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.
Existing classical optical network infrastructure cannot be immediately used for quantum network applications due to photon loss. The first step toward enabling quantum networks is the integration of quantum repeaters into optical networks. However, the expenses and intrinsic noise inherent in quantum hardware underscore the need for an efficient deployment strategy that optimizes the placement […]
Existing classical optical network infrastructure cannot be immediately used for quantum network applications due to photon loss. The first step toward enabling quantum networks is the integration of quantum repeaters into optical networks. However, the expenses and intrinsic noise inherent in quantum hardware underscore the need for an efficient deployment strategy that optimizes the placement […]
Detectors capable of resolving the number of photons are essential in many applications, ranging from classic photonics to quantum optics and quantum communication. In particular, photon-number-resolving detectors based on arrays of superconducting nanostrips can offer a high detection efficiency, a low dark count rate, and a recovery time of a few nanoseconds. In this work, […]
Detectors capable of resolving the number of photons are essential in many applications, ranging from classic photonics to quantum optics and quantum communication. In particular, photon-number-resolving detectors based on arrays of superconducting nanostrips can offer a high detection efficiency, a low dark count rate, and a recovery time of a few nanoseconds. In this work, […]
Accurately predicting a quantum computer’s capability—which circuits it can run and how well it can run them—is a foundational goal of quantum characterization and benchmarking. As modern quantum computers become increasingly hard to simulate, we must develop accurate and scalable predictive capability models to help researchers and stakeholders decide which quantum computers to build and […]
As quantum computing devices increase in size with respect to the number of qubits, two-qubit interactions become more challenging, necessitating innovative and scalable qubit routing solutions. In this work, we introduce beSnake, a novel algorithm specifically designed to address the intricate qubit routing challenges in scalable spin-qubit architectures. Unlike traditional methods in superconducting architectures that […]
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, […]