.github.io/squanch/demos/quantum-teleportation.html)\n- [Superdense coding](https://att-innovate.github.io/squanch/demos/superdense-coding.html)\n- [Man-in-the-middle attack](https://att-innovate.github.io/squanch/demos/man-in-the-middle.html)\n- [Quantum error correction](https://att-innovate.github.io/squanch/demos/quantum-error-correction.html)\n\n### Example: quantum interception attack\n\nAs an example to put in this readme, let's consider a scenario where Alice wants to send data to Bob. For security, she transmits her message through [quantum superdense coding](https://en.wikipedia.org/wiki/Superdense_coding). In this scenario, shown below as a circuit diagram, we have four [`Agents`](https://att-innovate.github.io/squanch/getting-started.html#using-agents-in-your-simulations), who act as follows:\n\n<img src=\"https://raw.githubusercontent.com/att-innovate/squanch/master/docs/source/img/man-in-middle-circuit.png\" width=500>\n\n- Charlie generates entangled pairs of qubits, which he sends to Alice and Bob.\n- Alice receives Charlie's qubit. She encodes two bits of her data in it and sends it Bob.\n- Bob receives the qubits from Charlie and Alice. He operates jointly on them and measures them to reconstruct Alice's two bits of information.\n- However, the fourth agent, Eve, wants to know Alice's data. She intercepts every qubit Alice sends to Bob, measures it, and re-transmits it to Bob, hoping he won't notice.\n\nAn implementation of this scenario in `SQUANCH` is given below.\n\n```python\nimport numpy as np\nimport matplotlib.image as image\nfrom squanch import *\n\nclass Charlie(Agent):\n    '''Charlie sends Bell pairs to Alice and Bob'''\n    def run(self):\n        for qsys in self.qstream:\n            a, b = qsys.qubits\n            H(a)\n            CNOT(a, b)\n            self.qsend(alice, a)\n            self.qsend(bob, b)\n\nclass Alice(Agent):\n    '''Alice tries to send data to Bob, but Eve intercepts'''\n    def run(self):\n        for _ in self.qstream:\n            bit1 = self.data.pop(0)\n            bit2 = self.data.pop(0)\n            q = self.qrecv(charlie)\n            if bit2 == 1: X(q)\n            if bit1 == 1: Z(q)\n            # Alice unknowingly sends the qubit to Eve\n            self.qsend(eve, q) \n\nclass Eve(Agent):\n    '''Eve naively tries to intercept Alice's data'''\n    def run(self):\n        bits = [] \n        for _ in self.qstream:\n            a = self.qrecv(alice)\n            bits.append(a.measure())\n            self.qsend(bob, a)\n        self.output(bits)\n\nclass Bob(Agent):\n    '''Bob receives Eve's intercepted data'''\n    def run(self):\n        bits = []\n        for _ in self.qstream:\n            a = self.qrecv(eve)\n            c = self.qrecv(charlie)\n            CNOT(a, c)\n            H(a)\n            bits.extend([a.measure(), c.measure()])\n        self.output(bits)\n\n# Load Alice's data (an image) and serialize it to a bitstream\nimg = image.imread(\"docs/source/img/foundryLogo.bmp\") \nbitstream = list(np.unpackbits(img))\n\n# Prepare an appropriately sized quantum stream\nqstream = QStream(2, int(len(bitstream) / 2))\nout = Agent.shared_output()\n\n# Instantiate agents\nalice = Alice(qstream, out, data=bitstream)\nbob = Bob(qstream, out)\ncharlie = Charlie(qstream, out)\neve = Eve(qstream, out)\n\n# Connect the agents to form the network\nalice.qconnect(bob)\nalice.qconnect(eve)\nalice.qconnect(charlie)\nbob.qconnect(charlie)\nbob.qconnect(eve)\n\n# Run the simulation\nSimulation(alice, eve, bob, charlie).run()\n\n# Display the images Alice sent, Eve intercepted, and Bob received\n# (Plotting code omitted for brevity; results shown below)\n``` \n\n![Images sent by Alice, intercepted by Eve, and received by Bob](https://raw.githubusercontent.com/att-innovate/squanch/master/docs/source/img/man-in-the-middle-results.png)\n\n## Citation\n\nIf you are doing research using `SQUANCH`, please cite our whitepaper:\n\n> B. Bartlett, \"A distributed simulation framework for quantum networks and channels,\" arXiv: 1808.07047 [quant-ph], Aug. 2018.\n\n\n",
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