QuTiPv5 Paper Notebook: QIP2 Circuit Simulation

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tutorials-v5/quantum-circuits/v5_paper-qip2.md

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+---
+jupyter:
+  jupytext:
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.3'
+      jupytext_version: 1.13.8
+  kernelspec:
+    display_name: qutip-tutorials-v5
+    language: python
+    name: python3
+---
+
+# QuTiPv5 Paper - Quantum Circuits with QIP
+
+Authors: Maximilian Meyer-Mölleringhof ([email protected]), Boxi Li ([email protected]), Neill Lambert ([email protected])
+
+Quantum circuits serve as a standard framework for representing and manipulating quantum algorithms visually and conceptually.
+As a member of the QuTiP family, the QuTiP-QIP [\[1\]](#References) package add this framework and enables several distinctive capabilities.
+It allows seamless incorporation of circuit-representing unitaries into QuTiP's ecosystem using the `Qobj` class.
+Moreover, it links QuTiP-QOC and the open-system solvers, enabling pulse-level simulations of circuits with realistic noise effects.
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+from qutip import (about, basis, destroy, expect, ket2dm, mesolve, qeye,
+                   sesolve, sigmax, sigmay, sigmaz, tensor)
+from qutip_qip.circuit import QubitCircuit
+from qutip_qip.device import SCQubits
+
+%matplotlib inline
+```
+
+## Introduction
+
+In this example, we show how to
+
+- Construct a simple quantum circuit which simulates the dynamics of a two-qubit Hamiltonian
+- Simulate the dynamics of an open system, using ancillas to induce noise
+- Run both simulations on hardware backend (i.e., processor) that simulates itself the intrinsic noisy dynamics
+
+As for the Hamiltonian, we consider two interacting qubits:
+
+$H = \dfrac{\epsilon_1}{2} \sigma_z^{(1)} + \dfrac{\epsilon_2}{2} \sigma_z^{(2)} + g \sigma_{x}^{(1)} \sigma_{x}^{(2)}$.
+
+The Pauli matrices $\sigma_z^{(1/2)}$ describe the respective two-level system of the qubits.
+The qubits are coupled via $\sigma_{x}^{(1/2)}$ and their interaction strength is given by $g$.
+
+```python
+# Parameters
+epsilon1 = 0.7
+epsilon2 = 1.0
+g = 0.3
+
+sx1 = sigmax() & qeye(2)
+sx2 = qeye(2) & sigmax()
+
+sy1 = sigmay() & qeye(2)
+sy2 = qeye(2) & sigmay()
+
+sz1 = sigmaz() & qeye(2)
+sz2 = qeye(2) & sigmaz()
+
+H = 0.5 * epsilon1 * sz1 + 0.5 * epsilon2 * sz2 + g * sx1 * sx2
+
+init_state = basis(2, 0) & basis(2, 1)
+```
+
+## Circuit Visualization
+
+Before jumping into the example, we want to quickly look at how the circuits we build can be visualized.
+In QuTiP-QIP, quantum circuits can be drawn in three different ways: as text, as matplotlib plot and in latex format.
+
+```python
+qc = QubitCircuit(2, num_cbits=1)
+qc.add_gate("H", 0)
+qc.add_gate("H", 1)
+qc.add_gate("CNOT", 1, 0)
+qc.add_measurement("M", targets=[0], classical_store=0)
+```
+
+After this, we can draw the circuit by defining the various renderers.
+
+```python
+qc.draw("text")
+qc.draw("matplotlib")
+qc.draw("latex")
+```
+
+## Simulating Hamiltonian Dynamics
+
+A very common method in quantum simulations is to reduce the propagation of the Schrödinger equation into several short time steps in order to finally arrive at the desired solution.
+The propagator for one-time such step is well approximated by using *Trotterization*:
+
+$\psi(t_f) = e^{-i (H_A + H_B) t_f} \psi(0) \approx [e^{-i H_A dt} e^{-i H_B dt}]^d \psi(0)$,
+
+given that the time steps $dt = t_f / d$ is sufficiently small.
+The idea then is that the Hamiltonians $H_A$ and $H_B$ are chosen such that $e^{-i H_{A,B} dt}$ can be mapped to quantum gates.
+
+For our example, we express
+
+$H_A = \dfrac{\epsilon_1}{2} \sigma_z^{(1)} + \dfrac{\epsilon_2}{2} \sigma_z^{(2)}$ and
+
+$H_B = g \sigma_x^{(1)} \sigma_x^{(2)}$.
+
+We can then construct the circuit with two qubits and a set of gates:
+
+$A_1 = e^{-i \epsilon_1 \sigma_z^{(1)} dt / 2}$,
+
+$A_2 = e^{-i \epsilon_2 \sigma_z^{(2)} dt / 2}$ and
+
+$B = e^{-i g \sigma_x^{(1)} \sigma_x^{(2)} dt}$.
+
+We apply them to an initial state and then repeat them $d$ times.
+
+Depending on the hardware, the type of available physical gates changes.
+Since we will be using a superconducting qubit hardware backend, we will express these gates in terms of RZ (rotation around Z axis), RZX (combined rotation around XZ) and Hadamard gates.
+In general, QuTiP-QIP supports a great variety of gates and also the option for custom gates exists.
+More information on this is presented in the original paper for QuTiP-QIP [\[1\]](#References).
+
+Of course, the accuracy of our simulation is greatly dependent on the Trotter step size.
+To keep this tutorial at a reasonable execution time, we choose $dt = 4.0$, however it is worth exploring smaller step sizes.
+
+```python
+# simulation parameters
+tf = 20.0  # total time
+dt = 4.0  # Trotter step size
+num_steps = int(tf / dt)
+times_circ = np.arange(0, tf + dt, dt)
+```
+
+```python
+# initialization of two qubit circuit
+trotter_simulation = QubitCircuit(2)
+
+# gates for trotterization with small timesteps dt
+trotter_simulation.add_gate("RZ", targets=[0], arg_value=(epsilon1 * dt))
+trotter_simulation.add_gate("RZ", targets=[1], arg_value=(epsilon2 * dt))
+
+trotter_simulation.add_gate("H", targets=[0])
+trotter_simulation.add_gate("RZX", targets=[0, 1], arg_value=g * dt * 2)
+trotter_simulation.add_gate("H", targets=[0])
+trotter_simulation.compute_unitary()
+```
+
+```python
+trotter_simulation.draw("matplotlib")
+```
+
+```python
+# Evaluate multiple iteration of a circuit
+result_circ = init_state
+state_trotter_circ = [init_state]
+
+for dd in range(num_steps):
+    result_circ = trotter_simulation.run(state=result_circ)
+    state_trotter_circ.append(result_circ)
+```
+
+### Noisy Hardware
+
+We can load our quantum circuit into a hardware backend to simulate the execution on various types of hardware.
+In our case, we are interested in a superconducting circuit:
+
+```python
+processor = SCQubits(num_qubits=2, t1=2e5, t2=2e5)
+processor.load_circuit(trotter_simulation)
+# Since SCQubit is modelled as a qutrit, we need three-level systems here
+init_state_trit = tensor(basis(3, 0), basis(3, 1))
+```
+
+Now we can run the simulation in a similar fashion as we did before.
+In this case however, the results is a `results` object from the QuTiP solver being used.
+For our example, `mesolve()` is used as we specified finite $T_1$ and $T_2$ upon initialization.
+The processor itself defines an internal Hamiltonian as well as available control operations, pulse shapes, $T_1$ and $T_2$, etc..
+
+```python
+state_proc = init_state_trit
+state_list_proc = [init_state_trit]
+
+for dd in range(num_steps):
+    result = processor.run_state(state_proc)
+    state_proc = result.final_state
+    state_list_proc.append(result.final_state)
+```
+
+We can see the pulse shapes used in the solver:
+
+```python
+processor.plot_pulses()
+```
+
+### Comparison of Results
+
+To evaluate the quantum simulation, we compare the final results with the standard `sesolve` method.
+
+```python
+# Exact Schrodinger equation
+tlist = np.linspace(0, tf, 100)
+states_sesolve = sesolve(H, init_state, tlist).states
+```
+
+```python
+sz_qutrit = basis(3, 0) * basis(3, 0).dag() - basis(3, 1) * basis(3, 1).dag()
+
+expec_sesolve = expect(sz1, states_sesolve)
+expec_trotter = expect(sz1, state_trotter_circ)
+expec_supcond = expect(sz_qutrit & qeye(3), state_list_proc)
+
+plt.plot(tlist, expec_sesolve, "-", label="Ideal")
+plt.plot(times_circ, expec_trotter, "--d", label="Trotter circuit")
+plt.plot(times_circ, expec_supcond, "-.o", label="noisy hardware")
+plt.xlabel("Time")
+plt.ylabel(r"$\langle \sigma_z^{(1)} \rangle$")
+plt.legend()
+plt.show()
+```
+
+## Lindblad Simulation
+
+To simulate the Lindblad master equation, we consider two recent proposals [\[2, 3\]](#References) where a sequence of unitaries is used to approximate Lindblad dynamics to arbitrary order.
+This is realized by employing a ancilla qubits, and measurements / resets, for the Lindblad collapse operators.
+The initial state is a dilated state $\ket{\psi_D(t=0)} = \ket{\psi(t = 0)} \otimes \ket{0}^{\otimes K}$ where $K$ referes to the number of ancillas.
+Every time step, the system and the ancillas interact for a time span $\sqrt{dt}$.
+Thereafter, the ancillas are reset to their ground state.
+
+We can find the Trotter approximation for the unitary describing the interaction between system $i$ and its associated ancilla for collapse operator $k$:
+
+$U(\sqrt{dt}) \approx e^{-\frac{1}{2} i \sigma_{x}^{(i)}\sigma_{x}^{(k)} \sqrt{\gamma_k dt}} \cdot e^{\frac{1}{2} i \sigma_{y}^{(i)}\sigma_{y}^{(k)}\sqrt{\gamma_k dt}}$,
+
+with the dissipation rate $\gamma_k$.
+Like in the example above, we can decompose this unitary into Hadamard, `RZ`, `RX` and `RZX` gates which are the native gates for the superconducting processor hardware in QuTiP.
+
+```python
+gam = 0.03  # dissipation rate
+```
+
+```python
+trotter_simulation_noisy = QubitCircuit(4)
+
+sqrt_term = np.sqrt(gam * dt)
+
+# Coherent dynamics
+trotter_simulation_noisy.add_gate("RZ", targets=[1], arg_value=epsilon1 * dt)
+trotter_simulation_noisy.add_gate("RZ", targets=[2], arg_value=epsilon2 * dt)
+
+trotter_simulation_noisy.add_gate("H", targets=[1])
+trotter_simulation_noisy.add_gate("RZX", targets=[1, 2], arg_value=g * dt * 2)
+trotter_simulation_noisy.add_gate("H", targets=[1])
+
+# Decoherence
+# exp(-i XX t)
+trotter_simulation_noisy.add_gate("H", targets=[0])
+trotter_simulation_noisy.add_gate("RZX", targets=[0, 1], arg_value=sqrt_term)
+trotter_simulation_noisy.add_gate("H", targets=[0])
+
+# exp(-i YY t)
+trotter_simulation_noisy.add_gate("RZ", 1, arg_value=np.pi / 2)
+trotter_simulation_noisy.add_gate("RX", 0, arg_value=-np.pi / 2)
+trotter_simulation_noisy.add_gate("RZX", [0, 1], arg_value=sqrt_term)
+trotter_simulation_noisy.add_gate("RZ", 1, arg_value=-np.pi / 2)
+trotter_simulation_noisy.add_gate("RX", 0, arg_value=np.pi / 2)
+
+# exp(-i XX t)
+trotter_simulation_noisy.add_gate("H", targets=[2])
+trotter_simulation_noisy.add_gate("RZX", targets=[2, 3], arg_value=sqrt_term)
+trotter_simulation_noisy.add_gate("H", targets=[2])
+
+# exp(-i YY t)
+trotter_simulation_noisy.add_gate("RZ", 3, arg_value=np.pi / 2)
+trotter_simulation_noisy.add_gate("RX", 2, arg_value=-np.pi / 2)
+trotter_simulation_noisy.add_gate("RZX", [2, 3], arg_value=sqrt_term)
+trotter_simulation_noisy.add_gate("RZ", 3, arg_value=-np.pi / 2)
+trotter_simulation_noisy.add_gate("RX", 2, arg_value=np.pi / 2)
+
+trotter_simulation_noisy.draw("matplotlib")
+```
+
+```python
+state_system = ket2dm(init_state)
+state_trotter_circ = [init_state]
+ancilla = basis(2, 1) * basis(2, 1).dag()
+for dd in range(num_steps):
+    state_full = tensor(ancilla, state_system, ancilla)
+    state_full = trotter_simulation_noisy.run(state=state_full)
+    state_system = state_full.ptrace([1, 2])
+    state_trotter_circ.append(state_system)
+```
+
+Again, we want to run this trotterized evolution on the suuperconducting hardware backend. Be aware that, due of the increased complexity, this computation can take several minutes depending on your hardware.
+
+```python
+processor = SCQubits(num_qubits=4, t1=3.0e4, t2=3.0e4)
+processor.load_circuit(trotter_simulation_noisy)
+```
+
+```python
+state_system = ket2dm(init_state_trit)
+state_list_proc = [state_system]
+for dd in range(num_steps):
+    state_full = tensor(
+        basis(3, 1) * basis(3, 1).dag(),
+        state_system,
+        basis(3, 1) * basis(3, 1).dag(),
+    )
+    result_noisey = processor.run_state(
+        state_full,
+        solver="mesolve",
+        options={
+            "store_states": False,
+            "store_final_state": True,
+        },
+    )
+    state_full = result_noisey.final_state
+    state_system = state_full.ptrace([1, 2])
+    state_list_proc.append(state_system)
+    print(f"Step {dd+1}/{num_steps} finished.")
+```
+
+```python
+processor.plot_pulses()
+```
+
+### Comparison of Results
+
+```python
+# Standard mesolve solution
+sm1 = tensor(destroy(2).dag(), qeye(2))
+sm2 = tensor(qeye(2), destroy(2).dag())
+c_ops = [np.sqrt(gam) * sm1, np.sqrt(gam) * sm2]
+
+result_me = mesolve(H, init_state, tlist, c_ops, e_ops=[sz1, sz2])
+```
+
+```python
+plt.plot(tlist, result_me.expect[0], "-", label=r"Ideal")
+plt.plot(times_circ, expect(sz1, state_trotter_circ), "--d", label="trotter")
+plt.plot(
+    times_circ,
+    expect(sz_qutrit & qeye(3), state_list_proc),
+    "-.o",
+    label=r"noisy hardware",
+)
+plt.xlabel("Time")
+plt.ylabel("Expectation values")
+plt.legend()
+plt.show()
+```
+
+## References
+
+\[1\] [Li, et. al, Quantum (2022)](http://dx.doi.org/10.22331/q-2022-01-24-630)
+
+\[2\] [Ding, et. al, PRX Quantum (2024)](https://doi.org/10.1103/PRXQuantum.5.020332)
+
+\[3\] [Cleve and Lang, ICALP (2017)](https://doi.org/10.48550/arXiv.1612.09512)
+
+\[4\] [QuTiP 5: The Quantum Toolbox in Python](https://arxiv.org/abs/2412.04705)
+
+
+## About
+
+```python
+about()
+```