Algorithms on Fire Opal
Understand how Fire Opal's error suppression can be applied to different algorithms
Fire Opal’s error suppression is algorithm agnostic—benchmarks show that it can be used to drastically improve the success of many different types of algorithms. Applying Fire Opal to different algorithms is simple, though there are a few tips and tricks to using the right functions in your algorithm design.
Deterministic quantum
Deterministic algorithms follow a fixed sequence of operations leading to a predefined output. They do not involve parameter tuning based on intermediate measurements. Some examples include Quantum Fourier Transform (QFT), Grover's Search Algorithm, Shor's Algorithm, Quantum Phase Estimation (QPE).
To apply Fire Opal’s error suppression to deterministic algorithms, you can use the execute function if you’re submitting less than 300 circuits. If you have a large batch of circuits, you can use the iterate function to submit a batch workload.
Variational quantum algorithms
Variational quantum algorithms use a combination of quantum and classical computing to optimize a solution iteratively. This includes algorithms such as Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA, and many classes of quantum machine learning algorithms, such as Variational Quantum Classifiers (VQCs) and Quantum Variational Autoencoder (QVAE).
When building these algorithms, you can use Fire Opal to run the quantum circuit in the quantum-classical procedure. Simply substitute the existing circuit execution statement in your algorithm with Fire Opal’s iterate function, which is optimized for submitting multiple consecutive jobs, as is typically required in variational workflows.
Quantum simulation algorithms
Quantum simulation algorithms are designed to simulate the behavior of quantum systems on quantum computers. These algorithms aim to model the dynamics of quantum systems, typically described by quantum mechanics, such as molecules, materials, or condensed matter systems. Some examples include Hamiltonian simulation methods, such as Trotter-Suzuki Decomposition, and VQE.
Quantum simulation algorithms may be either deterministic or variational, and you can use the same principles as previously mentioned to choose whether to use the execute or iterate function. Quantum simulation applications often require computing the expectation value of a Hamiltonian for different molecular configurations. When you receive the sampled probability results from Fire Opal, you can compute the expectation value of an observable.
