The koala from linjianma

koala's Introduction

Koala

Koala is a quantum circuit/state simulator based on PEPS tensor networks.

Installation

NumPy and TensorBackends are required. It is recommended to install Koala and TensorBackends in the editable (pip install -e) mode, as they are currently under development.

git clone https://github.com/cyclops-community/tensorbackends.git
pip install -e ./tensorbackends
git clone https://github.com/LinjianMa/koala.git
pip install -e ./koala

Parallelization is provided by Cyclops Tensor Framework, and is optional.

Testing

python -m unittest

Get Started

from koala import peps, Observable, Gate

# initialize a 2 by 3 state with peps approach and numpy backend
qstate = peps.computational_zeros(2, 3, backend='numpy')

# we also provide statevector approach and parallel backend
# statevector.computational_zeros(2, 3, backend='ctf')

# apply one gate or a list of gates
qstate.apply_gate(Gate('X', [], [0])) # (name, parameters, qubits)
qstate.apply_circuit([
    Gate('R', [0.42], [2]),
    Gate('CX', [], [1,4])
], threshold=None, maxrank=None)
# optional truncation threshold and rank for approximate simulation

# or apply arbitrary single site or local two-site operators
# qstate.apply_operator(np.array(...), [0])

# compute the amplitude, probability, and expectation value
qstate.amplitude([1,0,0,1,0,0])
qstate.probability([1,0,0,1,0,0])
observable = 1.5 * Observable.sum([
    Observable.Z(0),
    Observable.XY(3, 4) * 2
])
qstate.expectation(observable, use_cache=True) # optional caching

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