deepquantum 4.4.0

DeepQuantum for quantum computing

DeepQuantum

DeepQuantum is a platform that integrates artificial intelligence (AI) and quantum computing (QC). It is an efficient programming framework designed for quantum machine learning and photonic quantum computing. By leveraging the PyTorch deep learning platform for QC, DeepQuantum provides a powerful and easy-to-use tool for creating and simulating quantum circuits and photonic quantum circuits. This makes it ideal for developers to quickly get started and explore the field in depth. It also serves as a valuable learning platform for quantum computing enthusiasts.

Key Features

• AI-Enhanced Quantum Computing Framework: Seamlessly integrated with PyTorch, it utilizes technologies such as automatic differentiation, vectorized parallelism, and GPU acceleration for efficiency. It facilitates the easy construction of hybrid quantum-classical models, enabling end-to-end training and inference.
• User-Friendly API Design: The API is designed to be simple and intuitive, making it easy to initialize quantum neural networks and providing flexibility in data encoding.
• Photonic Quantum Computing Simulation: The Photonic module includes Fock, Gaussian and Bosonic backends, catering to different simulation needs in photonic quantum computing. It comes with built-in optimizers to support on-chip training of photonic quantum circuits.
• Large-Scale Quantum Circuit Simulation: Leveraging tensor network techniques, DeepQuantum enables approximate simulation of circuits with over 100 qubits on a single laptop. Through a distributed parallel architecture and PyTorch's native communication protocol, it efficiently utilizes multi-node, multi-GPU computational power to boost large-scale quantum simulations. These capabilities allow DeepQuantum to deliver industry-leading performance in both simulation scale and efficiency.
• Advanced Architecture for Cutting-Edge Algorithm Exploration: The first framework to support algorithm design and mapping for time-domain-multiplexed photonic quantum circuits, and the first to realize closed-loop integration of quantum circuits, photonic quantum circuits, and MBQC, enabling robust support for both specialized and universal photonic quantum algorithm design.

Installation

Before installing DeepQuantum, we recommend first manually installing PyTorch 2. If the latest version of PyTorch is not compatible with your CUDA version, manually install a compatible PyTorch 2 version.

The PyTorch installation instructions currently recommend:

1. Install Miniconda or Anaconda.
2. Setup conda environment. For example, run conda create -n <ENV_NAME> python=3.12 and conda activate <ENV_NAME>.
3. Install PyTorch following the PyTorch installation instructions. Currently, this suggests running pip install torch.

If you want to customize your installation, please follow the PyTorch installation instructions to build from source.

To install DeepQuantum with pip, run

pip install deepquantum
# or for developers
pip install deepquantum[dev]
# or use tsinghua source
pip install deepquantum -i https://pypi.tuna.tsinghua.edu.cn/simple

Alternatively, to install DeepQuantum from source, run

git clone https://github.com/TuringQ/deepquantum.git
cd deepquantum
pip install -e .
# or use tsinghua source
pip install -e . -i https://pypi.tuna.tsinghua.edu.cn/simple
# for developers
pip install -r requirements-dev.txt

Getting Started

To begin, please start with the tutorials (CN) on basics and photonic basics.

Below are some minimal demos to help you get started.

• Quantum circuit

import deepquantum as dq
cir = dq.QubitCircuit(2)
cir.h(0)
cir.cnot(0, 1)
cir.rx(1, 0.2)
cir.observable(0)
print(cir())
print(cir.expectation())

• Quantum circuit based on matrix product state

You can simply set mps=True in QubitCircuit and adjust the bond dimension chi to control the complexity.

cir = dq.QubitCircuit(2, mps=True, chi=4)
cir.h(0)
cir.cnot(0, 1)
cir.rx(1, 0.2)
cir.observable(0)
print(cir())
print(cir.expectation())

• Photonic quantum circuit with the Fock backend, based on Fock basis state

cir = dq.QumodeCircuit(2, [1,1])
cir.dc([0,1])
cir.ps(0, 0.1)
cir.bs([0,1], [0.2,0.3])
print(cir())
print(cir.measure())

• Photonic quantum circuit with the Fock backend, based on Fock state tensor

cir = dq.QumodeCircuit(2, [(1, [1,1])], basis=False)
cir.dc([0,1])
cir.ps(0, 0.1)
cir.bs([0,1], [0.2,0.3])
print(cir())
print(cir.measure())

• Photonic quantum circuit with the Gaussian backend

cir = dq.QumodeCircuit(2, 'vac', cutoff=10, backend='gaussian')
cir.s(0, 0.1)
cir.d(1, 0.1)
cir.bs([0,1], [0.2,0.3])
print(cir())
print(cir.measure())
print(cir.photon_number_mean_var(wires=0))
print(cir.measure_homodyne(wires=1))

• Photonic quantum circuit with the Bosonic backend

cir = dq.QumodeCircuit(2, 'vac', backend='bosonic')
cir.cat(0, 0.5, 0.0)
cir.gkp(1, 0.5, 0.5)
cir.bs([0,1], [0.2,0.3])
print(cir())
print(cir.photon_number_mean_var(wires=0))
print(cir.measure_homodyne(wires=1))

• Pattern of measurement-based quantum computation

pattern = dq.Pattern(2)
# Hadamard gate on qubit 1
pattern.n(2)
pattern.e(1, 2)
pattern.m(1)
pattern.x(2, domain=1)
# CNOT
pattern.n([3,4])
pattern.e(2, 3)
pattern.e(0, 3)
pattern.e(3, 4)
pattern.m(2)
pattern.m(3)
pattern.x(4, domain=3)
pattern.z(4, domain=2)
pattern.z(0, domain=2)
print(abs(pattern().full_state))

• Transpile quantum circuit to MBQC pattern

cir = dq.QubitCircuit(2)
cir.h(0)
cir.cnot(0, 1)
cir.rx(1, 0.2)
pattern = cir.pattern()
print(cir())
print(pattern().full_state)
print(cir() / pattern().full_state)

• Distributed simulation of quantum circuit

import torch
# OMP_NUM_THREADS=2 torchrun --nproc_per_node=4 main.py
backend = 'gloo' # for CPU
# torchrun --nproc_per_node=4 main.py
backend = 'nccl' # for GPU
rank, world_size, local_rank = dq.setup_distributed(backend)
if backend == 'nccl':
    device = f'cuda:{local_rank}'
elif backend == 'gloo':
    device = 'cpu'
data = torch.arange(4, dtype=torch.float, device=device, requires_grad=True)
cir = dq.DistributedQubitCircuit(4)
cir.rylayer(encode=True)
cir.cnot_ring()
cir.observable(0)
cir.observable(1, 'x')
if backend == 'nccl':
    cir.to(f'cuda:{local_rank}')
state = cir(data).amps
result = cir.measure(with_prob=True)
exp = cir.expectation().sum()
exp.backward()
if rank == 0:
    print(state)
    print(result)
    print(exp)
    print(data.grad)
dq.cleanup_distributed()

• Distributed simulation of photonic quantum circuit

# OMP_NUM_THREADS=2 torchrun --nproc_per_node=4 main.py
backend = 'gloo' # for CPU
# torchrun --nproc_per_node=4 main.py
backend = 'nccl' # for GPU
rank, world_size, local_rank = dq.setup_distributed(backend)
nmode = 4
cutoff = 4
data = torch.arange(14, dtype=torch.float) / 10
cir = dq.DistributedQumodeCircuit(nmode, [0] * nmode, cutoff)
for i in range(nmode):
    cir.s(i, encode=True)
for i in range(nmode - 1):
    cir.bs([i, i + 1], encode=True)
if backend == 'nccl':
    data = data.to(f'cuda:{local_rank}')
    cir.to(f'cuda:{local_rank}')
state = cir(data).amps
result = cir.measure(with_prob=True)
if rank == 0:
    print(state)
    print(result)
dq.cleanup_distributed()

License

DeepQuantum is open source, released under the Apache License, Version 2.0.