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Preparing the Gibbs state helps a researcher determine the probability of finding particles in each energy state, given a certain temperature.
This is important because, once someone can prepare Gibbs states on a quantum computer, it is far easier to simulate real-world physical conditions and how they may impact quantum computing.
They are designed for unitary operations, like Hadamard or Pauli-X, but Gibbs states require non-unitary operations, specifically interaction with a heat bath, which causes dissipation and impacts energy levels. They accumulate noise, which degrades performance. If researchers choose to combat the noise, they have to take qubits and assign them to noise reduction instead of other computational functions. Using qubits to combat noise is akin to taking two cores away from a traditional quad-core computer and forcing it to run like a dual-core machine.According to Ilin and Arad’s research, dissipative dynamics can help address these limitations.How to Incorporate Dissipative Dynamics into the Variational Framework Dissipative dynamics simulate the energy loss that would occur in a quantum system given a specific temperature. Ilin and Arad use an “R-gate,” which resets a qubit to a variational pure state that has a specific probability. Thus, their solution mimics the effects of a quantum system interacting with a specific environment. In other words, their approach eliminates the need for ancilla qubits. This frees up tremendous amounts of quantum computing power that would otherwise get used up while combating noise.Potential Advantages in Terms of Convergence and Robustness Dissipative variational quantum algorithms can mitigate barren plateaus. A barren plateau is a region in the optimization landscape where the gradient of the cost function becomes very small. As a result, barren plateaus make it very hard for optimizers to update parameters. This can completely stall the training process. Another way of looking at it is from the perspective of the optimizing functions. They need to see “hills and valleys” in the optimization landscape to determine the most efficient path toward the optimal solution. But barren plateaus make the landscape flat, leaving the functions with little to work with. On the other hand, D-VQAs roughen the landscape by increasing the variance of gradients. This makes the job of optimizers easier because they can see the hills and valleys and use this to find the best solution.Key Takeaways Ilin and Arad’s research may dramatically change the way researchers and engineers reduce quantum noise because it: Eliminates the need for ancilla qubits for noise reduction. Enables quantum computers to run more efficiently during the preparation of Gibbs states. Increases the efficiency of the convergence process. In short, engineers and researchers may be able to do more while using fewer resources, particularly when it comes to the Gibbs state preparation process. To learn more about how to harness D-VQAs, download the paper below.Download "Dissipative Variational Quantum Algorithms for Gibbs State Preparation" Article
Using qubits to combat noise is akin to taking two cores away from a traditional quad-core computer and forcing it to run like a dual-core machine.
According to Ilin and Arad’s research, dissipative dynamics can help address these limitations.
Dissipative dynamics simulate the energy loss that would occur in a quantum system given a specific temperature. Ilin and Arad use an “R-gate,” which resets a qubit to a variational pure state that has a specific probability. Thus, their solution mimics the effects of a quantum system interacting with a specific environment. In other words, their approach eliminates the need for ancilla qubits. This frees up tremendous amounts of quantum computing power that would otherwise get used up while combating noise.
Dissipative variational quantum algorithms can mitigate barren plateaus. A barren plateau is a region in the optimization landscape where the gradient of the cost function becomes very small. As a result, barren plateaus make it very hard for optimizers to update parameters. This can completely stall the training process. Another way of looking at it is from the perspective of the optimizing functions. They need to see “hills and valleys” in the optimization landscape to determine the most efficient path toward the optimal solution. But barren plateaus make the landscape flat, leaving the functions with little to work with. On the other hand, D-VQAs roughen the landscape by increasing the variance of gradients. This makes the job of optimizers easier because they can see the hills and valleys and use this to find the best solution.
Ilin and Arad’s research may dramatically change the way researchers and engineers reduce quantum noise because it:
In short, engineers and researchers may be able to do more while using fewer resources, particularly when it comes to the Gibbs state preparation process. To learn more about how to harness D-VQAs, download the paper below.