qgear 1.1.2

Q-Gear

qgear

paper link: https://arxiv.org/pdf/2504.03967

Preliminary

• Let’s assume you already have a computational GPU node allocated on HPC
• Checking the NVIDIA GPU
  • nvidia-smi
• Create a env (we do not recommend using default such .local / HOME)
• Note that more than one GPU support need to enable MPI > the way we choose is high performance lustre file system

1. Install ENV

clone repo

git clone [e-mail]:gzquse/qgear.git`

cd qgear

module load conda
conda create --prefix=/pscratch/sd/{location}/{username}/qgear -y python=3.11 pip
conda activate $SCRATCH/qgear

pip install -u qgear 
pip install -u ipykernel
python -m ipykernel install --user --name qgear --display-name qgear

test qgear install successfully

qgear

   ___     ____ _____    _    ____                                                      
  / _ \   / ___| ____|  / \  |  _ \                                                     
 | | | | | |  _|  _|   / _ \ | |_) |                                                    
 | |_| | | |_| | |___ / ___ \|  _ <                                                     
  \__\_\  \____|_____/_/   \_\_| \_\                                                    


Installation successful!

Welcome to QGEAR. Run 'qgear.run_cudaq()' to speed up.
Fun fact: We love to use Emacs! 🐧

2. Open Jupyter Notebook

NERSC jupyter

https://jupyter.nersc.gov/

Select the kernel

go to nbs/example.ipynb; run example

Pypi

https://pypi.org/project/qgear/

Demos

1. simple speed up with random circuit and QFT

https://gzquse.github.io/qgear/examples.html

2. quantum image encoding

see appendix F in the paper https://gzquse.github.io/qgear/apps.html

local development

. ./pm_martin.dev.source

# make sure qgear package is installed in development mode
https://nbdev.fast.ai/tutorials/tutorial.html
pip3 install -e '.[dev]'
pip3 install qgear

# compile to have changes apply to qgear
nbdev_prepare

Supported Quantum Gates

This is the list of quantum gates currently supported by the implementation, based on the gateId mapping.

Gate ID	Gate Name	Description	Parameters	Example Usage
1	H (Hadamard)	Creates superposition by mapping (	0) and (	1).
2	RY rotation	Rotation around the Y-axis by a given angle.	angle (in radians), target qubit q0	ry(angles[j], q0)
3	RZ rotation	Rotation around the Z-axis by a given angle.	angle (in radians), target qubit q0	rz(angles[j], q0)
4	CX (CNOT)	Controlled-X gate; flips target qubit if control qubit is (	1).	Control qubit q0, target qubit q1
5	Measure	Measures the qubit in the computational basis.	Target qubit q0	mz(q0) (example)
6	CP (Controlled-Phase)	Applies a phase shift to the target qubit if control qubit is (	1).	angle (phase in radians), control qubit q0, target qubit q1
7	SWAP	Swaps the quantum states of two qubits.	Qubit q0, qubit q1	swap(q0, q1)
8	U (U3 gate)	General single-qubit rotation parameterized by three Euler angles.	theta, phi, lambda_ (all in radians), target qubit q0	u3(theta, phi, lambda_, q0)

---

Notes

• q0 is the primary target qubit.
• q1 is an additional target or control qubit (depending on the gate).
• angles is an array of rotation parameters in radians.
• gate_type and qvector are used to determine qubit mapping for multi-qubit gates.
• The U3 gate is the most general single-qubit gate and can represent any rotation.

---

Example Gate Sequence

```python # Apply Hadamard to qubit 0 h(q0)

Rotate qubit 0 around Y-axis by pi/4

ry(math.pi/4, q0)

Apply CNOT from qubit 0 to qubit 1

x.ctrl(q0, q1)

Apply a controlled-phase gate

r1.ctrl(math.pi/2, q0, q1)

Swap qubits 0 and 1

swap(q0, q1)

Apply a general U3 rotation

u3(theta, phi, lambda_, q0)

Goal

build the versatile all-in-one quantum accelerator for HPC-QPU hybrid regime that supports all the mainstream quantum frameworks.