Quantum optimization is the application of quantum computing techniques and algorithms to tackle optimization problems. Such problems involve finding the best solution or configuration from a set of possibilities, often with constraints, in order to minimize or maximize a specific objective or cost function.
While quantum supremacy remains a distant goal, quantum optimization has been a key point of focus for researchers.
The first real quantum computer efficiently optimized problems on May 5th, 2022. The project, “Quantum Optimization of Maximum Independent Set using Rydberg Atom Arrays,” was co-led by Harvard’s Mikhail Lukin, MIT’s Markus Greiner, and Vladan Vuletic. Harvard’s 289-qubit quantum processor, operating analogically with depths up to 32, shattered precedents.
It was too vast and intricate for classical simulations to pre-optimize control parameters. Instead, a quantum-classical hybrid algorithm, with direct, automated quantum processor feedback, was used. This fusion of size, depth, and quantum control led to a quantum leap, outperforming classical heuristics.
These scientists proved neutral-atom quantum processors excel in encoding hard optimization dilemmas. They solved practical problems, like maximum independent set on graphs and quadratic unconstrained binary optimization (QUBO).
In a recent note, Atom Computing has announced a Record-Breaking 1,225-Qubit Quantum Computer, dwarfing now only Harvard’s earlier 289-qubit model, but also doubling the previous record holder’s tally of 433 qubits set by IBM’s Osprey machine.
A qubit is the simple building block of quantum data, similar to a computer’s regular bit. But, unlike plain bits, qubits can be in multiple states all at once. The greater number of qubits allows for the exploration of larger solution spaces, enabling the resolution of optimization challenges that were previously deemed intractable.
As quantum processors grow in size, the likelihood of errors in quantum operations also increases. The larger number of qubits also means more quantum gates and interactions, which necessitates efficient gate optimization techniques.
Large-scale optimization problems often need to be decomposed into smaller subproblems for quantum processing. Optimizing the decomposition process and ensuring that it does not introduce additional complexities or errors is vital.