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1. A quantum algorithm for evolving open quantum dynamics on quantum computing devices

   https://doi.org/10.1038/s41598-020-60321-x  

   Hu, Zixuan; Xia, Rongxin; Kais, Sabre (February 2020, Scientific Reports)

   Abstract Designing quantum algorithms for simulating quantum systems has seen enormous progress, yet few studies have been done to develop quantum algorithms for open quantum dynamics despite its importance in modeling the system-environment interaction found in most realistic physical models. In this work we propose and demonstrate a general quantum algorithm to evolve open quantum dynamics on quantum computing devices. The Kraus operators governing the time evolution can be converted into unitary matrices with minimal dilation guaranteed by the Sz.-Nagy theorem. This allows the evolution of the initial state through unitary quantum gates, while using significantly less resource than required by the conventional Stinespring dilation. We demonstrate the algorithm on an amplitude damping channel using the IBM Qiskit quantum simulator and the IBM Q 5 Tenerife quantum device. The proposed algorithm does not require particular models of dynamics or decomposition of the quantum channel, and thus can be easily generalized to other open quantum dynamical models. 

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2. Quantum Control Machine: The Limits of Control Flow in Quantum Programming

   https://doi.org/10.1145/3649811  

   Yuan, Charles; Villanyi, Agnes; Carbin, Michael (April 2024, Proceedings of the ACM on Programming Languages)

   Quantum algorithms for tasks such as factorization, search, and simulation rely on control flow such as branching and iteration that depends on the value of data in superposition. High-level programming abstractions for control flow, such as switches, loops, higher-order functions, and continuations, are ubiquitous in classical languages. By contrast, many quantum languages do not provide high-level abstractions for control flow in superposition, and instead require the use of hardware-level logic gates to implement such control flow. The reason for this gap is that whereas a classical computer supports control flow abstractions using a program counter that can depend on data, the typical architecture of a quantum computer does not analogously provide a program counter that can depend on data in superposition. As a result, the complete set of control flow abstractions that can be correctly realized on a quantum computer has not yet been established. In this work, we provide a complete characterization of the properties of control flow abstractions that are correctly realizable on a quantum computer. First, we prove that even on a quantum computer whose program counter exists in superposition, one cannot correctly realize control flow in quantum algorithms by lifting the classical conditional jump instruction to work in superposition. This theorem denies the ability to directly lift general abstractions for control flow such as the λ-calculus from classical to quantum programming. In response, we present the necessary and sufficient conditions for control flow to be correctly realizable on a quantum computer. We introduce the quantum control machine, an instruction set architecture featuring a conditional jump that is restricted to satisfy these conditions. We show how this design enables a developer to correctly express control flow in quantum algorithms using a program counter in place of logic gates. 

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3. Expressive Quantum Perceptrons for Quantum Neuromorphic Computing

   https://doi.org/10.1088/2058-9565/ad9fa4  

   Araiza_Bravo, Oscar_Rodrigo; Patti, Taylor_L; Najafi, Khadijeh; Gao, Xun; Yelin, Susanne_F (December 2024, Quantum Science and Technology)

   Abstract Quantum neuromorphic computing (QNC) is a sub-field of quantum machine learning (QML) that capitalizes on inherent system dynamics. As a result, QNC can run on contemporary, noisy quantum hardware and is poised to realize challenging algorithms in the near term. One key issue in QNC is the characterization of the requisite dynamics for ensuring expressive quantum neuromorphic computation. We address this issue by proposing a building block for QNC architectures, what we call quantum perceptrons (QPs). Our proposed QPs compute based on the analog dynamics of interacting qubits with tunable coupling constants. We show that QPs are, with restricted resources, a quantum equivalent to the classical perceptron, a simple mathematical model for a neuron that is the building block of various machine learning architectures. \framing{Moreover, we show that QPs are theoretically capable of producing any unitary operation.} Thus, QPs are computationally more expressive than their classical counterparts. As a result, QNC architectures built our of QPs are, theoretically, universal. We introduce a technique for mitigating barren plateaus in QPs called entanglement thinning. We demonstrate QPs' effectiveness by applying them to numerous QML problems, including calculating the inner products between quantum states, entanglement witnessing, and quantum metrology. Finally, we discuss potential implementations of QPs and how they can be used to build more complex QNC architectures. 

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4. Demonstration of Quantum Channel Monitoring via Quantum Wrappers

   https://doi.org/10.1364/OFC.2023.Tu3H.4  

   On, Mehmet Berkay; Singh, Sandeep Kumar; Gul, Gamze; Kanter, Gregory S.; Proietti, Roberto; Kumar, Prem; Ben Yoo, S. J. (January 2023, Optica Publishing Group)

   We experimentally demonstrate quantum channel monitoring by wavelength-time multiplexing of classical wrapper bits with quantum payloads. Bit-error-rate measurements of 5 Gb/s classical bits infer the coincidence-to-accidental ratio of the quantum channel up to 13.3 dB. 

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5. Topological quantum photonics

   https://doi.org/10.1063/5.0239265  

   Hashemi, Amin; Zakeri, M_Javad; Jung, Pawel_S; Blanco-Redondo, Andrea (January 2025, APL Photonics)

   Topological quantum photonics explores the interaction of the topology of the dispersion relation of photonic materials with the quantum properties of light. The main focus of this field is to create robust photonic quantum information systems by leveraging topological protection to produce and manipulate quantum states of light that are resilient to fabrication imperfections and other defects. In this perspective, we provide a theoretical background on topological protection of photonic quantum information and highlight the key state-of-the-art experimental demonstrations in the field, categorizing them based on the quantum features they address. An analysis of the key challenges and limitations concerning topological protection of quantum states is presented. Importantly, this paper takes a thorough perspective look into what future research in this area may bring. 

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