From 1376d0952a2d994ef723511dd8fbe6d13a8b659e Mon Sep 17 00:00:00 2001 From: LeiZzzzzz Date: Sat, 5 Oct 2024 22:27:01 +0800 Subject: [PATCH] update v0.2.0, readme & tutorials --- quairkit/__init__.py | 161 +++++++++++++++--- tutorials/feature/batch.ipynb | 122 +++++++------- tutorials/feature/custom.ipynb | 38 ++--- tutorials/feature/qudit.ipynb | 205 ++++++++++++----------- tutorials/introduction/Hamiltonian.ipynb | 76 ++++----- tutorials/introduction/circuit.ipynb | 122 +++++++------- tutorials/introduction/measure.ipynb | 76 +++++---- tutorials/introduction/operator.ipynb | 193 ++++++++++----------- tutorials/introduction/qinfo.ipynb | 74 ++++---- tutorials/introduction/state.ipynb | 96 +++++------ tutorials/introduction/training.ipynb | 40 ++--- 11 files changed, 654 insertions(+), 549 deletions(-) diff --git a/quairkit/__init__.py b/quairkit/__init__.py index 693d233..b3a4ae4 100644 --- a/quairkit/__init__.py +++ b/quairkit/__init__.py @@ -47,39 +47,151 @@ cd QuAIRKit pip install -e . -Functionality -------------- +Batch computation +----------------- + +QuAIRKit supports batch computations for quantum circuit simulations, state measurement and quantum information processing. It is easy to use and can be customized for different quantum (machine learning) algorithms. + +Below is an example of batch computation for quantum circuit simulation. Here a zero state is passed through four different quantum circuits, and compared with the target state. + +```python +import quairkit as qkit +from quairkit.database import * +from quairkit.qinfo import * + +target_state = zero_state(1) +unitary_data = pauli_group(1) + +cir = qkit.Circuit(1) +cir.oracle(unitary_data, 0) +cir.ry(param=[0, 1, 2, 3]) + +print(state_fidelity(cir(), target_state)) # zero-state input by default +``` + +```text +tensor([1.0000, 0.4794, 0.8415, 0.0707]) +``` + +Qudit computation +----------------- + +QuAIRKit also supports batch computations for quantum circuit simulations and most of the quantum information processing tools in qudit quantum computing. Note that qudit computation can be used with batch computation, as shown below + +```python +# claim three systems, with 1 qubit and 1 qutrit +cir = qkit.Circuit(2, system_dim=[2, 3]) + +# apply the Heisenberg-Weyl operators on all systems +cir.oracle(heisenberg_weyl(6), [0, 1]) + +# apply the H gate on the first system, controlled by the second system +cir.control_oracle(h(), [1, 0]) + +# trace out the qutrit system and get the qubit state +traced_state = cir().trace(1) + +print('The 6th and 7th state for the batched qubit state is', traced_state[5:7]) +``` + +```text +The 6th and 7th state for the batched qubit state is +--------------------------------------------------- + Backend: density_matrix + System dimension: [2] + System sequence: [0] + Batch size: [2] + + # 0: +[[1.+0.j 0.+0.j] + [0.+0.j 0.+0.j]] + # 1: +[[0.5+0.j 0.5+0.j] + [0.5+0.j 0.5+0.j]] +--------------------------------------------------- +``` + +Fast construction +----------------- + +QuAIRKit provides a fast and flexible way to construct quantum circuits, by self-managing the parameters. All parameters would be created randomly if not specified. QuAIRKit also supports built-in layer ansatzes, such as `complex_entangled_layer`. + +```python +cir = qkit.Circuit(3) + +cir.h() # apply Hadamard gate on all qubits +cir.complex_entangled_layer(depth=3) # apply complex entangled layers of depth 3 +cir.universal_three_qubits() # apply universal three-qubit gate with random parameters +``` + +`qkit.Circuit` is a child class of `torch.nn.Module`, so you can access its parameters and other attributes directly, or use it as a layer in a hybrid neural network. + +Implicit transition +------------------- + +If you want to perform noise simulation or mixed-state-related tools, there is no need to specify the backend, or import other libraries. Just call the function, and QuAIRKit will transit the backend for you. + +```python +cir = qkit.Circuit(3) + +cir.complex_entangled_layer(depth=3) +print(cir().backend) + +# partial transpose on the first two qubits +print(cir().transpose([0, 1]).backend) + +cir.depolarizing(prob=0.1) +print(cir().backend) +``` + +```text +state_vector +density_matrix +density_matrix +``` + +Global setup +------------ + +QuAIRKit provides global setup functions to set the default data type, device and random seed. + +```python +qkit.set_dtype('complex128') # default data type is complex64 +qkit.set_device('cuda') # make sure CUDA is setup with torch +qkit.set_seed(73) # set seeds for all random number generators +``` + +Overall Structure +----------------- + +QuAIRKit provides the following functionalities, - Quantum neural network algorithm simulation - Quantum circuit simulation & visualization - Quantum channel simulation - Quantum algorithm/information tools -Modules -------- - -``quairkit``: QuAIRKit source code +`quairkit`: QuAIRKit source code -- ``ansatz``: module of circuit templates -- ``database``: module of useful matrices & sets -- ``operator``: module of quantum operators -- ``qinfo``: library of quantum algorithms & information tools -- ``circuit``: quantum circuit interface +- `database`: module of useful matrices & sets +- `loss`: module of quantum loss functions +- `qinfo`: library of quantum algorithms & information tools +- `circuit`: quantum circuit interface Tutorials --------- -Check out the tutorial folder on `GitHub `_ for more information. - -Relations with Paddle Quantum ------------------------------ +- `Hamiltonian in QuAIRKit `_ +- `Constructing Quantum Circuits in QuAIRKit `_ +- `Measuring quantum states in QuAIRKit `_ +- `Quantum gates and quantum channels `_ +- `Quantum information tools `_ +- `Manipulation of Quantum States in QuAIRKit `_ +- `Training parameterized quantum circuits `_ +- `Batch Computation `_ +- `Neural network setup customization `_ +- `Introduction to qudit quantum computing `_ -`Paddle Quantum `_ is the world's first cloud-integrated -quantum machine learning platform based on Baidu PaddlePaddle. As most contributors to this project -are also contributors to Paddle Quantum, QuAIRKit incorporates key architectural elements and -interface designs from its predecessor. QuAIRKit focuses more on providing specialized tools and -resources for researchers and developers engaged in cutting-edge quantum algorithm design and -theoretical explorations in quantum information science. """ import os @@ -103,10 +215,10 @@ def print_info() -> None: r"""Print the information of QuAIRKit, its dependencies and current environment. """ - import torch + import matplotlib import numpy import scipy - import matplotlib + import torch print("\n---------VERSION---------") print("quairkit:", __version__) print("torch:", torch.__version__) @@ -123,8 +235,9 @@ def print_info() -> None: print("OS version:", platform.version()) - import subprocess import re + import subprocess + # stack overflow #4842448 print("---------DEVICE---------") if platform.system() == "Windows": diff --git a/tutorials/feature/batch.ipynb b/tutorials/feature/batch.ipynb index 350fecc..68f64c0 100644 --- a/tutorials/feature/batch.ipynb +++ b/tutorials/feature/batch.ipynb @@ -63,11 +63,11 @@ " Batch size: [3]\n", "\n", " # 0:\n", - "[ 0.95+0.j 0. -0.12j 0. -0.28j -0.03+0.j ]\n", + "[ 0.95+0.j 0. -0.29j 0. -0.14j -0.04+0.j ]\n", " # 1:\n", - "[ 0.97+0.j 0. -0.23j 0. -0.08j -0.02+0.j ]\n", + "[ 0.87+0.j 0. -0.46j 0. -0.17j -0.09+0.j ]\n", " # 2:\n", - "[ 0.98+0.j 0. -0.14j 0. -0.16j -0.02+0.j ]\n", + "[ 0.98+0.j 0. -0.05j 0. -0.19j -0.01+0.j ]\n", "---------------------------------------------------\n", "\n" ] @@ -114,11 +114,11 @@ " Batch size: [3]\n", "\n", " # 0:\n", - "[0.61-0.4j 0. +0.j 0.54-0.42j 0. +0.j ]\n", + "[-0.41+0.38j 0. +0.j -0.11-0.82j 0. +0.j ]\n", " # 1:\n", - "[ 0.08+0.06j 0. +0.j -0.99+0.03j 0. +0.j ]\n", + "[0.51+0.52j 0. +0.j 0.69+0.06j 0. +0.j ]\n", " # 2:\n", - "[ 0.03-0.74j 0. +0.j -0.61+0.29j 0. +0.j ]\n", + "[ 0.84-0.01j 0. +0.j -0.25+0.48j 0. +0.j ]\n", "---------------------------------------------------\n", "\n" ] @@ -163,20 +163,20 @@ " Batch size: [3]\n", "\n", " # 0:\n", - "[[0.45+0.j 0. +0.j 0.25+0.43j 0. +0.j ]\n", + "[[0.66+0.j 0. +0.j 0.3 -0.j 0. +0.j]\n", + " [0. +0.j 0. +0.j 0. +0.j 0. +0.j]\n", + " [0.3 +0.j 0. +0.j 0.34+0.j 0. +0.j]\n", + " [0. +0.j 0. +0.j 0. +0.j 0. +0.j]]\n", + " # 1:\n", + "[[0.82+0.j 0. +0.j 0.3 -0.08j 0. +0.j ]\n", " [0. +0.j 0. +0.j 0. +0.j 0. +0.j ]\n", - " [0.25-0.43j 0. +0.j 0.55+0.j 0. +0.j ]\n", + " [0.3 +0.08j 0. +0.j 0.18+0.j 0. +0.j ]\n", " [0. +0.j 0. +0.j 0. +0.j 0. +0.j ]]\n", - " # 1:\n", - "[[ 0.23+0.j 0. +0.j -0.21-0.37j 0. +0.j ]\n", + " # 2:\n", + "[[ 0.35+0.j 0. +0.j -0.43+0.17j 0. +0.j ]\n", " [ 0. +0.j 0. +0.j 0. +0.j 0. +0.j ]\n", - " [-0.21+0.37j 0. +0.j 0.77+0.j 0. +0.j ]\n", + " [-0.43-0.17j 0. +0.j 0.65+0.j 0. +0.j ]\n", " [ 0. +0.j 0. +0.j 0. +0.j 0. +0.j ]]\n", - " # 2:\n", - "[[0.06+0.j 0. +0.j 0.09+0.21j 0. +0.j ]\n", - " [0. +0.j 0. +0.j 0. +0.j 0. +0.j ]\n", - " [0.09-0.21j 0. +0.j 0.94+0.j 0. +0.j ]\n", - " [0. +0.j 0. +0.j 0. +0.j 0. +0.j ]]\n", "---------------------------------------------------\n", "\n" ] @@ -213,20 +213,20 @@ " Batch size: [3]\n", "\n", " # 0:\n", - "[[0.4 +0.j 0.06+0.35j 0. +0.j 0. +0.j ]\n", - " [0.06-0.35j 0.6 +0.j 0. +0.j 0. +0.j ]\n", + "[[0.07+0.j 0.11-0.24j 0. +0.j 0. +0.j ]\n", + " [0.11+0.24j 0.93+0.j 0. +0.j 0. +0.j ]\n", " [0. +0.j 0. +0.j 0. +0.j 0. +0.j ]\n", " [0. +0.j 0. +0.j 0. +0.j 0. +0.j ]]\n", " # 1:\n", - "[[0.45+0.j 0.08-0.05j 0. +0.j 0. +0.j ]\n", - " [0.08+0.05j 0.55+0.j 0. +0.j 0. +0.j ]\n", - " [0. +0.j 0. +0.j 0. +0.j 0. +0.j ]\n", - " [0. +0.j 0. +0.j 0. +0.j 0. +0.j ]]\n", + "[[ 0.54+0.j -0.44-0.23j 0. +0.j 0. +0.j ]\n", + " [-0.44+0.23j 0.46+0.j 0. +0.j 0. +0.j ]\n", + " [ 0. +0.j 0. +0.j 0. +0.j 0. +0.j ]\n", + " [ 0. +0.j 0. +0.j 0. +0.j 0. +0.j ]]\n", " # 2:\n", - "[[ 0.22+0.j -0.21+0.3j 0. +0.j 0. +0.j ]\n", - " [-0.21-0.3j 0.78+0.j 0. +0.j 0. +0.j ]\n", - " [ 0. +0.j 0. +0.j 0. +0.j 0. +0.j ]\n", - " [ 0. +0.j 0. +0.j 0. +0.j 0. +0.j ]]\n", + "[[ 0.27+0.j -0.14+0.42j 0. +0.j 0. +0.j ]\n", + " [-0.14-0.42j 0.73+0.j 0. +0.j 0. +0.j ]\n", + " [ 0. +0.j 0. +0.j 0. +0.j 0. +0.j ]\n", + " [ 0. +0.j 0. +0.j 0. +0.j 0. +0.j ]]\n", "---------------------------------------------------\n", "\n" ] @@ -312,14 +312,14 @@ " Batch size: [3]\n", "\n", " # 0:\n", - "[[0.72+0.j 0.07-0.33j]\n", - " [0.07+0.33j 0.28+0.j ]]\n", + "[[0.44-0.j 0.04+0.05j]\n", + " [0.04-0.05j 0.56+0.j ]]\n", " # 1:\n", - "[[ 0.63+0.j -0.05-0.23j]\n", - " [-0.05+0.23j 0.37+0.j ]]\n", + "[[0.27+0.j 0.25+0.22j]\n", + " [0.25-0.22j 0.73+0.j ]]\n", " # 2:\n", - "[[ 0.6 -0.j -0.04-0.17j]\n", - " [-0.04+0.17j 0.4 +0.j ]]\n", + "[[0.45+0.j 0.13+0.03j]\n", + " [0.13-0.03j 0.55+0.j ]]\n", "---------------------------------------------------\n", "\n" ] @@ -359,7 +359,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Hamiltonian: [[0.158452521275237, 'X1'], [-0.06368870737400667, 'X0'], [0.6403721911006688, 'Z0']]\n" + "Hamiltonian: [[-0.09066232223158144, 'Z0'], [-0.7931476010024183, 'X1'], [0.7190023895147757, 'Y0']]\n" ] } ], @@ -392,27 +392,27 @@ " Batch size: [3]\n", "\n", " # 0:\n", - "[[0.45+0.j 0. +0.j 0.25+0.43j 0. +0.j ]\n", + "[[0.66+0.j 0. +0.j 0.3 -0.j 0. +0.j]\n", + " [0. +0.j 0. +0.j 0. +0.j 0. +0.j]\n", + " [0.3 +0.j 0. +0.j 0.34+0.j 0. +0.j]\n", + " [0. +0.j 0. +0.j 0. +0.j 0. +0.j]]\n", + " # 1:\n", + "[[0.82+0.j 0. +0.j 0.3 -0.08j 0. +0.j ]\n", " [0. +0.j 0. +0.j 0. +0.j 0. +0.j ]\n", - " [0.25-0.43j 0. +0.j 0.55+0.j 0. +0.j ]\n", + " [0.3 +0.08j 0. +0.j 0.18+0.j 0. +0.j ]\n", " [0. +0.j 0. +0.j 0. +0.j 0. +0.j ]]\n", - " # 1:\n", - "[[ 0.23+0.j 0. +0.j -0.21-0.37j 0. +0.j ]\n", + " # 2:\n", + "[[ 0.35+0.j 0. +0.j -0.43+0.17j 0. +0.j ]\n", " [ 0. +0.j 0. +0.j 0. +0.j 0. +0.j ]\n", - " [-0.21+0.37j 0. +0.j 0.77+0.j 0. +0.j ]\n", + " [-0.43-0.17j 0. +0.j 0.65+0.j 0. +0.j ]\n", " [ 0. +0.j 0. +0.j 0. +0.j 0. +0.j ]]\n", - " # 2:\n", - "[[0.06+0.j 0. +0.j 0.09+0.21j 0. +0.j ]\n", - " [0. +0.j 0. +0.j 0. +0.j 0. +0.j ]\n", - " [0.09-0.21j 0. +0.j 0.94+0.j 0. +0.j ]\n", - " [0. +0.j 0. +0.j 0. +0.j 0. +0.j ]]\n", "---------------------------------------------------\n", "\n", - "expectation value: tensor([-0.0933, -0.3136, -0.5813])\n", - "expectation value: tensor([-0.0933, -0.3136, -0.5813])\n", - "expectation value of each Pauli term: tensor([[ 0.0000, 0.0000, 0.0000],\n", - " [-0.0315, 0.0264, -0.0117],\n", - " [-0.0618, -0.3400, -0.5696]])\n" + "expectation value: tensor([-0.0246, 0.0647, -0.2120])\n", + "expectation value: tensor([-0.0246, 0.0647, -0.2120])\n", + "expectation value of each Pauli term: tensor([[-0.0292, -0.0574, 0.0271],\n", + " [-0.0000, -0.0000, -0.0000],\n", + " [ 0.0047, 0.1221, -0.2391]])\n" ] } ], @@ -448,12 +448,12 @@ "output_type": "stream", "text": [ "The shape of PVM: torch.Size([2, 2, 2])\n", - "expectation value: tensor([[0.0784, 0.9216],\n", - " [0.9952, 0.0048],\n", - " [0.4924, 0.5076]])\n", - "expectation value: tensor([[0.0784, 0.9216],\n", - " [0.9952, 0.0048],\n", - " [0.4924, 0.5076]])\n" + "expectation value: tensor([[0.3801, 0.6199],\n", + " [0.3868, 0.6132],\n", + " [0.4948, 0.5052]])\n", + "expectation value: tensor([[0.3801, 0.6199],\n", + " [0.3868, 0.6132],\n", + " [0.4948, 0.5052]])\n" ] } ], @@ -511,13 +511,13 @@ "text": [ "\n", "---------VERSION---------\n", - "quairkit: 0.1.0\n", - "torch: 2.3.1+cpu\n", + "quairkit: 0.2.0\n", + "torch: 2.4.1+cpu\n", "numpy: 1.26.0\n", - "scipy: 1.14.0\n", - "matplotlib: 3.9.0\n", + "scipy: 1.14.1\n", + "matplotlib: 3.9.2\n", "---------SYSTEM---------\n", - "Python version: 3.10.14\n", + "Python version: 3.10.15\n", "OS: Windows\n", "OS version: 10.0.26100\n", "---------DEVICE---------\n", @@ -532,7 +532,7 @@ ], "metadata": { "kernelspec": { - "display_name": "quair", + "display_name": "quair_test", "language": "python", "name": "python3" }, @@ -546,7 +546,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/tutorials/feature/custom.ipynb b/tutorials/feature/custom.ipynb index eca5297..a194902 100644 --- a/tutorials/feature/custom.ipynb +++ b/tutorials/feature/custom.ipynb @@ -51,7 +51,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -82,7 +82,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -137,7 +137,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -158,7 +158,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -190,16 +190,16 @@ "text": [ "The parameters of first two RY gates are\n", " [Parameter containing:\n", - "tensor([[[0.2486]],\n", + "tensor([[[0.4804]],\n", "\n", - " [[2.1932]]], requires_grad=True)]\n", + " [[0.0517]]], requires_grad=True)]\n", "The Kraus representations of the first two RY gates are: \n", - " tensor([[[[ 0.9923+0.j, -0.1240+0.j],\n", - " [ 0.1240+0.j, 0.9923+0.j]]],\n", + " tensor([[[[ 0.9713+0.j, -0.2379+0.j],\n", + " [ 0.2379+0.j, 0.9713+0.j]]],\n", "\n", "\n", - " [[[ 0.4566+0.j, -0.8897+0.j],\n", - " [ 0.8897+0.j, 0.4566+0.j]]]], grad_fn=)\n" + " [[[ 0.9997+0.j, -0.0258+0.j],\n", + " [ 0.0258+0.j, 0.9997+0.j]]]], grad_fn=)\n" ] } ], @@ -251,7 +251,7 @@ "text": [ "The parameters of this circuit are\n", " [Parameter containing:\n", - "tensor([[[3.8688, 3.8239, 0.0042]]], requires_grad=True)]\n" + "tensor([[[5.0111, 5.7265, 0.4270]]], requires_grad=True)]\n" ] } ], @@ -445,13 +445,13 @@ "text": [ "\n", "---------VERSION---------\n", - "quairkit: 0.1.0\n", - "torch: 2.3.1+cpu\n", + "quairkit: 0.2.0\n", + "torch: 2.4.1+cpu\n", "numpy: 1.26.0\n", - "scipy: 1.14.0\n", - "matplotlib: 3.9.0\n", + "scipy: 1.14.1\n", + "matplotlib: 3.9.2\n", "---------SYSTEM---------\n", - "Python version: 3.10.14\n", + "Python version: 3.10.15\n", "OS: Windows\n", "OS version: 10.0.26100\n", "---------DEVICE---------\n", @@ -466,7 +466,7 @@ ], "metadata": { "kernelspec": { - "display_name": "quair", + "display_name": "quair_test", "language": "python", "name": "python3" }, @@ -480,7 +480,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/tutorials/feature/qudit.ipynb b/tutorials/feature/qudit.ipynb index 81ad6cc..48790fa 100644 --- a/tutorials/feature/qudit.ipynb +++ b/tutorials/feature/qudit.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -60,7 +60,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -111,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -123,8 +123,8 @@ " Backend: state_vector\n", " System dimension: [2, 2, 2]\n", " System sequence: [2, 0, 1]\n", - "[ 0.01+0.j 0.2 +0.34j -0.25-0.2j 0.02+0.02j 0.8 -0.26j 0. -0.01j\n", - " -0.01+0.01j 0.07-0.13j]\n", + "[ 0.59+0.j -0.01-0.07j -0.19+0.26j -0.03-0.15j -0.06+0.04j -0.33-0.56j\n", + " 0.13+0.04j -0.11-0.26j]\n", "---------------------------------------------------\n", "\n" ] @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -166,24 +166,24 @@ " Backend: density_matrix\n", " System dimension: [3, 3]\n", " System sequence: [0, 1]\n", - "[[ 0.1 +0.j -0.05-0.03j -0.05+0.01j -0.02-0.j 0.03+0.08j -0.04+0.02j\n", - " -0.01+0.05j -0.01-0.01j -0.12-0.02j]\n", - " [-0.05+0.03j 0.06+0.j 0.04-0.01j 0.02-0.j -0.02+0.02j -0.01-0.j\n", - " -0.01-0.03j -0.03+0.01j 0.07-0.03j]\n", - " [-0.05-0.01j 0.04+0.01j 0.07+0.j 0.03+0.01j 0.06+0.j 0.03+0.06j\n", - " 0.03-0.05j 0.02+0.j 0.01+0.03j]\n", - " [-0.02+0.j 0.02+0.j 0.03-0.01j 0.02+0.j 0.02-0.j 0.02+0.02j\n", - " 0.01-0.03j 0. -0.01j 0.01+0.01j]\n", - " [ 0.03-0.08j -0.02-0.02j 0.06-0.j 0.02+0.j 0.23+0.j 0.06+0.15j\n", - " 0.04-0.02j 0.02+0.03j -0.11+0.12j]\n", - " [-0.04-0.02j -0.01+0.j 0.03-0.06j 0.02-0.02j 0.06-0.15j 0.14+0.j\n", - " -0.02-0.06j 0.03-0.04j 0.03+0.1j ]\n", - " [-0.01-0.05j -0.01+0.03j 0.03+0.05j 0.01+0.03j 0.04+0.02j -0.02+0.06j\n", - " 0.06+0.j 0.04+0.02j -0.03+0.03j]\n", - " [-0.01+0.01j -0.03-0.01j 0.02-0.j 0. +0.01j 0.02-0.03j 0.03+0.04j\n", - " 0.04-0.02j 0.11+0.j -0.03-0.03j]\n", - " [-0.12+0.02j 0.07+0.03j 0.01-0.03j 0.01-0.01j -0.11-0.12j 0.03-0.1j\n", - " -0.03-0.03j -0.03+0.03j 0.22+0.j ]]\n", + "[[ 0.11+0.j -0.04-0.04j 0. -0.04j -0.03+0.01j -0. +0.03j 0. +0.02j\n", + " -0. -0.01j -0. -0.02j -0.01-0.03j]\n", + " [-0.04+0.04j 0.1 +0.j -0.02-0.06j -0.01-0.j -0. +0.02j 0.02+0.01j\n", + " -0.01+0.02j -0.01-0.02j 0.01-0.01j]\n", + " [ 0. +0.04j -0.02+0.06j 0.23+0.j 0.04-0.01j -0.03+0.j -0.08+0.03j\n", + " -0. +0.01j 0.03-0.02j -0.01-0.03j]\n", + " [-0.03-0.01j -0.01+0.j 0.04+0.01j 0.1 +0.j 0.01-0.03j 0.02+0.j\n", + " 0.01-0.01j 0.04+0.01j -0.03+0.01j]\n", + " [-0. -0.03j -0. -0.02j -0.03-0.j 0.01+0.03j 0.11+0.j -0.02-0.02j\n", + " 0. +0.01j -0.01+0.03j -0.04+0.02j]\n", + " [ 0. -0.02j 0.02-0.01j -0.08-0.03j 0.02-0.j -0.02+0.02j 0.11+0.j\n", + " 0.02-0.01j -0.01-0.01j -0.01-0.01j]\n", + " [-0. +0.01j -0.01-0.02j -0. -0.01j 0.01+0.01j 0. -0.01j 0.02+0.01j\n", + " 0.09+0.j -0. +0.03j -0.03+0.01j]\n", + " [-0. +0.02j -0.01+0.02j 0.03+0.02j 0.04-0.01j -0.01-0.03j -0.01+0.01j\n", + " -0. -0.03j 0.09+0.j 0.01+0.j ]\n", + " [-0.01+0.03j 0.01+0.01j -0.01+0.03j -0.03-0.01j -0.04-0.02j -0.01+0.01j\n", + " -0.03-0.01j 0.01-0.j 0.07+0.j ]]\n", "---------------------------------------------------\n", "\n" ] @@ -208,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -264,19 +264,19 @@ " Backend: density_matrix\n", " System dimension: [2, 3, 6]\n", " System sequence: [0, 1, 2]\n", - "[[ 0.04+0.j 0. +0.01j 0. -0.j ... 0.01-0.j -0.01+0.j\n", + "[[ 0.03+0.j 0. -0.01j -0. -0.01j ... 0.02+0.j -0.01-0.02j\n", " 0.01+0.j ]\n", - " [ 0. -0.01j 0.02+0.j -0. -0.j ... -0. -0.j -0. +0.j\n", + " [ 0. +0.01j 0.01+0.j 0. -0.j ... -0.01-0.j 0.01-0.01j\n", + " -0.01-0.j ]\n", + " [-0. +0.01j 0. +0.j 0.01+0.j ... -0.01+0.01j 0. -0.j\n", " 0. +0.j ]\n", - " [ 0. +0.j -0. +0.j 0.02+0.j ... 0.01-0.01j 0.01-0.j\n", - " 0.01-0.01j]\n", " ...\n", - " [ 0.01+0.j -0. +0.j 0.01+0.01j ... 0.04+0.j 0.01+0.01j\n", - " 0. +0.j ]\n", - " [-0.01-0.j -0. -0.j 0.01+0.j ... 0.01-0.01j 0.03+0.j\n", - " 0.01-0.j ]\n", - " [ 0.01-0.j 0. -0.j 0.01+0.01j ... 0. -0.j 0.01+0.j\n", - " 0.02+0.j ]]\n", + " [ 0.02-0.j -0.01+0.j -0.01-0.01j ... 0.04+0.j -0. +0.j\n", + " 0.01-0.01j]\n", + " [-0.01+0.02j 0.01+0.01j 0. +0.j ... -0. -0.j 0.03+0.j\n", + " -0. -0.01j]\n", + " [ 0.01-0.j -0.01+0.j 0. -0.j ... 0.01+0.01j -0. +0.01j\n", + " 0.03+0.j ]]\n", "---------------------------------------------------\n", "\n" ] @@ -305,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -355,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -367,7 +367,7 @@ }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -402,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -414,10 +414,10 @@ " Backend: density_matrix\n", " System dimension: [4]\n", " System sequence: [0]\n", - "[[ 0.1 +0.j -0.03-0.11j -0.02-0.11j 0.09-0.02j]\n", - " [-0.03+0.11j 0.63+0.j 0.2 +0.03j 0.13+0.15j]\n", - " [-0.02+0.11j 0.2 -0.03j 0.14+0.j 0.03+0.11j]\n", - " [ 0.09+0.02j 0.13-0.15j 0.03-0.11j 0.14+0.j ]]\n", + "[[ 0.36+0.j -0.05-0.01j -0.12-0.06j -0.02+0.21j]\n", + " [-0.05+0.01j 0.3 +0.j -0.1 +0.02j -0.16-0.06j]\n", + " [-0.12+0.06j -0.1 -0.02j 0.11+0.j 0.02-0.06j]\n", + " [-0.02-0.21j -0.16+0.06j 0.02+0.06j 0.23+0.j ]]\n", "---------------------------------------------------\n", "\n", "while the output state of first system is: \n", @@ -425,10 +425,10 @@ " Backend: density_matrix\n", " System dimension: [4]\n", " System sequence: [0]\n", - "[[ 0.1 +0.j -0.03-0.11j -0.02-0.11j 0.09-0.02j]\n", - " [-0.03+0.11j 0.63+0.j 0.2 +0.03j 0.13+0.15j]\n", - " [-0.02+0.11j 0.2 -0.03j 0.14+0.j 0.03+0.11j]\n", - " [ 0.09+0.02j 0.13-0.15j 0.03-0.11j 0.14+0.j ]]\n", + "[[ 0.36+0.j -0.05-0.01j -0.12-0.06j -0.02+0.21j]\n", + " [-0.05+0.01j 0.3 +0.j -0.1 +0.02j -0.16-0.06j]\n", + " [-0.12+0.06j -0.1 -0.02j 0.11+0.j 0.02-0.06j]\n", + " [-0.02-0.21j -0.16+0.06j 0.02+0.06j 0.23+0.j ]]\n", "---------------------------------------------------\n", "\n" ] @@ -458,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -473,25 +473,25 @@ " Batch size: [5]\n", "\n", " # 0:\n", - "[[ 0.31+0.j -0.08-0.17j -0.05-0.26j]\n", - " [-0.08+0.17j 0.32+0.j 0.33+0.04j]\n", - " [-0.05+0.26j 0.33-0.04j 0.37+0.j ]]\n", + "[[ 0.28+0.j -0.05+0.24j 0.02-0.11j]\n", + " [-0.05-0.24j 0.49+0.j -0.04-0.21j]\n", + " [ 0.02+0.11j -0.04+0.21j 0.23+0.j ]]\n", " # 1:\n", - "[[ 0.31+0.j -0.08-0.17j -0.05-0.26j]\n", - " [-0.08+0.17j 0.32+0.j 0.33+0.04j]\n", - " [-0.05+0.26j 0.33-0.04j 0.37+0.j ]]\n", + "[[ 0.28+0.j -0.05+0.24j 0.02-0.11j]\n", + " [-0.05-0.24j 0.49+0.j -0.04-0.21j]\n", + " [ 0.02+0.11j -0.04+0.21j 0.23+0.j ]]\n", " # 2:\n", - "[[ 0.31+0.j -0.08-0.17j -0.05-0.26j]\n", - " [-0.08+0.17j 0.32+0.j 0.33+0.04j]\n", - " [-0.05+0.26j 0.33-0.04j 0.37+0.j ]]\n", + "[[ 0.28+0.j -0.05+0.24j 0.02-0.11j]\n", + " [-0.05-0.24j 0.49+0.j -0.04-0.21j]\n", + " [ 0.02+0.11j -0.04+0.21j 0.23+0.j ]]\n", " # 3:\n", - "[[ 0.31+0.j -0.08-0.17j -0.05-0.26j]\n", - " [-0.08+0.17j 0.32+0.j 0.33+0.04j]\n", - " [-0.05+0.26j 0.33-0.04j 0.37+0.j ]]\n", + "[[ 0.28+0.j -0.05+0.24j 0.02-0.11j]\n", + " [-0.05-0.24j 0.49+0.j -0.04-0.21j]\n", + " [ 0.02+0.11j -0.04+0.21j 0.23+0.j ]]\n", " # 4:\n", - "[[ 0.31+0.j -0.08-0.17j -0.05-0.26j]\n", - " [-0.08+0.17j 0.32+0.j 0.33+0.04j]\n", - " [-0.05+0.26j 0.33-0.04j 0.37+0.j ]]\n", + "[[ 0.28+0.j -0.05+0.24j 0.02-0.11j]\n", + " [-0.05-0.24j 0.49+0.j -0.04-0.21j]\n", + " [ 0.02+0.11j -0.04+0.21j 0.23+0.j ]]\n", "---------------------------------------------------\n", "\n" ] @@ -519,24 +519,31 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The first state for obtaining measurement result '1' for the system 0 is \n", + "The first state for obtaining measurement result '1' for the system 0 is " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", "---------------------------------------------------\n", " Backend: state_vector\n", " System dimension: [2, 3, 6]\n", " System sequence: [0, 1, 2]\n", - "[-0.18+0.01j -0.05+0.05j -0.27+0.4j 0.03+0.03j -0.05+0.01j 0.04-0.19j\n", - " 0.06-0.05j 0.01-0.03j -0.01-0.22j -0.02-0.j 0.01-0.02j 0.03+0.08j\n", - " 0.04-0.11j -0.02-0.04j -0.17-0.26j -0.03+0.01j 0. -0.03j 0.1 +0.07j\n", - " 0.18-0.01j 0.05-0.05j 0.27-0.4j -0.03-0.03j 0.05-0.01j -0.04+0.19j\n", - " -0.06+0.05j -0.01+0.03j 0.01+0.22j 0.02+0.j -0.01+0.02j -0.03-0.08j\n", - " -0.04+0.11j 0.02+0.04j 0.17+0.26j 0.03-0.01j -0. +0.03j -0.1 -0.07j]\n", + "[ 0.14-0.18j -0.02+0.13j 0.05-0.21j -0. -0.03j 0.05-0.1j -0.05-0.j\n", + " 0.09-0.18j 0. +0.11j 0.01-0.19j -0. -0.02j 0.03-0.09j -0.04+0.01j\n", + " 0.22+0.24j -0.18-0.06j 0.28+0.13j 0.04+0.01j 0.13+0.09j 0.01-0.07j\n", + " -0.14+0.18j 0.02-0.13j -0.05+0.21j 0. +0.03j -0.05+0.1j 0.05+0.j\n", + " -0.09+0.18j -0. -0.11j -0.01+0.19j 0. +0.02j -0.03+0.09j 0.04-0.01j\n", + " -0.22-0.24j 0.18+0.06j -0.28-0.13j -0.04-0.01j -0.13-0.09j -0.01+0.07j]\n", "---------------------------------------------------\n", "\n", "The first state for obtaining measurement result '2' for the system 1, and '4' for the system 2 is \n", @@ -544,12 +551,12 @@ " Backend: state_vector\n", " System dimension: [2, 3, 6]\n", " System sequence: [0, 1, 2]\n", - "[0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j\n", - " 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j\n", - " 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0.63-0.28j 0. +0.j\n", - " 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j\n", - " 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j\n", - " 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0.52+0.5j 0. +0.j ]\n", + "[ 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j\n", + " 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j\n", + " 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0.34+0.47j 0. +0.j\n", + " 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j\n", + " 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j 0. +0.j\n", + " 0. +0.j 0. +0.j 0. +0.j 0. +0.j -0.74-0.33j 0. +0.j ]\n", "---------------------------------------------------\n", "\n" ] @@ -579,14 +586,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "systems [1, 2] collapse to the state |1>|3> with (average) probability 0.07355416868519202\n", + "systems [1, 2] collapse to the state |1>|3> with (average) probability 0.009825415569497007\n", "\n", " After collapsing the second qudit to its first eigenstate and the third qudit to its third eigenstate the state of the first system is: \n", "---------------------------------------------------\n", @@ -596,20 +603,20 @@ " Batch size: [5]\n", "\n", " # 0:\n", - "[[0.2 +0.j 0.37+0.14j]\n", - " [0.37-0.14j 0.8 +0.j ]]\n", + "[[0.95+0.j 0.08+0.19j]\n", + " [0.08-0.19j 0.05+0.j ]]\n", " # 1:\n", - "[[0.2 +0.j 0.37+0.14j]\n", - " [0.37-0.14j 0.8 +0.j ]]\n", + "[[0.95+0.j 0.08+0.19j]\n", + " [0.08-0.19j 0.05+0.j ]]\n", " # 2:\n", - "[[0.2 +0.j 0.37+0.14j]\n", - " [0.37-0.14j 0.8 +0.j ]]\n", + "[[0.95+0.j 0.08+0.19j]\n", + " [0.08-0.19j 0.05+0.j ]]\n", " # 3:\n", - "[[0.2 +0.j 0.37+0.14j]\n", - " [0.37-0.14j 0.8 +0.j ]]\n", + "[[0.95+0.j 0.08+0.19j]\n", + " [0.08-0.19j 0.05+0.j ]]\n", " # 4:\n", - "[[0.2 +0.j 0.37+0.14j]\n", - " [0.37-0.14j 0.8 +0.j ]]\n", + "[[0.95+0.j 0.08+0.19j]\n", + " [0.08-0.19j 0.05+0.j ]]\n", "---------------------------------------------------\n", "\n" ] @@ -640,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -649,13 +656,13 @@ "text": [ "\n", "---------VERSION---------\n", - "quairkit: 0.1.0\n", - "torch: 2.3.1+cpu\n", + "quairkit: 0.2.0\n", + "torch: 2.4.1+cpu\n", "numpy: 1.26.0\n", - "scipy: 1.14.0\n", - "matplotlib: 3.9.0\n", + "scipy: 1.14.1\n", + "matplotlib: 3.9.2\n", "---------SYSTEM---------\n", - "Python version: 3.10.14\n", + "Python version: 3.10.15\n", "OS: Windows\n", "OS version: 10.0.26100\n", "---------DEVICE---------\n", @@ -670,7 +677,7 @@ ], "metadata": { "kernelspec": { - "display_name": "quair", + "display_name": "quair_test", "language": "python", "name": "python3" }, @@ -684,7 +691,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/tutorials/introduction/Hamiltonian.ipynb b/tutorials/introduction/Hamiltonian.ipynb index 14dd8f7..ef664ac 100644 --- a/tutorials/introduction/Hamiltonian.ipynb +++ b/tutorials/introduction/Hamiltonian.ipynb @@ -127,9 +127,9 @@ "0.5 Z1, Z2\n", "----------------------------------------------------------------------------------------------------\n", "The Pauli decomposition of the random Hamiltonian is:\n", - " 0.8979110061506057 Z0, Z1\n", - "0.34592743506578594 Z0, Y1, X2\n", - "0.05319839079740385 X2\n", + " 0.30402787550624577 Z1, Z2\n", + "-0.44363024944835594 X0, Y1, X2\n", + "0.6638237762231716 Y0, Z1, X2\n", "----------------------------------------------------------------------------------------------------\n" ] } @@ -217,9 +217,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Expection value of the Hamiltonian: tensor(-0.0039)\n", + "Expection value of the Hamiltonian: tensor(-0.0484)\n", "----------------------------------------------------------------------------------------------------\n", - "Expection value of the Hamiltonian: tensor(-0.0039)\n" + "Expection value of the Hamiltonian: tensor(-0.0484)\n" ] } ], @@ -248,12 +248,12 @@ "output_type": "stream", "text": [ "For 1000 random 3-qubit states, a set of Expection value of a given Hamiltonian:\n", - " tensor([-0.0149, 0.0714, 0.0518, -0.3050, -0.0510, -0.2239, 0.1357, -0.1817,\n", - " 0.0769, 0.1664])\n", + " tensor([-0.0007, -0.0193, -0.0598, -0.0859, -0.1097, -0.1909, 0.0444, 0.0678,\n", + " -0.1136, 0.0709])\n", "----------------------------------------------------------------------------------------------------\n", "For 1000 random 3-qubit states, a set of Expection value of a given Hamiltonian:\n", - " tensor([-0.0149, 0.0714, 0.0518, -0.3050, -0.0510, -0.2239, 0.1357, -0.1817,\n", - " 0.0769, 0.1664])\n" + " tensor([-0.0007, -0.0193, -0.0598, -0.0859, -0.1097, -0.1909, 0.0444, 0.0678,\n", + " -0.1136, 0.0709])\n" ] } ], @@ -317,37 +317,37 @@ "output_type": "stream", "text": [ "The Pauli decomposition of the random Hamiltonian is:\n", - " 0.8608537228435211 X0\n", - "-0.07604062649501331 Y0, Z1, X2\n", - "0.7978893154846145 X0, Y2\n", + " 0.6087798181466624 Y1, X2\n", + "-0.8219269134530314 Z0, Z1, Z2\n", + "-0.7649579970411409 Y0, Y1, Y2\n", "----------------------------------------------------------------------------------------------------\n", "Number of terms: 3\n", "----------------------------------------------------------------------------------------------------\n", "The Pauli string corresponding to the Hamiltonian:\n", - " [[0.8608537228435211, 'X0'], [-0.07604062649501331, 'Y0,Z1,X2'], [0.7978893154846145, 'X0,Y2']]\n", + " [[0.6087798181466624, 'Y1,X2'], [-0.8219269134530314, 'Z0,Z1,Z2'], [-0.7649579970411409, 'Y0,Y1,Y2']]\n", "----------------------------------------------------------------------------------------------------\n", "The coefficients of the terms in the Hamiltonian:\n", - " [0.8608537228435211, -0.07604062649501331, 0.7978893154846145]\n", + " [0.6087798181466624, -0.8219269134530314, -0.7649579970411409]\n", "----------------------------------------------------------------------------------------------------\n", "The matrix form of the Hamiltonian:\n", - " tensor([[0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j,\n", - " 0.8609+0.0000j, 0.0000-0.7218j, 0.0000+0.0000j, 0.0000+0.0000j],\n", - " [0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j,\n", - " 0.0000+0.8739j, 0.8609+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j],\n", - " [0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j,\n", - " 0.0000+0.0000j, 0.0000+0.0000j, 0.8609+0.0000j, 0.0000-0.8739j],\n", - " [0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j,\n", - " 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.7218j, 0.8609+0.0000j],\n", - " [0.8609+0.0000j, 0.0000-0.8739j, 0.0000+0.0000j, 0.0000+0.0000j,\n", - " 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j],\n", - " [0.0000+0.7218j, 0.8609+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j,\n", - " 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j],\n", - " [0.0000+0.0000j, 0.0000+0.0000j, 0.8609+0.0000j, 0.0000-0.7218j,\n", - " 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j],\n", - " [0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.8739j, 0.8609+0.0000j,\n", - " 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j]])\n", + " tensor([[-0.8219+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000-0.6088j,\n", + " 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000-0.7650j],\n", + " [ 0.0000+0.0000j, 0.8219+0.0000j, 0.0000-0.6088j, 0.0000+0.0000j,\n", + " 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.7650j, 0.0000+0.0000j],\n", + " [ 0.0000+0.0000j, 0.0000+0.6088j, 0.8219+0.0000j, 0.0000+0.0000j,\n", + " 0.0000+0.0000j, 0.0000+0.7650j, 0.0000+0.0000j, 0.0000+0.0000j],\n", + " [ 0.0000+0.6088j, 0.0000+0.0000j, 0.0000+0.0000j, -0.8219+0.0000j,\n", + " 0.0000-0.7650j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j],\n", + " [ 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.7650j,\n", + " 0.8219+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000-0.6088j],\n", + " [ 0.0000+0.0000j, 0.0000+0.0000j, 0.0000-0.7650j, 0.0000+0.0000j,\n", + " 0.0000+0.0000j, -0.8219+0.0000j, 0.0000-0.6088j, 0.0000+0.0000j],\n", + " [ 0.0000+0.0000j, 0.0000-0.7650j, 0.0000+0.0000j, 0.0000+0.0000j,\n", + " 0.0000+0.0000j, 0.0000+0.6088j, -0.8219+0.0000j, 0.0000+0.0000j],\n", + " [ 0.0000+0.7650j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j,\n", + " 0.0000+0.6088j, 0.0000+0.0000j, 0.0000+0.0000j, 0.8219+0.0000j]])\n", "----------------------------------------------------------------------------------------------------\n", - "The Pauli word of each term: ['XII', 'YZX', 'XIY']\n", + "The Pauli word of each term: ['IYX', 'ZZZ', 'YYY']\n", "----------------------------------------------------------------------------------------------------\n", "Number of qubits in the Hamiltonian: 3\n", "----------------------------------------------------------------------------------------------------\n" @@ -422,13 +422,13 @@ "text": [ "\n", "---------VERSION---------\n", - "quairkit: 0.1.0\n", - "torch: 2.3.1+cpu\n", + "quairkit: 0.2.0\n", + "torch: 2.4.1+cpu\n", "numpy: 1.26.0\n", - "scipy: 1.14.0\n", - "matplotlib: 3.9.0\n", + "scipy: 1.14.1\n", + "matplotlib: 3.9.2\n", "---------SYSTEM---------\n", - "Python version: 3.10.14\n", + "Python version: 3.10.15\n", "OS: Windows\n", "OS version: 10.0.26100\n", "---------DEVICE---------\n", @@ -443,7 +443,7 @@ ], "metadata": { "kernelspec": { - "display_name": "quair", + "display_name": "quair_test", "language": "python", "name": "python3" }, @@ -457,7 +457,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/tutorials/introduction/circuit.ipynb b/tutorials/introduction/circuit.ipynb index 83975a4..3a7f781 100644 --- a/tutorials/introduction/circuit.ipynb +++ b/tutorials/introduction/circuit.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -88,7 +88,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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" ] @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -177,7 +177,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -218,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -230,7 +230,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -275,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -287,7 +287,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -315,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -327,7 +327,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -356,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -368,7 +368,7 @@ }, { "data": { - "image/png": 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", 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" ] @@ -395,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -414,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -438,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -470,7 +470,7 @@ ")" ] }, - "execution_count": 13, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -507,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -519,7 +519,7 @@ }, { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -555,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -563,11 +563,11 @@ "output_type": "stream", "text": [ "The matrix of the gate at the 5th position is\n", - " tensor([[[-0.2045+0.j, -0.9789+0.j],\n", - " [ 0.9789+0.j, -0.2045+0.j]],\n", + " tensor([[[ 0.7492+0.j, -0.6624+0.j],\n", + " [ 0.6624+0.j, 0.7492+0.j]],\n", "\n", - " [[ 0.0607+0.j, -0.9982+0.j],\n", - " [ 0.9982+0.j, 0.0607+0.j]]], grad_fn=)\n" + " [[ 0.8098+0.j, -0.5868+0.j],\n", + " [ 0.5868+0.j, 0.8098+0.j]]], grad_fn=)\n" ] } ], @@ -591,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -603,7 +603,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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", 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", 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g77zIjxRFIQCkKIqm5fv6+ggAXbx4kYiIhoaGyGw2U3Z2tteyZ86codTUVBodHVWn7d+/n+bPn083btygoaEh2rRpE2VmZnosM5HDhw9TfHw8/e9//xs3791336XCwkICQC0tLR7znn/+eWpoaPBa/xhf2+hpys2fP5/0ej1FRUWRJEm0atUqamtr81ouMzOTPvroI3V8dHSUduzYQXPnzqWoqChau3YtdXd3T1pHXV0dhYaGUlRUlDps2LBBnb93717KyMigmJgYio+Pp8LCwnHbdvXqVUpKSnri/8mTTLVtpyqow/Xxxx+TTqcjp9OpTtu7dy+lpKR4LbtlyxZ67bXXPKaZTCaqr69Xx/v7+0mn09Fnn33mtb7FixdTTU3NuOl37tyhtLQ0stlsTwxXXV0dvfTSS17rH+OvcE2145quTquuro5Wr1494fzOzk5yOBxERPTw4UM6ePAgGY3GcXX60nn5O1xBfVl46dIlZGVlQa/Xw+1248KFC6ivr0dZWZnXsleuXMGyZcvUcUVRYLPZsGLFCnWaLMtYsGCB18vMs2fP4j//+Q+2bNniMZ2IUFFRgV27dmHevHlPLJuZmQmr1ep1e/3NarVCp9MhOzsbACBJElatWoWenp5Jy508eRIFBQXqpTYAHD16FDU1NVi0aBEkScKBAwfQ2dmJ8+fPT3n7Fi5ciNjYWACP2jksLAx2ux2KongsV1hYOOGleqAFdbisViva29shyzL0ej3y8/OxY8cO7N+/32vZ/v5+GAwGdXzs3UyyLHssJ8uy1/c21dfXw2w2Iz093WP6kSNHQETYvHnzhGVjYmLgcDi8bq+/TbXjms5Oq7W1FYmJiTCZTCgtLUVXV5fH/NOnT0OWZYSHh6O6uhrV1dVq4MYEa+cFzIBwNTQ0YGBgAD09PVi5ciXa2toQEhLitWxcXJxHLzf2jNN3e76BgYFJn3/673//i1OnTmHr1q0e02/duoW9e/fi2LFjk27H4OAg4uLivG6vv02145quTuvll19GR0cHent78fnnn2POnDkoKCjA8PCwukxRUREGBgZw//59HDp0CLm5uePqCdbOC/DhYcnpeCufL3XYbDb09vaqly1xcXHYtWsXLBYLDh06hNjYWHzxxRd48803ceLECQDA66+/DovFArPZjJycHHR0dKj1GQwGmEwmtLa2qr2soii4desWsrKyJtyOt99+G2lpaXjxxRc9pv/zn//E/fv3kZOT4zHdYrGgtLQUR44cAQBcvXrVo1fXytf29nX5sY6rrKwMDocDFotFU8c1XZ3W42e/5ORkNDQ0wGAw4OLFiygsLBy3zqqqKsTGxmLx4sVYunSpOm8qndd0HMuaHkjV+uEMwLQNWj5QfvjhhxQVFeXxAXZkZIRkWVY/wD58+JCWLFlCRESXL1+mkpISddlPP/2U0tLSxn3wzsjIoM7OThoeHqbKyspJP3iPjIxQcnIy/e53vxs378GDB3Tnzh2PAQB98MEH6gdxIqLc3Fw6duyY1/0dM/ahW2Tbdnd3EwDq6OhQpzU3N5Ner1e3fXR0lAoKCmj16tWUk5Oj3kTaunUrlZeXe9RnMpnoyJEj6vjAwADp9XpNN4rGjIyMUGRkJDU3N084PyIighobGz2m7969m4qKijSt42nb9vFBC83hUhTlqYexA1DLAVBTU0O5ubnjpr/66qu0du1adTwvL4/sdjvl5+fTV1995bHs8uXLx90yrq2tpcTERIqMjKTCwkLq6upS51dWVpLZbFbH//KXv5Ber6e+vj5NbYTv3C3s6OiguXPn0jfffKOpPNH/HwB37twR1rZaOq4xfX19VFxcTNevXyei6em0iIj+9Kc/UW9vLxER9fT00GuvvUYmk4kGBweJ6NGfPu7du0dERL29vbRp0yaSZZnsdrtHPb50XlNt2ycNWgT1rXgttm3bRq+88grV1dWNm3f27Fl67rnnNP0dS4Ti4mJ65513fCrjj1vxWjuu27dvk8Viodu3b3ss52unRTS+41q3bh0lJCRQREQEJScn04YNG+jmzZvqfIvFQklJSRQZGUlGo5HWr19Ply9f9qjT186L/87lo8bGRpo/f77mPyQGO3/+EXky169fp+Li4ieetQPdaY3xtfPyd7j8+k7kwcFBGAwGKIoybb9QVF1djRdeeAHr16+flvoCbaptNN1tm5qaCqPRCEmSAADvv/8+UlJSnrreQBJx/E0mID+tNh3u3r2LN954Az/4wQ9mTbCCyd27dwO9CTPejA1XamoqmpqaAr0ZjE0oqP+IzNhMxuFiTBAOF2OCcLgYE4TDxZggHC7GBOFwMSYIh4sxQThcjAnC4WJMEA4XY4IE5LuF0/GY9Wz1tG3DbTsxf7eNX8Ol0+lgNBqRlpbmz9XOOEajETqdzqcy3LbaTKVtp8qvz3MBgNPphMvl8ucqZxydTofw8HCfy3HbejfVtp0Kv4eLsWcF39BgTBAOF2OCcLgYE4TDxZggHC7GBOFwMSYIh4sxQThcjAnC4WJMEA4XY4JwuBgThMPFmCAcLsYE4XAxJsj/AVPT8JDnUsCoAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -662,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -674,8 +674,8 @@ " Backend: state_vector\n", " System dimension: [2, 2, 2]\n", " System sequence: [2, 1, 0]\n", - "[ 0.24-0.15j -0.54+0.34j 0. +0.j 0. +0.j -0.28+0.09j 0.62-0.2j\n", - " 0. +0.j 0. +0.j ]\n", + "[0.67-0.62j 0.13-0.12j 0. +0.j 0. +0.j 0.35-0.07j 0.07-0.01j\n", + " 0. +0.j 0. +0.j ]\n", "---------------------------------------------------\n", "\n", "the output state for inputting state rho is: \n", @@ -683,22 +683,22 @@ " Backend: density_matrix\n", " System dimension: [2, 2, 2]\n", " System sequence: [2, 1, 0]\n", - "[[ 0.07-0.j -0.07-0.01j 0.05+0.05j 0.02-0.03j 0.04+0.04j -0.04+0.02j\n", - " 0.03+0.01j 0.01+0.02j]\n", - " [-0.07+0.01j 0.15+0.j -0.11-0.03j -0.1 +0.11j -0.06-0.08j 0.01-0.12j\n", - " -0.07+0.03j -0.03+0.06j]\n", - " [ 0.05-0.05j -0.11+0.03j 0.14+0.j 0.02-0.04j 0.06+0.01j 0.01+0.1j\n", - " 0.03-0.05j 0.02-0.04j]\n", - " [ 0.02+0.03j -0.1 -0.11j 0.02+0.04j 0.22+0.j -0.05+0.1j -0.09+0.11j\n", - " 0.08+0.05j 0.1 -0.05j]\n", - " [ 0.04-0.04j -0.06+0.08j 0.06-0.01j -0.05-0.1j 0.08-0.j 0.05+0.04j\n", - " 0.01-0.05j -0.03-0.02j]\n", - " [-0.04-0.02j 0.01+0.12j 0.01-0.1j -0.09-0.11j 0.05-0.04j 0.16-0.j\n", - " -0.04-0.08j -0.11-0.05j]\n", - " [ 0.03-0.01j -0.07-0.03j 0.03+0.05j 0.08-0.05j 0.01+0.05j -0.04+0.08j\n", - " 0.06+0.j 0.05-0.04j]\n", - " [ 0.01-0.02j -0.03-0.06j 0.02+0.04j 0.1 +0.05j -0.03+0.02j -0.11+0.05j\n", - " 0.05+0.04j 0.1 +0.j ]]\n", + "[[ 0.13+0.j -0.03-0.03j -0.02+0.06j 0.02-0.01j -0.01+0.05j 0.04+0.1j\n", + " -0.04-0.03j 0.06-0.03j]\n", + " [-0.03+0.03j 0.18-0.j -0.03-0.02j -0.13+0.01j -0.07+0.j 0.01-0.01j\n", + " 0. -0.02j -0.09-0.02j]\n", + " [-0.02-0.06j -0.03+0.02j 0.1 +0.j 0.05+0.01j 0. -0.02j 0.02-0.03j\n", + " 0.04+0.03j -0. -0.03j]\n", + " [ 0.02+0.01j -0.13-0.01j 0.05-0.01j 0.15+0.j 0.03+0.01j -0.05+0.03j\n", + " 0.02-0.02j 0.08+0.03j]\n", + " [-0.01-0.05j -0.07-0.j 0. +0.02j 0.03-0.01j 0.11+0.j 0.04-0.01j\n", + " -0.06+0.04j 0.03-0.j ]\n", + " [ 0.04-0.1j 0.01+0.01j 0.02+0.03j -0.05-0.03j 0.04+0.01j 0.12+0.j\n", + " -0.05+0.04j -0.03-0.07j]\n", + " [-0.04+0.03j 0. +0.02j 0.04-0.03j 0.02+0.02j -0.06-0.04j -0.05-0.04j\n", + " 0.09+0.j -0.01+0.02j]\n", + " [ 0.06+0.03j -0.09+0.02j -0. +0.03j 0.08-0.03j 0.03+0.j -0.03+0.07j\n", + " -0.01-0.02j 0.13+0.j ]]\n", "---------------------------------------------------\n", "\n" ] @@ -724,7 +724,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -733,9 +733,9 @@ "text": [ "the circuit depth is 2 \n", "\n", - "the gate history of the circuit is [{'gate': 'ry', 'which_system': [0], 'theta': tensor([[3.9764]], grad_fn=)}, {'gate': 'ry', 'which_system': [2], 'theta': tensor([[4.6975]], grad_fn=)}, {'gate': 'rz', 'which_system': [1], 'theta': tensor([[0.8615]])}, {'gate': 'rz', 'which_system': [2], 'theta': tensor([[0.2529]])}] \n", + "the gate history of the circuit is [{'gate': 'ry', 'which_system': [0], 'theta': tensor([[0.3819]], grad_fn=)}, {'gate': 'ry', 'which_system': [2], 'theta': tensor([[0.7412]], grad_fn=)}, {'gate': 'rz', 'which_system': [1], 'theta': tensor([[0.9501]])}, {'gate': 'rz', 'which_system': [2], 'theta': tensor([[0.5337]])}] \n", "\n", - "the qubit history of the circuit is [[[{'gate': 'ry', 'which_system': [0], 'theta': tensor([[3.9764]], grad_fn=)}, 0]], [[{'gate': 'rz', 'which_system': [1], 'theta': tensor([[0.8615]])}, 2]], [[{'gate': 'ry', 'which_system': [2], 'theta': tensor([[4.6975]], grad_fn=)}, 1], [{'gate': 'rz', 'which_system': [2], 'theta': tensor([[0.2529]])}, 3]]]\n" + "the qubit history of the circuit is [[[{'gate': 'ry', 'which_system': [0], 'theta': tensor([[0.3819]], grad_fn=)}, 0]], [[{'gate': 'rz', 'which_system': [1], 'theta': tensor([[0.9501]])}, 2]], [[{'gate': 'ry', 'which_system': [2], 'theta': tensor([[0.7412]], grad_fn=)}, 1], [{'gate': 'rz', 'which_system': [2], 'theta': tensor([[0.5337]])}, 3]]]\n" ] } ], @@ -756,14 +756,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "the trainable parameters of entire circuit are tensor([3.9764, 4.6975])\n", + "the trainable parameters of entire circuit are tensor([0.3819, 0.7412])\n", "the updated trainable parameters of entire circuit are tensor([1., 1.])\n" ] } @@ -817,7 +817,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -826,13 +826,13 @@ "text": [ "\n", "---------VERSION---------\n", - "quairkit: 0.1.0\n", - "torch: 2.3.1+cpu\n", + "quairkit: 0.2.0\n", + "torch: 2.4.1+cpu\n", "numpy: 1.26.0\n", - "scipy: 1.14.0\n", - "matplotlib: 3.9.0\n", + "scipy: 1.14.1\n", + "matplotlib: 3.9.2\n", "---------SYSTEM---------\n", - "Python version: 3.10.14\n", + "Python version: 3.10.15\n", "OS: Windows\n", "OS version: 10.0.26100\n", "---------DEVICE---------\n", @@ -847,7 +847,7 @@ ], "metadata": { "kernelspec": { - "display_name": "quair", + "display_name": "quair_test", "language": "python", "name": "python3" }, @@ -861,7 +861,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/tutorials/introduction/measure.ipynb b/tutorials/introduction/measure.ipynb index 0ecb7c4..9b4a5a8 100644 --- a/tutorials/introduction/measure.ipynb +++ b/tutorials/introduction/measure.ipynb @@ -135,7 +135,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The probability distribution of outcome tensor([0.3603, 0.6397])\n" + "The probability distribution of outcome tensor([0.8449, 0.1551])\n" ] } ], @@ -170,15 +170,15 @@ " Batch size: [2]\n", "\n", " # 0:\n", - "[[0.25+0.j 0.13+0.02j 0.25+0.j 0.13+0.02j]\n", - " [0.13-0.02j 0.25+0.j 0.13-0.02j 0.25+0.j ]\n", - " [0.25+0.j 0.13+0.02j 0.25+0.j 0.13+0.02j]\n", - " [0.13-0.02j 0.25+0.j 0.13-0.02j 0.25+0.j ]]\n", + "[[ 0.3+0.j -0. -0.24j 0.3+0.j -0. -0.24j]\n", + " [-0. +0.24j 0.2+0.j -0. +0.24j 0.2+0.j ]\n", + " [ 0.3+0.j -0. -0.24j 0.3+0.j -0. -0.24j]\n", + " [-0. +0.24j 0.2+0.j -0. +0.24j 0.2+0.j ]]\n", " # 1:\n", - "[[ 0.16+0.j 0.03+0.22j -0.16+0.j -0.03-0.22j]\n", - " [ 0.03-0.22j 0.34+0.j -0.03+0.22j -0.34+0.j ]\n", - " [-0.16+0.j -0.03-0.22j 0.16+0.j 0.03+0.22j]\n", - " [-0.03+0.22j -0.34+0.j 0.03-0.22j 0.34+0.j ]]\n", + "[[ 0.15+0.j 0.19+0.09j -0.15+0.j -0.19-0.09j]\n", + " [ 0.19-0.09j 0.35+0.j -0.19+0.09j -0.35+0.j ]\n", + " [-0.15+0.j -0.19-0.09j 0.15+0.j 0.19+0.09j]\n", + " [-0.19+0.09j -0.35+0.j 0.19-0.09j 0.35+0.j ]]\n", "---------------------------------------------------\n", "\n" ] @@ -205,15 +205,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "The probability for obtaining outcome 1 is tensor([0.6397]), with outcome state \n", + "The probability for obtaining outcome 1 is tensor([0.1551]), with outcome state \n", "---------------------------------------------------\n", " Backend: density_matrix\n", " System dimension: [2, 2]\n", " System sequence: [0, 1]\n", - "[[ 0.16+0.j 0.03+0.22j -0.16+0.j -0.03-0.22j]\n", - " [ 0.03-0.22j 0.34+0.j -0.03+0.22j -0.34+0.j ]\n", - " [-0.16+0.j -0.03-0.22j 0.16+0.j 0.03+0.22j]\n", - " [-0.03+0.22j -0.34+0.j 0.03-0.22j 0.34+0.j ]]\n", + "[[ 0.15+0.j 0.19+0.09j -0.15+0.j -0.19-0.09j]\n", + " [ 0.19-0.09j 0.35+0.j -0.19+0.09j -0.35+0.j ]\n", + " [-0.15+0.j -0.19-0.09j 0.15+0.j 0.19+0.09j]\n", + " [-0.19+0.09j -0.35+0.j 0.19-0.09j 0.35+0.j ]]\n", "---------------------------------------------------\n", "\n" ] @@ -246,7 +246,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The probability distribution of outcome tensor([0.4162, 0.5838])\n", + "The probability distribution of outcome tensor([0.5878, 0.4122])\n", "The collapsed state for each outcome is \n", "---------------------------------------------------\n", " Backend: density_matrix\n", @@ -300,8 +300,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Time for measuring with pvm: 0.0017409325s\n", - "Time for measuring with povm: 0.0000000000s\n" + "Time for measuring with pvm: 0.0069618225s\n", + "Time for measuring with povm: 0.0017402172s\n" ] }, { @@ -309,9 +309,9 @@ "output_type": "stream", "text": [ "Traceback (most recent call last):\n", - " File \"C:\\Users\\Cloud\\AppData\\Local\\Temp\\ipykernel_27996\\2671688636.py\", line 11, in \n", + " File \"C:\\Users\\Cloud\\AppData\\Local\\Temp\\ipykernel_24616\\2671688636.py\", line 11, in \n", " rho.measure(pvm, is_povm=True, keep_state=True)\n", - " File \"c:\\users\\cloud\\quairkit-dev\\quairkit\\core\\state\\backend\\__init__.py\", line 633, in measure\n", + " File \"c:\\Users\\Cloud\\anaconda3\\envs\\quair_test\\lib\\site-packages\\quairkit\\core\\state\\backend\\__init__.py\", line 633, in measure\n", " raise ValueError(\n", "ValueError: `is_povm` and `keep_state` cannot be both True, since a general POVM does not distinguish states.\n" ] @@ -424,7 +424,7 @@ "[[ 0.5+0.j -0.5+0.j]\n", " [-0.5+0.j 0.5+0.j]]\n", "---------------------------------------------------\n", - " with prob distribution tensor([0.4162, 0.5838])\n" + " with prob distribution tensor([0.5878, 0.4122])\n" ] } ], @@ -455,17 +455,19 @@ "text": [ "The measured states for the first batch is \n", "---------------------------------------------------\n", - " Backend: state_vector\n", + " Backend: density_matrix\n", " System dimension: [2]\n", " System sequence: [0]\n", " Batch size: [2]\n", "\n", " # 0:\n", - "[-0.36+0.61j -0.36+0.61j]\n", + "[[0.5+0.j 0.5+0.j]\n", + " [0.5+0.j 0.5+0.j]]\n", " # 1:\n", - "[-0.63-0.32j 0.63+0.32j]\n", + "[[ 0.5+0.j -0.5+0.j]\n", + " [-0.5+0.j 0.5+0.j]]\n", "---------------------------------------------------\n", - " with prob distribution tensor([0.0371, 0.9629])\n" + " with prob distribution tensor([0.6556, 0.3444])\n" ] } ], @@ -515,18 +517,18 @@ "output_type": "stream", "text": [ "3 probability distributions are\n", - " tensor([[0.0371, 0.9629],\n", - " [0.9121, 0.0879],\n", - " [0.4576, 0.5424]])\n", + " tensor([[0.6556, 0.3444],\n", + " [0.7414, 0.2586],\n", + " [0.8497, 0.1503]])\n", "\n", "The outcomes of quantum measurements:\n", - "{'0': tensor([ 41, 927, 471]), '1': tensor([983, 97, 553])}\n", + "{'0': tensor([657, 753, 860]), '1': tensor([367, 271, 164])}\n", "\n", "The outcomes of quantum measurements with the decimal system of dictionary system:\n", - " {'0': tensor([ 36, 925, 459]), '1': tensor([988, 99, 565])}\n", + " {'0': tensor([658, 753, 864]), '1': tensor([366, 271, 160])}\n", "\n", "The outcomes of quantum measurements in proportion:\n", - " {'0': tensor([0.0420, 0.9121, 0.4375]), '1': tensor([0.9580, 0.0879, 0.5625])}\n" + " {'0': tensor([0.6494, 0.7432, 0.8350]), '1': tensor([0.3506, 0.2568, 0.1650])}\n" ] } ], @@ -587,13 +589,13 @@ "text": [ "\n", "---------VERSION---------\n", - "quairkit: 0.1.0\n", - "torch: 2.3.1+cpu\n", + "quairkit: 0.2.0\n", + "torch: 2.4.1+cpu\n", "numpy: 1.26.0\n", - "scipy: 1.14.0\n", - "matplotlib: 3.9.0\n", + "scipy: 1.14.1\n", + "matplotlib: 3.9.2\n", "---------SYSTEM---------\n", - "Python version: 3.10.14\n", + "Python version: 3.10.15\n", "OS: Windows\n", "OS version: 10.0.26100\n", "---------DEVICE---------\n", @@ -608,7 +610,7 @@ ], "metadata": { "kernelspec": { - "display_name": "quair", + "display_name": "quair_test", "language": "python", "name": "python3" }, @@ -622,7 +624,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/tutorials/introduction/operator.ipynb b/tutorials/introduction/operator.ipynb index a098381..02463c7 100644 --- a/tutorials/introduction/operator.ipynb +++ b/tutorials/introduction/operator.ipynb @@ -297,17 +297,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "The matrix form of x-axis rotation gate with parameter 4.512 is\n", - "tensor([[-0.6328+0.0000j, 0.0000-0.7743j],\n", - " [ 0.0000-0.7743j, -0.6328+0.0000j]])\n", + "The matrix form of x-axis rotation gate with parameter 2.537 is\n", + "tensor([[0.2977+0.0000j, 0.0000-0.9547j],\n", + " [0.0000-0.9547j, 0.2977+0.0000j]])\n", "\n", - "The matrix form of y-axis rotation gate with parameter 4.512 is\n", - "tensor([[-0.6328+0.j, -0.7743+0.j],\n", - " [ 0.7743+0.j, -0.6328+0.j]])\n", + "The matrix form of y-axis rotation gate with parameter 2.537 is\n", + "tensor([[ 0.2977+0.j, -0.9547+0.j],\n", + " [ 0.9547+0.j, 0.2977+0.j]])\n", "\n", - "The matrix form of z-axis rotation gate with parameter 4.512 is\n", - "tensor([[-0.6328-0.7743j, 0.0000+0.0000j],\n", - " [ 0.0000+0.0000j, -0.6328+0.7743j]])\n" + "The matrix form of z-axis rotation gate with parameter 2.537 is\n", + "tensor([[0.2977-0.9547j, 0.0000+0.0000j],\n", + " [0.0000+0.0000j, 0.2977+0.9547j]])\n" ] } ], @@ -434,12 +434,12 @@ "output_type": "stream", "text": [ "pure output state after applying a unitary:\n", - " tensor([[-0.0208+0.4851j],\n", - " [-0.2760-0.8295j]])\n", + " tensor([[0.4696+0.0464j],\n", + " [0.5735+0.6696j]])\n", "\n", "mixed output state after applying a unitary:\n", - " tensor([[ 0.8229+2.7756e-17j, -0.1768+3.2594e-01j],\n", - " [-0.1768-3.2594e-01j, 0.1771+2.7756e-17j]])\n" + " tensor([[ 0.3775+1.3878e-17j, -0.1887+6.7681e-02j],\n", + " [-0.1887-6.7681e-02j, 0.6225-1.3878e-17j]])\n" ] } ], @@ -471,14 +471,10 @@ "output_type": "stream", "text": [ "state after applying a unitary on the first qubit:\n", - " tensor([[ 0.3059-2.0817e-17j, 0.0096+4.5260e-02j, -0.0447-1.0331e-01j,\n", - " -0.1121-1.9699e-02j],\n", - " [ 0.0096-4.5260e-02j, 0.2093+1.7347e-18j, -0.1597-6.9293e-02j,\n", - " 0.0745-9.0401e-02j],\n", - " [-0.0447+1.0331e-01j, -0.1597+6.9293e-02j, 0.3303-6.9389e-18j,\n", - " 0.0124+6.7292e-02j],\n", - " [-0.1121+1.9699e-02j, 0.0745+9.0401e-02j, 0.0124-6.7292e-02j,\n", - " 0.1545+0.0000e+00j]])\n" + " tensor([[ 0.1232+0.0000j, -0.2102+0.0393j, -0.0415+0.1123j, 0.0775-0.2049j],\n", + " [-0.2102-0.0393j, 0.3710+0.0000j, 0.1065-0.1784j, -0.1975+0.3248j],\n", + " [-0.0415-0.1123j, 0.1065+0.1784j, 0.1163+0.0000j, -0.2128-0.0017j],\n", + " [ 0.0775+0.2049j, -0.1975-0.3248j, -0.2128+0.0017j, 0.3894+0.0000j]])\n" ] } ], @@ -515,20 +511,20 @@ " Batch size: [5]\n", "\n", " # 0:\n", - "[[ 0.38+0.j -0.03+0.34j]\n", - " [-0.03-0.34j 0.62+0.j ]]\n", + "[[0.86-0.j 0.26-0.16j]\n", + " [0.26+0.16j 0.14-0.j ]]\n", " # 1:\n", - "[[ 0.3 -0.j -0.35-0.27j]\n", - " [-0.35+0.27j 0.7 -0.j ]]\n", + "[[ 0.78+0.j -0.09-0.23j]\n", + " [-0.09+0.23j 0.22+0.j ]]\n", " # 2:\n", - "[[0.45+0.j 0.3 -0.32j]\n", - " [0.3 +0.32j 0.55-0.j ]]\n", + "[[0.52+0.j 0.4 +0.03j]\n", + " [0.4 -0.03j 0.48+0.j ]]\n", " # 3:\n", - "[[ 0.33+0.j -0.03-0.05j]\n", - " [-0.03+0.05j 0.67-0.j ]]\n", + "[[ 0.45+0.j -0.16+0.25j]\n", + " [-0.16-0.25j 0.55+0.j ]]\n", " # 4:\n", - "[[0.31+0.j 0.2 +0.31j]\n", - " [0.2 -0.31j 0.69-0.j ]]\n", + "[[0.38-0.j 0.34-0.01j]\n", + " [0.34+0.01j 0.62-0.j ]]\n", "---------------------------------------------------\n", "\n" ] @@ -576,20 +572,20 @@ " Batch size: [5]\n", "\n", " # 0:\n", - "[[0.82+0.j 0.01+0.13j]\n", - " [0.01-0.13j 0.18-0.j ]]\n", + "[[0.11-0.j 0.19+0.17j]\n", + " [0.19-0.17j 0.89+0.j ]]\n", " # 1:\n", - "[[ 0.37+0.j -0.31-0.07j]\n", - " [-0.31+0.07j 0.63+0.j ]]\n", + "[[0.6 +0.j 0.42+0.18j]\n", + " [0.42-0.18j 0.4 +0.j ]]\n", " # 2:\n", - "[[ 0.66+0.j -0.3 -0.07j]\n", - " [-0.3 +0.07j 0.34-0.j ]]\n", + "[[ 0.67+0.j -0.17-0.4j]\n", + " [-0.17+0.4j 0.33+0.j ]]\n", " # 3:\n", - "[[ 0.76-0.j -0.2 -0.12j]\n", - " [-0.2 +0.12j 0.24+0.j ]]\n", + "[[0.11+0.j 0.07-0.25j]\n", + " [0.07+0.25j 0.89+0.j ]]\n", " # 4:\n", - "[[0.77+0.j 0.19+0.1j]\n", - " [0.19-0.1j 0.23+0.j ]]\n", + "[[0.78-0.j 0.21+0.31j]\n", + " [0.21-0.31j 0.22+0.j ]]\n", "---------------------------------------------------\n", "\n" ] @@ -633,7 +629,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
" ] @@ -673,13 +669,10 @@ "output state after applying a quantum circuit:\n", " \n", "---------------------------------------------------\n", - " Backend: density_matrix\n", + " Backend: state_vector\n", " System dimension: [2, 2]\n", " System sequence: [0, 1]\n", - "[[ 0.16+0.j 0.05-0.04j 0.06+0.03j -0.1 -0.12j]\n", - " [ 0.05+0.04j 0.21+0.j 0.05-0.04j -0.06-0.12j]\n", - " [ 0.06-0.03j 0.05+0.04j 0.33+0.j 0.08-0.11j]\n", - " [-0.1 +0.12j -0.06+0.12j 0.08+0.11j 0.3 +0.j ]]\n", + "[-0.2 +0.57j 0.55+0.24j -0.05-0.48j -0.22-0.06j]\n", "---------------------------------------------------\n", "\n" ] @@ -711,36 +704,21 @@ "output batched states after applying a quantum circuit:\n", " \n", "---------------------------------------------------\n", - " Backend: density_matrix\n", + " Backend: state_vector\n", " System dimension: [2, 2]\n", " System sequence: [0, 1]\n", " Batch size: [5]\n", "\n", " # 0:\n", - "[[ 0.13+0.j -0.03+0.07j -0.05+0.08j 0.05-0.05j]\n", - " [-0.03-0.07j 0.15+0.j -0.03+0.05j -0.07-0.07j]\n", - " [-0.05-0.08j -0.03-0.05j 0.49+0.j -0.17+0.17j]\n", - " [ 0.05+0.05j -0.07+0.07j -0.17-0.17j 0.23+0.j ]]\n", + "[-0.21+0.18j -0.28-0.42j 0.3 +0.18j -0.71+0.2j ]\n", " # 1:\n", - "[[ 0.24+0.j -0.06-0.05j -0.04+0.04j -0.01-0.04j]\n", - " [-0.06+0.05j 0.57+0.j 0.1 -0.05j -0.15-0.15j]\n", - " [-0.04-0.04j 0.1 +0.05j 0.05+0.j -0.02-0.06j]\n", - " [-0.01+0.04j -0.15+0.15j -0.02+0.06j 0.14+0.j ]]\n", + "[ 0.24-0.41j 0.1 +0.62j 0.26-0.33j -0.06-0.44j]\n", " # 2:\n", - "[[ 0.26+0.j -0.03+0.02j 0.12-0.11j 0.23-0.16j]\n", - " [-0.03-0.02j 0.14+0.j -0.04+0.05j 0. -0.03j]\n", - " [ 0.12+0.11j -0.04-0.05j 0.14+0.j 0.11+0.j ]\n", - " [ 0.23+0.16j 0. +0.03j 0.11-0.j 0.46+0.j ]]\n", + "[-0.57+0.04j 0.32+0.26j -0.25-0.1j 0.54+0.37j]\n", " # 3:\n", - "[[ 0.13+0.j 0.04-0.01j 0.13-0.j -0.02+0.1j ]\n", - " [ 0.04+0.01j 0.36+0.j 0.11+0.1j -0.01-0.06j]\n", - " [ 0.13+0.j 0.11-0.1j 0.23+0.j 0.03+0.14j]\n", - " [-0.02-0.1j -0.01+0.06j 0.03-0.14j 0.27+0.j ]]\n", + "[-0.15-0.3j -0.34-0.08j -0. -0.43j -0.59-0.48j]\n", " # 4:\n", - "[[ 0.1 +0.j -0.09+0.02j -0.03+0.08j 0.01-0.j ]\n", - " [-0.09-0.02j 0.18+0.j 0.03-0.11j 0.05-0.05j]\n", - " [-0.03-0.08j 0.03+0.11j 0.2 +0.j 0.01+0.06j]\n", - " [ 0.01+0.j 0.05+0.05j 0.01-0.06j 0.52+0.j ]]\n", + "[-0. +0.56j 0.61-0.1j 0.01-0.53j 0.02-0.18j]\n", "---------------------------------------------------\n", "\n" ] @@ -857,18 +835,23 @@ "output_type": "stream", "text": [ "Kraus representation of a quantum channel:\n", - " tensor([[[ 0.3501-0.5552j, 0.5229+0.5438j],\n", - " [-0.4485+0.6066j, 0.4187+0.5055j]]])\n", + " tensor([[[-0.5809-0.0638j, 0.2919-0.2353j],\n", + " [-0.0027-0.5762j, -0.4139+0.0954j]],\n", + "\n", + " [[ 0.3836-0.3976j, 0.5083-0.0660j],\n", + " [-0.1131-0.0923j, -0.3360+0.5509j]]])\n", "\n", "Choi representation of the same quantum channel:\n", - " tensor([[ 0.4308+0.0000j, -0.4938+0.0366j, -0.1189-0.4807j, -0.1341-0.4094j],\n", - " [-0.4938-0.0366j, 0.5692+0.0000j, 0.0954+0.5611j, 0.1189+0.4807j],\n", - " [-0.1189+0.4807j, 0.0954-0.5611j, 0.5692+0.0000j, 0.4938-0.0366j],\n", - " [-0.1341+0.4094j, 0.1189-0.4807j, 0.4938+0.0366j, 0.4308+0.0000j]])\n", + " tensor([[ 0.6467+0.0000j, 0.0316-0.2541j, 0.0667-0.3321j, -0.1136+0.0041j],\n", + " [ 0.0316+0.2541j, 0.3533+0.0000j, 0.0834-0.2232j, -0.0667+0.3321j],\n", + " [ 0.0667+0.3321j, 0.0834+0.2232j, 0.4033+0.0000j, -0.3504-0.1883j],\n", + " [-0.1136-0.0041j, -0.0667-0.3321j, -0.3504+0.1883j, 0.5967+0.0000j]])\n", "\n", "Stinespring representation of the same quantum channel:\n", - " tensor([[ 0.3501-0.5552j, 0.5229+0.5438j],\n", - " [-0.4485+0.6066j, 0.4187+0.5055j]])\n", + " tensor([[-0.5809-0.0638j, 0.2919-0.2353j],\n", + " [ 0.3836-0.3976j, 0.5083-0.0660j],\n", + " [-0.0027-0.5762j, -0.4139+0.0954j],\n", + " [-0.1131-0.0923j, -0.3360+0.5509j]])\n", "\n" ] } @@ -1238,12 +1221,12 @@ "output_type": "stream", "text": [ "state after applying a quantum channel in Kraus representation:\n", - " tensor([[0.6883+6.9389e-18j, 0.0986-1.0855e-01j],\n", - " [0.0986+1.0855e-01j, 0.3117-2.7756e-17j]])\n", + " tensor([[0.0340+0.0000e+00j, 0.1522+9.8172e-02j],\n", + " [0.1522-9.8172e-02j, 0.9660-1.3878e-17j]])\n", "\n", "state after applying a quantum channel in Choi representation:\n", - " tensor([[0.6883+0.0000j, 0.0986-0.1085j],\n", - " [0.0986+0.1085j, 0.3117+0.0000j]])\n" + " tensor([[0.0340+0.0000j, 0.1522+0.0982j],\n", + " [0.1522-0.0982j, 0.9660+0.0000j]])\n" ] } ], @@ -1276,14 +1259,14 @@ "output_type": "stream", "text": [ "state after applying a quantum channel on the first qubit:\n", - " tensor([[ 0.3161-1.3010e-17j, -0.1059+2.1838e-01j, 0.0434-3.2660e-02j,\n", - " -0.0511-4.6972e-02j],\n", - " [-0.1059-2.1838e-01j, 0.3134-1.0408e-17j, -0.0577+5.1967e-02j,\n", - " 0.0578-1.3096e-01j],\n", - " [ 0.0434+3.2660e-02j, -0.0577-5.1967e-02j, 0.0524+0.0000e+00j,\n", - " -0.1158-2.3222e-02j],\n", - " [-0.0511+4.6972e-02j, 0.0578+1.3096e-01j, -0.1158+2.3222e-02j,\n", - " 0.3181-6.9389e-18j]])\n" + " tensor([[ 0.4647+0.0000e+00j, 0.1369+3.2492e-02j, -0.1272-1.2458e-01j,\n", + " 0.1082+8.6246e-02j],\n", + " [ 0.1369-3.2492e-02j, 0.1639-1.0408e-17j, -0.0345-2.4886e-02j,\n", + " 0.0253+1.0877e-01j],\n", + " [-0.1272+1.2458e-01j, -0.0345+2.4886e-02j, 0.1847+1.3878e-17j,\n", + " -0.0517+5.0142e-02j],\n", + " [ 0.1082-8.6246e-02j, 0.0253-1.0877e-01j, -0.0517-5.0142e-02j,\n", + " 0.1867-8.6736e-18j]])\n" ] } ], @@ -1320,20 +1303,20 @@ " Batch size: [5]\n", "\n", " # 0:\n", - "[[0.82+0.j 0.01+0.13j]\n", - " [0.01-0.13j 0.18-0.j ]]\n", + "[[0.11-0.j 0.19+0.17j]\n", + " [0.19-0.17j 0.89+0.j ]]\n", " # 1:\n", - "[[ 0.37+0.j -0.31-0.07j]\n", - " [-0.31+0.07j 0.63+0.j ]]\n", + "[[0.6 +0.j 0.42+0.18j]\n", + " [0.42-0.18j 0.4 +0.j ]]\n", " # 2:\n", - "[[ 0.66+0.j -0.3 -0.07j]\n", - " [-0.3 +0.07j 0.34-0.j ]]\n", + "[[ 0.67+0.j -0.17-0.4j]\n", + " [-0.17+0.4j 0.33+0.j ]]\n", " # 3:\n", - "[[ 0.76-0.j -0.2 -0.12j]\n", - " [-0.2 +0.12j 0.24+0.j ]]\n", + "[[0.11+0.j 0.07-0.25j]\n", + " [0.07+0.25j 0.89+0.j ]]\n", " # 4:\n", - "[[0.77+0.j 0.19+0.1j]\n", - " [0.19-0.1j 0.23+0.j ]]\n", + "[[0.78-0.j 0.21+0.31j]\n", + " [0.21-0.31j 0.22+0.j ]]\n", "---------------------------------------------------\n", "\n" ] @@ -1426,13 +1409,13 @@ "text": [ "\n", "---------VERSION---------\n", - "quairkit: 0.1.0\n", - "torch: 2.3.1+cpu\n", + "quairkit: 0.2.0\n", + "torch: 2.4.1+cpu\n", "numpy: 1.26.0\n", - "scipy: 1.14.0\n", - "matplotlib: 3.9.0\n", + "scipy: 1.14.1\n", + "matplotlib: 3.9.2\n", "---------SYSTEM---------\n", - "Python version: 3.10.14\n", + "Python version: 3.10.15\n", "OS: Windows\n", "OS version: 10.0.26100\n", "---------DEVICE---------\n", @@ -1447,7 +1430,7 @@ ], "metadata": { "kernelspec": { - "display_name": "quair", + "display_name": "quair_test", "language": "python", "name": "python3" }, @@ -1461,7 +1444,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/tutorials/introduction/qinfo.ipynb b/tutorials/introduction/qinfo.ipynb index ad5ef93..792b3c8 100644 --- a/tutorials/introduction/qinfo.ipynb +++ b/tutorials/introduction/qinfo.ipynb @@ -47,12 +47,12 @@ "output_type": "stream", "text": [ "Matrix A is:\n", - "tensor([[ 0.1914+0.9269j, -0.2656+0.1834j],\n", - " [-0.0108-0.3226j, -0.8587+0.3980j]])\n", + "tensor([[-0.1954+0.2146j, 0.1822+0.9394j],\n", + " [-0.1701-0.9417j, 0.2599+0.1293j]])\n", "\n", "Matrix B is:\n", - "tensor([[ 0.2658-0.4467j, 0.4252+0.7409j],\n", - " [ 0.6777-0.5201j, -0.0733-0.5146j]])\n" + "tensor([[ 0.1297-0.5157j, -0.4703-0.7043j],\n", + " [ 0.4578+0.7125j, -0.5246-0.0871j]])\n" ] } ], @@ -86,24 +86,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "The trace of matrix A is (-0.6672641169304913+1.3249511422386733j)\n", + "The trace of matrix A is (0.06443467586598006+0.343978055135579j)\n", "The direct sum of matrix A and B is: \n", - "tensor([[ 0.1914+0.9269j, -0.2656+0.1834j, 0.0000+0.0000j, 0.0000+0.0000j],\n", - " [-0.0108-0.3226j, -0.8587+0.3980j, 0.0000+0.0000j, 0.0000+0.0000j],\n", - " [ 0.0000+0.0000j, 0.0000+0.0000j, 0.2658-0.4467j, 0.4252+0.7409j],\n", - " [ 0.0000+0.0000j, 0.0000+0.0000j, 0.6777-0.5201j, -0.0733-0.5146j]])\n", + "tensor([[-0.1954+0.2146j, 0.1822+0.9394j, 0.0000+0.0000j, 0.0000+0.0000j],\n", + " [-0.1701-0.9417j, 0.2599+0.1293j, 0.0000+0.0000j, 0.0000+0.0000j],\n", + " [ 0.0000+0.0000j, 0.0000+0.0000j, 0.1297-0.5157j, -0.4703-0.7043j],\n", + " [ 0.0000+0.0000j, 0.0000+0.0000j, 0.4578+0.7125j, -0.5246-0.0871j]])\n", "\n", "The tensor product of matrix A and B is: \n", - "tensor([[ 0.4650+0.1609j, -0.6054+0.5360j, 0.0113+0.1674j, -0.2488-0.1188j],\n", - " [ 0.6118+0.5286j, 0.4630-0.1665j, -0.0846+0.2624j, 0.1139+0.1232j],\n", - " [-0.1470-0.0809j, 0.2344-0.1452j, -0.0504+0.4894j, -0.6601-0.4670j],\n", - " [-0.1751-0.2130j, -0.1652+0.0292j, -0.3749+0.7164j, 0.2678+0.4127j]])\n", + "tensor([[ 0.0853+0.1286j, 0.2431+0.0367j, 0.5081+0.0279j, 0.5760-0.5702j],\n", + " [-0.2424-0.0410j, 0.1212-0.0956j, -0.5859+0.5599j, -0.0138-0.5087j],\n", + " [-0.5077-0.0344j, -0.5832+0.5627j, 0.1004-0.1172j, -0.0311-0.2439j],\n", + " [ 0.5930-0.5524j, 0.0073+0.5088j, 0.0268+0.2444j, -0.1251-0.0905j]])\n", "\n", "The conjugate transpose of matrix A is: \n", - "tensor([[ 0.1914-0.9269j, -0.0108+0.3226j],\n", - " [-0.2656-0.1834j, -0.8587-0.3980j]])\n", + "tensor([[-0.1954-0.2146j, -0.1701+0.9417j],\n", + " [ 0.1822-0.9394j, 0.2599-0.1293j]])\n", "\n", - "The decomposition of single-qubit unitary operator A to Z-Y-Z rotation angles is (tensor(-2.9713), tensor(0.6573), tensor(4.3117))\n" + "The decomposition of single-qubit unitary operator A to Z-Y-Z rotation angles is (tensor(-4.0590), tensor(2.5525), tensor(2.2114))\n" ] } ], @@ -145,16 +145,16 @@ "output_type": "stream", "text": [ "The first quantum state is:\n", - " tensor([[ 0.2684+0.0000j, 0.0092+0.0313j, 0.0341+0.0293j, -0.0813+0.0701j],\n", - " [ 0.0092-0.0313j, 0.2159+0.0000j, -0.1825-0.0705j, -0.0018+0.0696j],\n", - " [ 0.0341-0.0293j, -0.1825+0.0705j, 0.3833+0.0000j, 0.0972-0.0349j],\n", - " [-0.0813-0.0701j, -0.0018-0.0696j, 0.0972+0.0349j, 0.1325+0.0000j]])\n", + " tensor([[ 0.7755+0.0000j, 0.0727-0.0237j, -0.1560-0.2811j, -0.2145-0.1373j],\n", + " [ 0.0727+0.0237j, 0.0075+0.0000j, -0.0060-0.0311j, -0.0159-0.0194j],\n", + " [-0.1560+0.2811j, -0.0060+0.0311j, 0.1333+0.0000j, 0.0929-0.0501j],\n", + " [-0.2145+0.1373j, -0.0159+0.0194j, 0.0929+0.0501j, 0.0836+0.0000j]])\n", "\n", "The second quantum state is:\n", - " tensor([[-0.0700+0.5420j],\n", - " [-0.0039+0.5182j],\n", - " [ 0.2417+0.5471j],\n", - " [ 0.0225-0.2730j]])\n" + " tensor([[0.3182-0.1243j],\n", + " [0.0199-0.5031j],\n", + " [0.1631-0.3059j],\n", + " [0.3035-0.6461j]])\n" ] } ], @@ -175,18 +175,18 @@ "output_type": "stream", "text": [ "The von Neumann entropy between state 1 is:\n", - "1.4001894880834034\n", + "1.3013920171748103e-15\n", "torch.float64\n", "The trace distance between state 1 and state 2 is:\n", - "0.8345775362792703\n", + "0.9986828156358498\n", "The state fidelity between state 1 and state 2 is:\n", - "0.4385664929761338\n", + "0.05130919865131176\n", "The purity of state 1 is:\n", - "0.41984282366044323\n", + "0.9999999999999994\n", "The relative entropy of state 1 and state 2 is:\n", - "42.64251394695692\n", + "55.26619734246961\n", "The Schatten 2-norm of state 1 is:\n", - "0.6479527943148662\n" + "0.9999999999999998\n" ] } ], @@ -305,13 +305,13 @@ "text": [ "\n", "---------VERSION---------\n", - "quairkit: 0.1.0\n", - "torch: 2.3.1+cpu\n", + "quairkit: 0.2.0\n", + "torch: 2.4.1+cpu\n", "numpy: 1.26.0\n", - "scipy: 1.14.0\n", - "matplotlib: 3.9.0\n", + "scipy: 1.14.1\n", + "matplotlib: 3.9.2\n", "---------SYSTEM---------\n", - "Python version: 3.10.14\n", + "Python version: 3.10.15\n", "OS: Windows\n", "OS version: 10.0.26100\n", "---------DEVICE---------\n", @@ -326,7 +326,7 @@ ], "metadata": { "kernelspec": { - "display_name": "quair", + "display_name": "quair_test", "language": "python", "name": "python3" }, @@ -340,7 +340,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/tutorials/introduction/state.ipynb b/tutorials/introduction/state.ipynb index 33ca1b0..eb42ac7 100644 --- a/tutorials/introduction/state.ipynb +++ b/tutorials/introduction/state.ipynb @@ -133,7 +133,7 @@ " Backend: state_vector\n", " System dimension: [2]\n", " System sequence: [0]\n", - "[0.27+0.72j 0.63+0.11j]\n", + "[0.23-0.11j 0.28-0.93j]\n", "---------------------------------------------------\n", "\n", "type of the state: density_matrix\n" @@ -177,11 +177,11 @@ " Batch size: [3]\n", "\n", " # 0:\n", - "[ 0.19+0.59j -0.22-0.76j]\n", + "[-0.46-0.j -0.85-0.27j]\n", " # 1:\n", - "[-0.14-0.92j -0.24+0.27j]\n", + "[ 0.39-0.11j -0.87-0.26j]\n", " # 2:\n", - "[0.37+0.73j 0.07+0.57j]\n", + "[ 0.82-0.32j -0.42+0.24j]\n", "---------------------------------------------------\n", "\n" ] @@ -209,31 +209,31 @@ "output_type": "stream", "text": [ "Its density matrix is :\n", - " tensor([[[ 0.3824+0.0000j, -0.4858+0.0143j],\n", - " [-0.4858-0.0143j, 0.6176+0.0000j]],\n", + " tensor([[[ 0.2082+0.0000j, 0.3876-0.1210j],\n", + " [ 0.3876+0.1210j, 0.7918+0.0000j]],\n", "\n", - " [[ 0.8693+0.0000j, -0.2137+0.2606j],\n", - " [-0.2137-0.2606j, 0.1307+0.0000j]],\n", + " [[ 0.1690+0.0000j, -0.3152+0.2027j],\n", + " [-0.3152-0.2027j, 0.8310+0.0000j]],\n", "\n", - " [[ 0.6673+0.0000j, 0.4423-0.1625j],\n", - " [ 0.4423+0.1625j, 0.3327+0.0000j]]])\n", + " [[ 0.7664+0.0000j, -0.4182-0.0642j],\n", + " [-0.4182+0.0642j, 0.2336+0.0000j]]])\n", "\n", "Its ket is :\n", - " tensor([[[ 0.1890+0.5888j],\n", - " [-0.2180-0.7550j]],\n", + " tensor([[[-0.4563-0.0035j],\n", + " [-0.8473-0.2717j]],\n", "\n", - " [[-0.1424-0.9214j],\n", - " [-0.2412+0.2692j]],\n", + " [[ 0.3947-0.1150j],\n", + " [-0.8741-0.2588j]],\n", "\n", - " [[ 0.3672+0.7297j],\n", - " [ 0.0656+0.5730j]]])\n", + " [[ 0.8157-0.3179j],\n", + " [-0.4185+0.2418j]]])\n", "\n", "Its bra is :\n", - " tensor([[[ 0.1890-0.5888j, -0.2180+0.7550j]],\n", + " tensor([[[-0.4563+0.0035j, -0.8473+0.2717j]],\n", "\n", - " [[-0.1424+0.9214j, -0.2412-0.2692j]],\n", + " [[ 0.3947+0.1150j, -0.8741+0.2588j]],\n", "\n", - " [[ 0.3672-0.7297j, 0.0656-0.5730j]]])\n" + " [[ 0.8157+0.3179j, -0.4185-0.2418j]]])\n" ] } ], @@ -261,9 +261,9 @@ "text": [ "\n", "The state is :\n", - " [[ 0.18895906+0.58880204j -0.21803916-0.7550269j ]\n", - " [-0.14242978-0.9214408j -0.24123384+0.26919672j]\n", - " [ 0.3671875 +0.7297144j 0.06562214+0.57304794j]]\n" + " [[-0.45629326-0.00350509j -0.8473218 -0.2717167j ]\n", + " [ 0.39468753-0.1149976j -0.8740804 -0.25880632j]\n", + " [ 0.8156662 -0.31794474j -0.41849422+0.24178998j]]\n" ] } ], @@ -287,7 +287,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The trace of these states are tensor([1.0000+0.j, 1.0000+0.j, 1.0000+0.j])\n", + "The trace of these states are tensor([1.+0.j, 1.+0.j, 1.+0.j])\n", "The rank of these states are 1\n", "The size of these states are 2\n", "The shape of vectorization of these states are torch.Size([3, 4, 1])\n" @@ -346,9 +346,9 @@ " Batch size: [2]\n", "\n", " # 0:\n", - "[-0.14-0.92j -0.24+0.27j]\n", + "[ 0.39-0.11j -0.87-0.26j]\n", " # 1:\n", - "[0.37+0.73j 0.07+0.57j]\n", + "[ 0.82-0.32j -0.42+0.24j]\n", "---------------------------------------------------\n", "\n" ] @@ -382,9 +382,9 @@ " Batch size: [2]\n", "\n", " # 0:\n", - "[-0.14-0.92j -0.24+0.27j]\n", + "[ 0.39-0.11j -0.87-0.26j]\n", " # 1:\n", - "[0.37+0.73j 0.07+0.57j]\n", + "[ 0.82-0.32j -0.42+0.24j]\n", "---------------------------------------------------\n", "\n" ] @@ -413,9 +413,9 @@ "output_type": "stream", "text": [ "matrix multiplication:\n", - "tensor([[ 0.0404+0.0000j, -0.0397+0.1927j],\n", - " [ 0.0000+0.0000j, 0.0000+0.0000j]])\n", - "The overlap of state_1 and state_2 is : tensor(0.0404+0.j)\n" + "tensor([[0.6244+0.0000j, 0.1924+0.4444j],\n", + " [0.0000+0.0000j, 0.0000+0.0000j]])\n", + "The overlap of state_1 and state_2 is : tensor(0.6244+0.j)\n" ] } ], @@ -444,10 +444,10 @@ "output_type": "stream", "text": [ "tensor product:\n", - "tensor([[ 0.0404+0.0000j, -0.0397+0.1927j, 0.0000+0.0000j, -0.0000+0.0000j],\n", - " [-0.0397-0.1927j, 0.9596+0.0000j, 0.0000-0.0000j, 0.0000+0.0000j],\n", - " [ 0.0000+0.0000j, -0.0000+0.0000j, 0.0000+0.0000j, -0.0000+0.0000j],\n", - " [ 0.0000-0.0000j, 0.0000+0.0000j, 0.0000-0.0000j, 0.0000+0.0000j]])\n" + "tensor([[0.6244+0.0000j, 0.1924+0.4444j, 0.0000+0.0000j, 0.0000+0.0000j],\n", + " [0.1924-0.4444j, 0.3756+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j],\n", + " [0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j],\n", + " [0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j, 0.0000+0.0000j]])\n" ] } ], @@ -477,7 +477,7 @@ " Backend: state_vector\n", " System dimension: [2, 2]\n", " System sequence: [0, 1]\n", - "[-0.53+0.14j -0.29+0.21j -0.32+0.45j 0.37-0.36j]\n", + "[-0.56+0.12j -0.28+0.56j -0.3 +0.26j -0.24+0.24j]\n", "---------------------------------------------------\n", "\n", "state after permutation: \n", @@ -485,7 +485,7 @@ " Backend: state_vector\n", " System dimension: [2, 2]\n", " System sequence: [1, 0]\n", - "[-0.53+0.14j -0.32+0.45j -0.29+0.21j 0.37-0.36j]\n", + "[-0.56+0.12j -0.3 +0.26j -0.28+0.56j -0.24+0.24j]\n", "---------------------------------------------------\n", "\n" ] @@ -556,7 +556,7 @@ " Backend: state_vector\n", " System dimension: [2, 2]\n", " System sequence: [1, 0]\n", - "[-0.11+0.22j -0.27-0.5j -0.12+0.59j 0.41+0.29j]\n", + "[0.24+0.56j 0.22+0.15j 0.35+0.48j 0.21+0.4j ]\n", "---------------------------------------------------\n", "\n" ] @@ -589,7 +589,7 @@ " Backend: state_vector\n", " System dimension: [2, 2]\n", " System sequence: [0, 1]\n", - "[-0.53+0.14j -0.29+0.21j -0.32+0.45j 0.37-0.36j]\n", + "[-0.56+0.12j -0.28+0.56j -0.3 +0.26j -0.24+0.24j]\n", "---------------------------------------------------\n", "\n" ] @@ -615,7 +615,7 @@ " Backend: state_vector\n", " System dimension: [2, 2]\n", " System sequence: [0, 1]\n", - "[-0.53+0.14j -0.29+0.21j -0.32+0.45j 0.37-0.36j]\n", + "[-0.56+0.12j -0.28+0.56j -0.3 +0.26j -0.24+0.24j]\n", "---------------------------------------------------\n", "\n" ] @@ -643,7 +643,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "the expectation value under the given observable: tensor(-0.2975)\n" + "the expectation value under the given observable: tensor(0.9029)\n" ] } ], @@ -671,7 +671,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Theoretical value is : tensor([0.2965, 0.1291, 0.3060, 0.2684])\n" + "Theoretical value is : tensor([0.3325, 0.3932, 0.1569, 0.1173])\n" ] } ], @@ -714,13 +714,13 @@ "text": [ "\n", "---------VERSION---------\n", - "quairkit: 0.1.0\n", - "torch: 2.3.1+cpu\n", + "quairkit: 0.2.0\n", + "torch: 2.4.1+cpu\n", "numpy: 1.26.0\n", - "scipy: 1.14.0\n", - "matplotlib: 3.9.0\n", + "scipy: 1.14.1\n", + "matplotlib: 3.9.2\n", "---------SYSTEM---------\n", - "Python version: 3.10.14\n", + "Python version: 3.10.15\n", "OS: Windows\n", "OS version: 10.0.26100\n", "---------DEVICE---------\n", @@ -735,7 +735,7 @@ ], "metadata": { "kernelspec": { - "display_name": "quair", + "display_name": "quair_test", "language": "python", "name": "python3" }, @@ -749,7 +749,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/tutorials/introduction/training.ipynb b/tutorials/introduction/training.ipynb index 1cd1535..8de6614 100644 --- a/tutorials/introduction/training.ipynb +++ b/tutorials/introduction/training.ipynb @@ -361,17 +361,17 @@ "output_type": "stream", "text": [ "Training:\n", - "iter: 0, loss: -1.03517652, lr: 5.00E-02, avg_time: 0.0233s\n", - "iter: 20, loss: -2.40390968, lr: 5.00E-02, avg_time: 0.0181s\n", - "iter: 40, loss: -2.79073524, lr: 5.00E-02, avg_time: 0.0180s\n", - "iter: 60, loss: -3.16234064, lr: 5.00E-02, avg_time: 0.0195s\n", - "iter: 80, loss: -3.45437813, lr: 5.00E-02, avg_time: 0.0192s\n", - "iter: 100, loss: -3.48992562, lr: 5.00E-02, avg_time: 0.0192s\n", - "iter: 120, loss: -3.49346781, lr: 5.00E-02, avg_time: 0.0191s\n", - "iter: 140, loss: -3.49388337, lr: 5.00E-02, avg_time: 0.0209s\n", - "iter: 160, loss: -3.49395490, lr: 5.00E-02, avg_time: 0.0195s\n", - "iter: 180, loss: -3.49395800, lr: 5.00E-02, avg_time: 0.0193s\n", - "iter: 199, loss: -3.49395871, lr: 5.00E-02, avg_time: 0.0194s\n", + "iter: 0, loss: -1.03517652, lr: 5.00E-02, avg_time: 0.0497s\n", + "iter: 20, loss: -2.40390968, lr: 5.00E-02, avg_time: 0.0109s\n", + "iter: 40, loss: -2.79073524, lr: 5.00E-02, avg_time: 0.0117s\n", + "iter: 60, loss: -3.16234064, lr: 5.00E-02, avg_time: 0.0105s\n", + "iter: 80, loss: -3.45437813, lr: 5.00E-02, avg_time: 0.0104s\n", + "iter: 100, loss: -3.48992562, lr: 5.00E-02, avg_time: 0.0119s\n", + "iter: 120, loss: -3.49346781, lr: 5.00E-02, avg_time: 0.0108s\n", + "iter: 140, loss: -3.49388337, lr: 5.00E-02, avg_time: 0.0106s\n", + "iter: 160, loss: -3.49395490, lr: 5.00E-02, avg_time: 0.0099s\n", + "iter: 180, loss: -3.49395800, lr: 5.00E-02, avg_time: 0.0119s\n", + "iter: 199, loss: -3.49395871, lr: 5.00E-02, avg_time: 0.0141s\n", "\n", "----------------------------------------------------------------------------------------------------\n", "\n", @@ -380,7 +380,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -480,13 +480,13 @@ "text": [ "\n", "---------VERSION---------\n", - "quairkit: 0.1.0\n", - "torch: 2.3.1+cpu\n", + "quairkit: 0.2.0\n", + "torch: 2.4.1+cpu\n", "numpy: 1.26.0\n", - "scipy: 1.14.0\n", - "matplotlib: 3.9.0\n", + "scipy: 1.14.1\n", + "matplotlib: 3.9.2\n", "---------SYSTEM---------\n", - "Python version: 3.10.14\n", + "Python version: 3.10.15\n", "OS: Windows\n", "OS version: 10.0.26100\n", "---------DEVICE---------\n", @@ -501,7 +501,7 @@ ], "metadata": { "kernelspec": { - "display_name": "quair", + "display_name": "quair_test", "language": "python", "name": "python3" }, @@ -515,7 +515,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.15" } }, "nbformat": 4,