In the next step, we need to define an executor. In Qiskit, we need to specify explicitly when we “look” at our qubits. In fact, if we look ahead to solving the Hamiltonian simulation using Trotterization, this structure comes in handy because Trotterization builds upon such a repetition of a series of quantum gates.įinally, the important difference to the example is the measurement we include in our circuit. Similar to Mitiq’s example, we apply the series of gates multiple times to increase the length of the overall circuit. As mentioned, let's assume they represent the problem at hand. For the application of the CDR method, the details of this circuit do not really matter. These are mainly rotations ( rz, rx, and entanglements cx). Inside the for-loop, we apply some arbitrary quantum gates on our qubits. There’s nothing really special going on here. The following listing depicts the adaption of the quantum circuit to Qiskit. Clifford gates are easy to simulate on a classical computer - a precondition for the CDR method. This is a two-qubit circuit that only consists of Clifford gates and rotations around the Z-axis (□□). Yet, we stick with the example Mitiq provides. The quantum circuit we need to define represents the problem we aim to solve, such as the Hamiltonian simulation IBM asks us for. So, we need to adapt the code to the Qiskit API. However, when we look a little closer, we see that their example uses Google’s Cirq library. At first sight, they clearly describe what we need to do. Let’s first have a look at Mitiq’s introduction to CDR. So, it should be a piece of cake to get this working, no? Mitiq provides an API for the CDR method and they integrate well with Qiskit. We use Qiskit, the IBM quantum development library, and Mitiq, a Python toolkit for implementing error mitigation techniques on quantum computers. In this recent and promising error mitigation method, we create a machine learning model that we can use to predict and mitigate the noise by using the data from quantum circuits that we can simulate classically. Czarnik et al., Error mitigation with Clifford quantum-circuit data, Quantum 5, 592 (2021). In my previous post, I introduced the Clifford Data Regression (CDR) method developed by P. Yet, the main challenge is to cope with the inevitable noise because they want us to solve the problem on their 7-qubit Jakarta system.īut before we can solve this problem on the real quantum computer, let’s first have a look at how we can implement a quantum error mitigation method with Qiskit at all. They want us to use Trotterization to simulate a 3-particle Heisenberg model Hamiltonian. They’re looking for a solution to a quantum simulation problem. Recently, IBM announced its second Quantum Science Prize. That is what quantum error mitigation is about. The best we can do today is to reduce the impact the noise has on the computation. Second, by no means, we don’t have enough physical qubits to bundle them into fault-tolerant logical qubits. First, the qubits we have today suffer from noise in the environment ultimately destroying any meaningful computation. Quantum error mitigation is paramount to tap the potential of quantum computing today.
0 Comments
Leave a Reply. |