Relaxation is a common way for dealing with combinatorial optimization problems. Quantum random-access optimization (QRAO) is a quantum-relaxation-based optimizer that uses fewer qubits than the number of bits in the original problem by encoding multiple variables per qubit using quantum random-access code (QRAC). Reducing the number of qubits will alleviate physical noise (typically, decoherence), and […]
Relaxation is a common way for dealing with combinatorial optimization problems.
This article delves into the role of the quantum Fisher information matrix (FIM) in enhancing the performance of parameterized quantum circuit (PQC)-based reinforcement learning agents. While previous studies have highlighted the effectiveness of PQC-based policies preconditioned with the quantum FIM in contextual bandits, its impact in broader reinforcement learning contexts, such as Markov decision processes, […]
Combinatorial problems are formulated to find optimal designs within a fixed set of constraints and are commonly found across diverse engineering and scientific domains. Understanding how to best use quantum computers for combinatorial optimization remains an ongoing area of study. Here, we propose new methods for producing approximate solutions to quadratic unconstrained binary optimization problems, […]
This article delves into the role of the quantum Fisher information matrix (FIM) in enhancing the performance of parameterized quantum circuit (PQC)-based reinforcement learning agents. While previous studies have highlighted the effectiveness of PQC-based policies preconditioned with the quantum FIM in contextual bandits, its impact in broader reinforcement learning contexts, such as Markov decision processes, […]
Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to […]
Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to […]
Since people became aware of the power of quantum phenomena in the domain of traditional computation, a great number of complex problems that were once considered intractable in the classical world have been tackled. The downsides of quantum supremacy are its high cost and unpredictability. Numerous researchers are relying on quantum simulators running on classical […]
Since people became aware of the power of quantum phenomena in the domain of traditional computation, a great number of complex problems that were once considered intractable in the classical world have been tackled. The downsides of quantum supremacy are its high cost and unpredictability. Numerous researchers are relying on quantum simulators running on classical […]
Quantum computers present a compelling platform for the study of open quantum systems, namely, the nonunitary dynamics of a system. Here, we investigate and report digital simulations of Markovian nonunitary dynamics that converge to a unique steady state. The steady state is programmed as a desired target state, yielding semblance to a quantum state preparation […]