Quantum computation is a form of computation utilizing quantum mechanical phenomena.A formal formulation of quantum computer was proposed in 1985, leading to novel quantum algorithms whose computation time scales better, with respect to the problem size, than the classical counterparts.However, running these quantum algorithms requires fault-tolerant implementations, and hence, such engineering in quantum systems can be an overhead in utilizing quantum systems for information processing.This suggests, in addition to seeking faster quantum algorithms, the necessity of exploring how to harness quantum systems more naturally without extensive engineering, leading to another form of quantum information processing.Quantum machine learning (QML) is expected to be a promising approach for harnessing quantum systems for information processing.This is motivated by the fact that the success of neural networks has led to the possibility of solving ML tasks more efficiently with non-digital computer designs.Several QML algorithms that resemble neural networks utilize quantum systems in a manner distinct from digital quantum algorithms.However, training quantum systems is fundamentally challenging, suggesting that it still remains necessary to explore an alternative way of harnessing quantum systems for ML.Along with this direction, the quantum reservoir model harnesses quantum systems in a unique way: nontrivial outputs from an untrained quantum system are leveraged by a linear classical machine learning model to solve various tasks.Hence, the quantum part does not need to be programmable and can be analog quantum dynamics.Without extensive engineering in the quantum part, a quantum reservoir model was shown to be capable of performing nontrivial ML tasks using physical dynamics, which had already been realized experimentally.Therefore, the quantum reservoir model should be one of the promising directions for pursuing another form of quantum information processing.In this thesis, I will investigate the computational capability of natural quantum dynamics in the quantum reservoir model.I will tackle the following critical open questions in this context: (i) how well natural dynamics perform nontrivial ML tasks compared to engineered systems, and (ii) what kind of engineering in quantum dynamics is essential and appropriate for a given task.To tackle the first question (i), I will benchmark the learning performance of various natural quantum dynamics against random circuits that require complicated engineering, thereby revealing the computational power of natural dynamics.For the second question (ii), I will present a characterization method of quantum dynamics that has the potential to bridge quantum properties and their machine learning roles, providing concrete insights into essential and appropriate engineering to design quantum systems for ML.vQuantum computation is a form of computation utilizing quantum mechanical phenomena.A formal formulation of quantum computer was proposed in 1985, leading to novel quantum algorithms whose computation time scales better, with respect to the problem size, than the classical counterparts.However, running these quantum algorithms requires fault-tolerant implementations, and hence, such engineering in quantum systems can be an overhead in utilizing quantum systems for information processing.This suggests, in addition to seeking faster quantum algorithms, the necessity of exploring how to harness quantum systems more naturally without extensive engineering, leading to another form of quantum information processing.Quantum machine learning (QML) is expected to be a promising approach for harnessing quantum systems for information processing.This is motivated by the fact that the success of neural networks has led to the possibility of solving ML tasks more efficiently with non-digital computer designs.Several QML algorithms that resemble neural networks utilize quantum systems in a manner distinct from digital quantum algorithms.However, training quantum systems is fundamentally challenging, suggesting that it still remains necessary to explore an alternative way of harnessing quantum systems for ML.Along with this direction, the quantum reservoir model harnesses quantum systems in a unique way: nontrivial outputs from an untrained quantum system are leveraged by a linear classical machine learning model to solve various tasks.Hence, the quantum part does not need to be programmable and can be analog quantum dynamics.Without extensive engineering in the quantum part, a quantum reservoir model was shown to be capable of performing nontrivial ML tasks using physical dynamics, which had already been realized experimentally.Therefore, the quantum reservoir model should be one of the promising directions for pursuing another form of quantum information processing.In this thesis, I will investigate the computational capability of natural quantum dynamics in the quantum reservoir model.I will tackle the following critical open questions in this context: (i) how well natural dynamics perform nontrivial ML tasks compared to engineered systems, and (ii) what kind of engineering in quantum dynamics is essential and appropriate for a given task.To tackle the first question (i), I will benchmark the learning performance of various natural quantum dynamics against random circuits that require complicated engineering, thereby revealing the computational power of natural dynamics.For the second question (ii), I will present a characterization method of quantum dynamics that has the potential to bridge quantum properties and their machine learning roles, providing concrete insights into essential and appropriate engineering to design quantum systems for ML.v

Mar 24, 20260 citations

Ancilla mediated steady-state engineering in open quantum systems

Soham Pal, Karen Hovhannisyan, Omri Porat +3 more

Engineering the properties of a reservoir and its coupling to a quantum system is a powerful tool for simulating quantum thermodynamic processes and for generating otherwise inaccessible steady states. Yet tailoring both the reservoir and its coupling within a single platform remains challenging. Here we introduce a platform, in which an ancilla qubit mediates the coupling of a target system to a reservoir, providing independent control over the interaction form, coupling strength, and effective reservoir temperature. Our implementation uses the electron spin of a single nitrogen-vacancy center in diamond as the ancilla and a proximal $^{13}$ C nuclear spin as the target. By alternating engineered unitary interactions with dissipative ancilla resets, we realize dynamics naturally described by a collision model, enabling straight-forward tracking of the work, heat, coherence, and entropy generated at every collision. We experimentally demonstrate conventional thermalization and also realize anti-thermalization: the stabilization of the target system in a temperature opposite to that of its reservoir. Finally, harnessing this steady-state engineering, we utilize the nuclear spin as a quantum battery, achieving a steady-state ergotropy exceeding $70\%$ of the theoretical maximum.Engineering the properties of a reservoir and its coupling to a quantum system is a powerful tool for simulating quantum thermodynamic processes and for generating otherwise inaccessible steady states. Yet tailoring both the reservoir and its coupling within a single platform remains challenging. Here we introduce a platform, in which an ancilla qubit mediates the coupling of a target system to a reservoir, providing independent control over the interaction form, coupling strength, and effective reservoir temperature. Our implementation uses the electron spin of a single nitrogen-vacancy center in diamond as the ancilla and a proximal $^{13}$C nuclear spin as the target. By alternating engineered unitary interactions with dissipative ancilla resets, we realize dynamics naturally described by a collision model, enabling straight-forward tracking of the work, heat, coherence, and entropy generated at every collision. We experimentally demonstrate conventional thermalization and also realize anti-thermalization: the stabilization of the target system in a temperature opposite to that of its reservoir. Finally, harnessing this steady-state engineering, we utilize the nuclear spin as a quantum battery, achieving a steady-state ergotropy exceeding $70\%$ of the theoretical maximum.

quant-pharXiv:2609.04905Sep 7, 20260 citations

Quantum Computing and Nuclear Fusion

Sangaralingam Ramesh

Springer eBooksJan 1, 20254 citations

Assessment of Quantum Technologies for Potential Nuclear Applications

Shelby Karpa, Alexander Cornwell, Jeremy Renshaw +1 more

Transactions of the American Nuclear SocietyJan 1, 20220 citations

Co-Design of Quantum Hardware and Algorithms in Nuclear and High Energy Physics

Maja Franz, Pía Zurita, M. Diefenthaler +1 more

Quantum computing (QC) has emerged as a promising technology, and is believed to have the potential to advance nuclear and high energy physics (NHEP) by harnessing quantum mechanical phenomena to accelerate computations. In this paper, we give a brief overview of the current state of quantum computing by highlighting challenges it poses and opportunities it offers to the NHEP community. Noisy intermediate-scale quantum (NISQ) computers, while limited by imperfections and small scale, may hold promise for near-term quantum advantages when coupled with co-designed quantum algorithms and special-purpose quantum processing units (QPUs). We explore various applications in NHEP, including quantum simulation, event classification, and realtime experiment control, emphasising the potential of variational quantum circuits and related techniques. To identify current interests of the community, we perform an analysis of recent literature in NHEP related to QC.Quantum computing (QC) has emerged as a promising technology, and is believed to have the potential to advance nuclear and high energy physics (NHEP) by harnessing quantum mechanical phenomena to accelerate computations. In this paper, we give a brief overview of the current state of quantum computing by highlighting challenges it poses and opportunities it offers to the NHEP community. Noisy intermediate-scale quantum (NISQ) computers, while limited by imperfections and small scale, may hold promise for near-term quantum advantages when coupled with co-designed quantum algorithms and special-purpose quantum processing units (QPUs). We explore various applications in NHEP, including quantum simulation, event classification, and realtime experiment control, emphasising the potential of variational quantum circuits and related techniques. To identify current interests of the community, we perform an analysis of recent literature in NHEP related to QC.

EPJ Web of ConferencesJan 1, 20241 citation

Quantum Computing for Nuclear Dynamics

Alessandro Baroni

Feb 21, 20240 citations

^{13}

C nuclear spin as the target. By alternating engineered unitary interactions with dissipative ancilla resets, we realize dynamics naturally described by a collision model, enabling straight-forward tracking of the work, heat, coherence, and entropy generated at every collision. We experimentally demonstrate conventional thermalization and also realize anti-thermalization: the stabilization of the target system in a temperature opposite to that of its reservoir. Finally, harnessing this steady-state engineering, we utilize the nuclear spin as a quantum battery, achieving a steady-state ergotropy exceeding

70\%

of the theoretical maximum.