cirq

Name: cirq
Author: K-Dense-AI

799installs3.1kforks

How to install cirq?

k-dense-ai/scientific-agent-skills/cirq

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill cirq

Installs into the current project.

Summary

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML

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SKILL.md

1	# Cirq - Quantum Computing with Python
2
3	Cirq is Google Quantum AI's open-source framework for designing, simulating, and running quantum circuits on quantum computers and simulators.
4
5	## When to Use This Skill
6
7	Use this skill when:
8	- Building, simulating, or optimizing NISQ circuits in Python
9	- Running jobs on Google Quantum AI processors (via `cirq-google`) or partner backends (IonQ, Azure Quantum, AQT, Pasqal)
10	- Modeling noise, compiling to hardware gatesets, or designing characterization experiments
11	- Using parameter sweeps, transformers, or the ReCirq experiment patterns
12
13	For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.
14
15	## Installation
16
17	Requires Python 3.11+. Current stable release: 1.6.1 (August 2025). Vendor packages share the same version number.
18
19	```bash
20	uv pip install "cirq==1.6.1"
21	```
22
23	For hardware integration (pin matching versions for reproducibility):
24	```bash
25	# Google Quantum Engine (requires approved GCP project access)
26	uv pip install "cirq-google==1.6.1"
27
28	# IonQ
29	uv pip install "cirq-ionq==1.6.1"
30
31	# AQT (Alpine Quantum Technologies)
32	uv pip install "cirq-aqt==1.6.1"
33
34	# Pasqal
35	uv pip install "cirq-pasqal==1.6.1"
36
37	# Azure Quantum (IonQ, Honeywell/Quantinuum backends)
38	uv pip install "azure-quantum[cirq]"
39	```
40
41	For latest features during development, omit version pins; for production or hardware runs, pin all packages to the same Cirq release.
42
43	## Quick Start
44
45	### Basic Circuit
46
47	```python
48	import cirq
49	import numpy as np
50
51	# Create qubits
52	q0, q1 = cirq.LineQubit.range(2)
53
54	# Build circuit
55	circuit = cirq.Circuit(
56	cirq.H(q0), # Hadamard on q0
57	cirq.CNOT(q0, q1), # CNOT with q0 control, q1 target
58	cirq.measure(q0, q1, key='result')
59	)
60
61	print(circuit)
62
63	# Simulate
64	simulator = cirq.Simulator()
65	result = simulator.run(circuit, repetitions=1000)
66
67	# Display results
68	print(result.histogram(key='result'))
69	```
70
71	### Parameterized Circuit
72
73	```python
74	import sympy
75
76	# Define symbolic parameter
77	theta = sympy.Symbol('theta')
78
79	# Create parameterized circuit
80	circuit = cirq.Circuit(
81	cirq.ry(theta)(q0),
82	cirq.measure(q0, key='m')
83	)
84
85	# Sweep over parameter values
86	sweep = cirq.Linspace('theta', start=0, stop=2*np.pi, length=20)
87	results = simulator.run_sweep(circuit, params=sweep, repetitions=1000)
88
89	# Process results
90	for params, result in zip(sweep, results):
91	theta_val = params['theta']
92	counts = result.histogram(key='m')
93	print(f"θ={theta_val:.2f}: {counts}")
94	```
95
96	## Core Capabilities
97
98	### Circuit Building
99	For comprehensive information about building quantum circuits, including qubits, gates, operations, custom gates, and circuit patterns, see:
100	- [references/building.md](references/building.md) - Complete guide to circuit construction
101
102	Common topics:
103	- Qubit types (GridQubit, LineQubit, NamedQubit)
104	- Single and two-qubit gates
105	- Parameterized gates and operations
106	- Custom gate decomposition
107	- Circuit organization with moments
108	- Standard circuit patterns (Bell states, GHZ, QFT)
109	- Import/export (OpenQASM, JSON)
110	- Working with qudits and observables
111
112	### Simulation
113	For detailed information about simulating quantum circuits, including exact simulation, noisy simulation, parameter sweeps, and the Quantum Virtual Machine, see:
114	- [references/simulation.md](references/simulation.md) - Complete guide to quantum simulation
115
116	Common topics:
117	- Exact simulation (state vector, density matrix)
118	- Sampling and measurements
119	- Parameter sweeps (single and multiple parameters)
120	- Noisy simulation
121	- State histograms and visualization
122	- Quantum Virtual Machine (QVM)
123	- Expectation values and observables
124	- Performance optimization
125
126	### Circuit Transformation
127	For information about optimizing, compiling, and manipulating quantum circuits, see:
128	- [references/transformation.md](references/transformation.md) - Complete guide to circuit transformations
129
130	Common topics:
131	- Transformer framework
132	- Gate decomposition
133	- Circuit optimization (merge gates, eject Z gates, drop negligible operations)
134	- Circuit compilation for hardware
135	- Qubit routing and SWAP insertion
136	- Custom transformers
137	- Transformation pipelines
138
139	### Hardware Integration
140	For information about running circuits on real quantum hardware from various providers, see:
141	- [references/hardware.md](references/hardware.md) - Complete guide to hardware integration
142
143	Supported providers:
144	- Google Quantum AI (`cirq-google`) — Sycamore, Weber, Willow processors via Quantum Engine (res

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cirq

799installs3.1kforks

View on GitHub

How to install cirq?

k-dense-ai/scientific-agent-skills/cirq

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill cirq

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Cirq - Quantum Computing with Python
2
3	Cirq is Google Quantum AI's open-source framework for designing, simulating, and running quantum circuits on quantum computers and simulators.
4
5	## When to Use This Skill
6
7	Use this skill when:
8	- Building, simulating, or optimizing NISQ circuits in Python
9	- Running jobs on Google Quantum AI processors (via `cirq-google`) or partner backends (IonQ, Azure Quantum, AQT, Pasqal)
10	- Modeling noise, compiling to hardware gatesets, or designing characterization experiments
11	- Using parameter sweeps, transformers, or the ReCirq experiment patterns
12
13	For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.
14
15	## Installation
16
17	Requires Python 3.11+. Current stable release: 1.6.1 (August 2025). Vendor packages share the same version number.
18
19	```bash
20	uv pip install "cirq==1.6.1"
21	```
22
23	For hardware integration (pin matching versions for reproducibility):
24	```bash
25	# Google Quantum Engine (requires approved GCP project access)
26	uv pip install "cirq-google==1.6.1"
27
28	# IonQ
29	uv pip install "cirq-ionq==1.6.1"
30
31	# AQT (Alpine Quantum Technologies)
32	uv pip install "cirq-aqt==1.6.1"
33
34	# Pasqal
35	uv pip install "cirq-pasqal==1.6.1"
36
37	# Azure Quantum (IonQ, Honeywell/Quantinuum backends)
38	uv pip install "azure-quantum[cirq]"
39	```
40
41	For latest features during development, omit version pins; for production or hardware runs, pin all packages to the same Cirq release.
42
43	## Quick Start
44
45	### Basic Circuit
46
47	```python
48	import cirq
49	import numpy as np
50
51	# Create qubits
52	q0, q1 = cirq.LineQubit.range(2)
53
54	# Build circuit
55	circuit = cirq.Circuit(
56	cirq.H(q0), # Hadamard on q0
57	cirq.CNOT(q0, q1), # CNOT with q0 control, q1 target
58	cirq.measure(q0, q1, key='result')
59	)
60
61	print(circuit)
62
63	# Simulate
64	simulator = cirq.Simulator()
65	result = simulator.run(circuit, repetitions=1000)
66
67	# Display results
68	print(result.histogram(key='result'))
69	```
70
71	### Parameterized Circuit
72
73	```python
74	import sympy
75
76	# Define symbolic parameter
77	theta = sympy.Symbol('theta')
78
79	# Create parameterized circuit
80	circuit = cirq.Circuit(
81	cirq.ry(theta)(q0),
82	cirq.measure(q0, key='m')
83	)
84
85	# Sweep over parameter values
86	sweep = cirq.Linspace('theta', start=0, stop=2*np.pi, length=20)
87	results = simulator.run_sweep(circuit, params=sweep, repetitions=1000)
88
89	# Process results
90	for params, result in zip(sweep, results):
91	theta_val = params['theta']
92	counts = result.histogram(key='m')
93	print(f"θ={theta_val:.2f}: {counts}")
94	```
95
96	## Core Capabilities
97
98	### Circuit Building
99	For comprehensive information about building quantum circuits, including qubits, gates, operations, custom gates, and circuit patterns, see:
100	- [references/building.md](references/building.md) - Complete guide to circuit construction
101
102	Common topics:
103	- Qubit types (GridQubit, LineQubit, NamedQubit)
104	- Single and two-qubit gates
105	- Parameterized gates and operations
106	- Custom gate decomposition
107	- Circuit organization with moments
108	- Standard circuit patterns (Bell states, GHZ, QFT)
109	- Import/export (OpenQASM, JSON)
110	- Working with qudits and observables
111
112	### Simulation
113	For detailed information about simulating quantum circuits, including exact simulation, noisy simulation, parameter sweeps, and the Quantum Virtual Machine, see:
114	- [references/simulation.md](references/simulation.md) - Complete guide to quantum simulation
115
116	Common topics:
117	- Exact simulation (state vector, density matrix)
118	- Sampling and measurements
119	- Parameter sweeps (single and multiple parameters)
120	- Noisy simulation
121	- State histograms and visualization
122	- Quantum Virtual Machine (QVM)
123	- Expectation values and observables
124	- Performance optimization
125
126	### Circuit Transformation
127	For information about optimizing, compiling, and manipulating quantum circuits, see:
128	- [references/transformation.md](references/transformation.md) - Complete guide to circuit transformations
129
130	Common topics:
131	- Transformer framework
132	- Gate decomposition
133	- Circuit optimization (merge gates, eject Z gates, drop negligible operations)
134	- Circuit compilation for hardware
135	- Qubit routing and SWAP insertion
136	- Custom transformers
137	- Transformation pipelines
138
139	### Hardware Integration
140	For information about running circuits on real quantum hardware from various providers, see:
141	- [references/hardware.md](references/hardware.md) - Complete guide to hardware integration
142
143	Supported providers:
144	- Google Quantum AI (`cirq-google`) — Sycamore, Weber, Willow processors via Quantum Engine (res

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